rotten
>
Library
>
Biographies
>
Crime
> Terrorists
Jamal al-Fadl
Abu Faraj al-Liby
Ali Mohammed
Dr. Rashid
Abu Musab al-Zarqawi
Mohammed Jamal Khalifa
Eric Rudolph
John Walker Lindh
Zacarias Moussaoui
Hambali
Ayman Al-Zawahiri
Khalid Shaikh Mohammed
Jose Padilla
Ramzi Yousef
Terry Nichols
Mohammed Atta
Abu Nidal
Timothy McVeigh
Carlos the Jackal
Osama bin Laden
Pornopolis
|
Rotten
|
Faces of Death
|
Famous Nudes


See also Visualization of COVID-19 data: tablepedia.com/en/covid-19.html Github: https://github.com/yaroslav1982/tablepedia.com
Visualization of user data (single HTML page with JS in 8 KB)
Xmin=
Xmax=
Xstep=
Ymin=
Ymax=
Ystep=
USER DATA:
SVG
See also Visualization of COVID-19 data: tablepedia.com/en/covid-19.html Github: https://github.com/yaroslav1982/tablepedia.com


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Since our incorporation, we've grown from a regional organization to a multi-faceted, national presence in the industry.
Our roots are based in construction, real estate, investment and development management. Our portfolio includes work across industries. Project's uniqueness comes from understanding what it is to be the owner, the developer and the builder – a trifecta that is evident in Project's core portfolio of prestigious commercial, corporate and residential properties.
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Report: Legalizing casinos would provide modest boost
RICHMOND, Va. (AP) - A new state report says legalizing casinos in Virginia would provide a modest economic boost in state tax revenues and to communities where casinos are located.
The Virginia General Assembly’s research agency issued a report Monday evaluating the economic impact of allowing new casinos around the state and legalizing types of online gambling.
Whether to legalize casinos and other gambling issues are set to be heavily debated in the 2020 legislative session.
The Joint Legislative Audit and Review Commission found that legalizing online betting and allowing casino resorts in Bristol, Portsmouth, Norfolk, Danville and Richmond, would provide an overall yearly increase in state tax revenues by about $370 million.
The report also said the state’s horse racing industry would be hurt by the new casinos.
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[gecode-users] How to compile faster gecode programs
Guido Tack
tack at ps.uni-sb.de
Thu Jun 12 12:38:37 CEST 2008
Mauricio Toro wrote:
> I've been developing an application with Gecode.
> I am compiling this way:
>
> g++ -I. -DNDEBUG -fvisibility=hidden -fPIC -Wextra -Wall -pipe -ggdb
> -O3 -fno-strict-aliasing -finline-limit=3000 -ffast-math \
> -o examples/nttcfo examples/nttcfo.cc -lgecodesearch -
> lgecodecpltset -lgecodeset -lgecodeint -lgecodeminimodel -
> lgecodeserialization -lgecodekernel -lgecodesupport -L /System/
> Library/Frameworks/MidiShare.framework/MidiShare
>
> It has been great for the developement, but now, I want the
> compilation
> and the application to run faster and I don't want to add support
> for assertions, ggdb, debug symbols, etc.
You already used full optimization (-O3), and no assertions are
compiled in (-DNDEBUG). The only thing left are the symbols (-ggdb),
which shouldn't do much harm.
> How should I compile?
Exactly the way you did.
> Additionally, I would like to know if it matters when say
> -lgecodecpltset -lgecodeserialization, etc
> when I am not using those APIs.
If you don't need them, you don't have to link them, but that won't
change your executable's efficiency. A slightly faster system can be
built by disabling set variables when you compile Gecode, i.e., using
the --disable-set-vars configure switch. But I doubt the performance
will be really noticeably faster for you.
Cheers,
Guido
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Overview
- Where is Javascript used ?
- Server side
- New languages based on Javascript
Where is Javascript used ?
- part of web browsers
- client side scripts
- interact with the user
- alter the document that is displyed
- works "almost" cross-browser and platform
Server side
- Node.js is a packaged compilation of Google's V8 JavaScript engine
- ... write same language on client-side and server-side.
- ... high throughput via non-blocking I/O
Based on Javascript
- CoffeScript ES5 "strict mode" subset (AKA the "good parts") of JavaScript. That means no with statement, no loose equality checking, etc.
- TypeScript offers classes, modules, and interfaces to help you build robust components.
- Asm.js an extraordinarily optimizable, low-level subset of JavaScript.
Languages which compiles to Javascript
- Dart is a class-based, single inheritance, object-oriented language with C-style syntax.
- ClojureScript compiler for Clojure that targets JavaScript.
Conclusion
- Javascript as a language has improved a lot in recent years
- Hopefully becomes better with ECMAScript 6 implementation


Illuminate.css makes browsers render all elements more consistently and in line with modern standards. It precisely targets only the styles that need normalizing.
resets the font sizesso that using semantic markup doesn't affect the styling
resets the block marginsso that the spacing is only applied when you need it
resets tablesso that tabular data only takes the space it needs
preserves the inline paddingsso that buttons and inputs keep their default layout
sets the border-box box sizingso that borders and paddings don't affect the set dimensions
sets responsive media elementsso that images and embeds scale with the browser width


---
layout: post
title: character sketch watercolour
date: '2018-02-04T11:30:20+01:00'
categories:
- Sketchbooks
- Character
picture_url: https://film-Storyboards.tumblr.com/post/170500309072/character-sketch-watercolour
---
Character sketch — From Sketchbooks


--- layout: page title: About header-img: "img/about-bg.png" ---
Catherine Cordova is a graduate of Rutgers University - New Brunswick. A Web Developer by day, champion sushi, pizza, and dessert eater by night. New York native, currently crashing at a little town called Nashville.


- Reset the layout to a plain pattern. Preset #1
- Click the tile to change the pattern. Shift-click the tile to rotate clockwise.
- Copy the link from the URL bar, or from this link to keep the layout.
- Toggle outlines
- Ripples: , Straight: , Curved: , Lotus:
- Randomize with the recommended breakup of 166 ripples and 23 of each of the others


APR ARM Loan Calculator
APR Loan Calculator
ARM Loan Payment
Cost of Living Index?
Debt Consolidation Calculator
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Tax Benefits of Buying


Concatenate (multiply) a coordinate frame (on the right) with this coordinate frame.
Transform a direction in THIS coordinate frame to the identity (world space) coordinate frame.
Transform a direction in THIS coordinate frame to the identity (world space) coordinate frame.
Equivalent to post-multiplying a column vector by the basis matrix.
Transform a point in THIS coordinate frame to the identity (world space) coordinate frame.
Transform a point in THIS coordinate frame to the identity (world space) coordinate frame.
Equivalent to post-multiplying a column vector by the basis matrix and offset by origin.
Add the component values (as Doubles, basis vectors followed by origin) of the corresponding 4x4 matrix starting at the current position to given native array.
Add the component values (as Floats, basis vectors followed by origin) of the corresponding 4x4 matrix starting at the current position to given native array.
A method that should be called from every well-designed equals method that is open to be overridden in a subclass.
Concatenate (multiply) a coordinate frame (on the right) with this coordinate frame.
The component-wise comparison of whether the given coordinate frame is within a type specific epsilon of this coordinate frame.
The component-wise comparison of whether the given coordinate frame in within a given epsilon of this coordinate frame.
Tests whether the given predicate holds true for all of the corresponding components of this and the given coordinate frame.
Tests whether the given predicate holds true for all components of this coordinate frame.
Tests whether the given predicate holds true for any of the corresponding components of this and the given coordinate frame.
Tests whether the given predicate holds true for any components of this coordinate frame.
Applies a function to all components of this coordinate frame.
Applies a function to all components of this coordinate frame.
The function that is applied for its side-effect to every component.
Find the inverse (if possible) of this coordinate frame.
Find the inverse (if possible) of this coordinate frame.
The inverse being that coordinate frame that transforms points from the world (identity) coordinate frame to this one.
Whether any components are Not-a-Number (NaN).
Whether any components are Not-a-Number (NaN).
Builds a new coordinate frame by applying a function to each component of this coordinate frame.
Add the component values (as Doubles, basis vectors followed by origin) of the corresponding 4x4 matrix starting at the current position to given native array.
Add the component values (as Floats, basis vectors followed by origin) of the corresponding 4x4 matrix starting at the current position to given native array.
Create a coordinate frame in which two virtual matrix rows have been exchanged.
Create a coordinate frame in which two virtual matrix rows have been exchanged.
The index of the row to swap.
The index of the row to swap.
Create a coordinate frame in which a given virtual matrix row is scaled by a constant factor.
Create a coordinate frame in which a given virtual matrix row is scaled by a constant factor.
The scaling factor.
Create a coordinate frame in which a multiple of one virtual matrix row is summed with another row.
Create a coordinate frame in which a multiple of one virtual matrix row is summed with another row.
The index of the row to change.
The index of the row to scale and sum.
The scaling factor.
Convert to a nested array (basis vectors followed by origin) of the corresponding 4x4 matrix.
Convert to a nested list (basis vectors followed by origin) of the corresponding 4x4 matrix.
Transform a direction in THIS coordinate frame to the identity (world space) coordinate frame.
Transform a direction in THIS coordinate frame to the identity (world space) coordinate frame.
Equivalent to post-multiplying a column vector by the basis matrix.
Transform a point in THIS coordinate frame to the identity (world space) coordinate frame.
Transform a point in THIS coordinate frame to the identity (world space) coordinate frame.
Equivalent to post-multiplying a column vector by the basis matrix and offset by origin.
An immutable coordinate frame defined by three basis vectors and an origin.


| Tag | Name | Req'd | Description |
|---|---|---|---|
| 93 | SignatureLength | N | Number of bytes in signature field |
| 89 | Signature | N | Electronic signature |
| 10 | CheckSum | Y | Three byte, simple checksum (see Volume 2: "Checksum Calculation" for description). ALWAYS LAST FIELD IN MESSAGE; i.e. serves, with the trailing <SOH>, as the end-of-message delimiter. Always defined as three characters. (Always unencrypted) |


NSView
class NSView : NSResponder, NSAnimatablePropertyContainer, NSUserInterfaceItemIdentification, NSDraggingDestination, NSAppearanceCustomization, NSAccessibilityElementProtocol, NSAccessibilityProtocol
-
Sets the receiver’s tooltip to the style-stripped Markdown string.
Throws
ADownErrors
error.Declaration
Swift
func enlightenTooltip(markdownString: String) throws
Parameters
markdownString
The CommonMark Markdown string to use for the tooltip.
-
Sets the receiver’s tooltip to the style-stripped Markdown content of a Markdown file.
Throws
An error if the contents of the filemarkdownName
cannot be read, and aDownErrors
error if the CommonMark Markdown string fails to load.Declaration
Swift
func enlightenTooltip(markdownFilename: String, in bundle: Bundle) throws
Parameters
markdownFilename
The name of the CommonMark Markdown file in the provided bundle to use for the tooltip.
bundle
The bundle that contains the Markdown file named
markdownFilename
.


CASCADE 1.0 Analysis - 1 ss models
Stable States Data
In this section, only the boolean models that have 1 stable state will be used in the analysis. All variables of interest (stable state, link-operator parameterization, fitness to steady state, performance MCC score, etc.) will relate only to the 1 stable state models from now on.
To load the stable state data for the models that have 1 stable state use the Zenodo dataset and the script get_ss_data.R
Parameterization Maps
In this section we present the results of using UMAP (McInnes, Healy, and Melville 2018) on the link-operator parameterization data of the CASCADE 1.0 models with 1 stable state. We created several such parameterization maps by adjusting the n_neighbors parameter input (from \(2\) to \(20\)), which is responsible for the size of the local neighborhood (in terms of number of neighboring sample points) used for the manifold approximation. As the documentation says, larger values result in more global views of the manifold, while smaller values result in more local data being preserved. To get these map images and the reduced dimension dataset, use the script 1ss_models_umap.R for more details.
Note that in all these mappings to the 2D space, models that share similar link-operator parameterization will reside in the same area/cluster in the map. The larger the n_neighbors is, the more the smaller clusters merge into larger ones. The images for \(\ge 14\) n_neighbors are almost exactly the same.
We present some of these maps below:
knitr::include_graphics(path = "img/1ss_umap/1ss_umap_unsup_2.png")
knitr::include_graphics(path = "img/1ss_umap/1ss_umap_unsup_3.png")
knitr::include_graphics(path = "img/1ss_umap/1ss_umap_unsup_5.png")
knitr::include_graphics(path = "img/1ss_umap/1ss_umap_unsup_6.png")
knitr::include_graphics(path = "img/1ss_umap/1ss_umap_unsup_8.png")
knitr::include_graphics(path = "img/1ss_umap/1ss_umap_unsup_11.png")
knitr::include_graphics(path = "img/1ss_umap/1ss_umap_unsup_12.png")
knitr::include_graphics(path = "img/1ss_umap/1ss_umap_unsup_15.png")
We observe the existence of two large families (superclusters) of parameterization models, especially for more global views of the dataset (\(\text{n_neighbors}\ge 8\)).
Distinct smaller sub-clusters of closely parameterized models also manifest for different number of neighbors, which suggests that there is some order in the parameterization of the 1 stable state models (as exemplified by the UMAP method) across multiple visualization scales.
Gitsbe Models on the Map
Gitsbe uses a genetic algorithm approach to produce boolean models that are fitted to activity-based, biomarker training data.
We used Gitsbe and tested the produced models performance (ensemble-wise drug combination predictions) against synergy data from (Flobak et al. 2015) in another report (AGS paper Sup. Material). The calibrated models performed very well in terms of both ROC and PR-AUC.
Here we want to check whether models produced by a method such as a genetic algorithm-based one have similar parameterization - i.e. they belong in the same neighborhood in the parameterization map.
We will use models from \(1000\) gitsbe simulations, calibrated to steady state (a total of \(3000\) models, choosing the \(3\) best-fit models from each simulation). The results are provided in this data file and to reproduce them, follow the instructions here, keeping the default configuration options for CASCADE 1.0 and changing only the number of simulations to \(1000\)).
All the Gitsbe models had a large fitness to the steady state AGS data (their stable states fitting almost exactly the states of the manually-curated 24 nodes), as it can be seen from the next figure (see gitsbe_models_fit.R):
To generate the next figures (same map, same gitsbe models, different number of neighbors) use the gitsbe_model_embedding.R - all figures are available here:
knitr::include_graphics(path = "img/gitsbe_umaps/2nn.png")
knitr::include_graphics(path = "img/gitsbe_umaps/4nn.png")
knitr::include_graphics(path = "img/gitsbe_umaps/8nn.png")
knitr::include_graphics(path = "img/gitsbe_umaps/14nn.png")
Gitsbe-generated models that fit the biomarker steady state data for the AGS cell line have a diverse structure that spans across the parameterization map but nonetheless appear to gather in smaller parameterization-specific sub-clusters (better seen in the Figure with 14 neighbors which gives a more global view of the dataset).
Observing the distribution of the gitsbe models in the parameterization map, we see that most of them are being placed at one of the two superclusters.
Of course, there are areas in the map that Gitsbe models do not cover, which may as well be high-performance model areas. Since we have generated all possible link-operator models with CASCADE 1.0, we can proceed to generate a performance map (atop the parameterization one) and cross-check if the gitsbe models fall into high-performance areas or not.
Performance Maps
Every model generated via abmlog
(Model Stable State Statistics) was tested against the synergy dataset of (Flobak et al. 2015).
Among \(21\) drug combinations, \(4\) were found synergistic in that dataset, namely:
obs_syn = emba::get_observed_synergies(file = "data/observed_synergies_cascade_1.0")
usefun::pretty_print_vector_values(obs_syn, vector.values.str = "synergies")
4 synergies: PI-PD, PI-5Z, PD-AK, AK-5Z
Using the drabme software module, we tested every CASCADE 1.0 model against this dataset and got each model’s predictions for each drug combination.
The HSA rule was used to define if a model is synergistic for a particular combination.
The results are available in the Zenodo dataset , file cascade_1.0_hsa_fixpoints.tar.gz
.
As previously said, we are going to use the 1 stable state model predictions only.
We used the emba R package (Zobolas, Kuiper, and Flobak 2020) to perform a biomarker analysis on the 1 stable state models dataset and their predictions from drabme
(see script emba_analysis.R).
Part of the results from the emba analysis is the calculation of the Matthews correlation coefficient (MCC) performance score for each model.
We use these MCC model scores to draw the next figures (see mcc_figures.R script)
Splitting all the 1 stable state models to \(4\) MCC classes we can see that a lot of models have performance close to random prediction or even worse:
knitr::include_graphics(path = "img/mcc_hist.png")
knitr::include_graphics(path = "img/mcc_hist2.png")
Most of the 1 stable state models have MCC performance close to random or worse, making it thus difficult for any algorithm to find the best performance models (i.e. the 3-4th MCC class models, which amounts to only \(5\%\) of the total number of models with 1 stable state, given the standardized boolean equation parameterization inspired by (Mendoza and Xenarios 2006)).
Unsupervised UMAP
If we draw the parameterization maps for different number of neighbors and color the points/models according to their MCC score, we get these images (to see all figures, check here):
knitr::include_graphics(path = "img/mcc_maps/2nn.png")
knitr::include_graphics(path = "img/mcc_maps/14nn.png")
We observe that the two large parameterization superclusters (especially outlined by the figures with \(2\) and \(\ge 8\) neighbors) are closely related with the MCC performance metric. Specifically, these 2 superclusters dichotomize the models performance landscape into 2 areas, where only one of them has the majority of good performance models (i.e. those that have an MCC score \(>0\)).
Also, we observe that closely parameterized models tend to have same performance (existence of smaller parameterization clusters of same performance models).
Comparing the corresponding Gitsbe parameterization maps with the MCC maps, we can clearly verify now that the Gitsbe models might not always be the best performing ones (in terms of MCC score) since they appear in both superclusters (and that’s only a first-fine measure for determining performance based on structure) - but at least they tend to be more in the higher performance supercluster.
Reasons why the gitsbe models are not always on the high-performance supercluster can be justified by:
- The nature of the genetic algorithm, which is a heuristic method and as such produces local optima
- The training dataset does not provide enough constraints for the optimization problem, i.e. more steady state nodes are needed to be fitted in the models stable state which will result in models with more strict parameterization patterns
- A combination of the two above
Supervised UMAP
We perform supervised dimension reduction using UMAP on the 1 stable state model dataset. The only difference with the previous performance maps is that we provide UMAP (as an input) the information of the models performance (the MCC score) and not just overlay it (via coloring) afterwards.
The main thing we want to answer here is if UMAP can do a better job placing the models on the map, given additional information about their performance.
We tried UMAP with different number of neighbors (from local to a more global view of the dataset), target weight (weighting factor between data topology (\(0\)) and target/response topology (\(1\)) or somewhere in between) and setting the response either to the MCC score as a continuous, numeric value or to the MCC classes (discrete variable) as shown above. See script mcc_sumap.R for more details.
First, we show some maps, where the response MCC was treated as a continuous variable:
knitr::include_graphics(path = "img/mcc_sumaps/4nn_0w.png")
knitr::include_graphics(path = "img/mcc_sumaps/4nn_0.5w.png")
knitr::include_graphics(path = "img/mcc_sumaps/4nn_1w.png")
knitr::include_graphics(path = "img/mcc_sumaps/14nn_0.5w.png")
knitr::include_graphics(path = "img/mcc_sumaps/14nn_1w.png")
knitr::include_graphics(path = "img/mcc_sumaps/20nn_0.5w.png")
knitr::include_graphics(path = "img/mcc_sumaps/20nn_1w.png")
Very artistic and knotty figures! UMAP achieves some sort of distinction between the different MCC values, especially when the topology is heavily based on the response MCC value (target weight is \(1\))
Next, we show some maps, where the response MCC was treated as a categorical variable, denoting the MCC class the models belong to (higher the better):
knitr::include_graphics(path = "img/mcc_sumaps/class/4nn_0.5w_class.png")
knitr::include_graphics(path = "img/mcc_sumaps/class/6nn_0.5w_class.png")
knitr::include_graphics(path = "img/mcc_sumaps/class/8nn_0.5w_class.png")
knitr::include_graphics(path = "img/mcc_sumaps/class/10nn_0.5w_class.png")
knitr::include_graphics(path = "img/mcc_sumaps/class/14nn_0w_class.png")
knitr::include_graphics(path = "img/mcc_sumaps/class/14nn_0.5w_class.png")
knitr::include_graphics(path = "img/mcc_sumaps/class/14nn_1w_class.png")
We observe that the higher the number of neighbors is (\(\ge 10\) with a balanced target weight of \(0.5\)), the better UMAP classifies the models to distinct (super-) clusters representing the different MCC classes.
Performance vs Fitness
We try to see if there is any correlation between performance (MCC) and fitness to the AGS steady state.
We study two AGS steady states: one with a total of \(24\) nodes which is the result of manual literature curation (S4 Table in Flobak et al. (2015)) and serves as the gold-standard, and one with all \(77\) nodes, which is the single (and only) fixpoint of the AGS boolean model in Flobak et al. (2015).
Note that second, larger steady state reports the same activity values for the common \(24\) nodes as the first one.
Use the fit_figures.R script to reproduce the figures for the literature-curated steady state and the fit_vs_performance_ags_pub.R for the AGS model fixpoint/steady state.
First, we check if the fitness density of all 1 stable state models covers the whole fitness spectrum:
knitr::include_graphics(path = "img/1ss_models_fit_density.png")
knitr::include_graphics(path = "img/1ss_models_fit_density_pub.png")
The Pearson correlation between the two fitness vectors is high:
fit_data = readRDS(file = "data/fit_data.rds")
fit_data_pub = readRDS(file = "data/fit_data_pub.rds")
# data check
stopifnot(all(names(fit_data) == names(fit_data_pub)))
# Pearson correlation
cor(x = fit_data, y = fit_data_pub, method = "pearson") %>% round(digits = 2)
[1] 0.94
We observe that most of the models have at least half of the nodes in the same state as in the AGS steady state, no matter which steady state we fit the stable state data against. This makes the fitness score distribution skewed towards the higher fitness values.
Of course, though the two steady states completely agree on the values of the \(24\) nodes, it might be that the rest of the nodes have slightly different activity state values from the ones found in the fixpoint attractor of the AGS model in Flobak et al. (2015). If that is the case in reality, the fitness distribution might be more uniform.
We follow the same classification scheme as above, i.e. splitting the 1 stable models to \(4\) MCC classes and comparing the fitness scores between these classes:
knitr::include_graphics(path = "img/mcc_vs_fit.png")
knitr::include_graphics(path = "img/mcc_vs_fit_pub.png")
The last MCC class has a statistically significant higher median fitness to the AGS steady state compared to all other lower MCC classes, even though the correlation between fitness and performance is not linear and most of the models have a fitness higher than \(0.5\). Note also that models that have significantly lower than \(0.5\) fitness in each steady state case, belong to the first two, lower performance classes.
The last two figures points us to the fact that more constraints are needed for the fitness calculation of the Gitsbe genetic algorithm or any other for that matter (i.e. more literature-curated nodes in the AGS steady state - now only \(24/77=31\%\) is included) in order to define more restrictive parameterized models that would allow a much more uniform fitness density spectrum (or at least not skewed towards the higher fitness values). Such fitness spectrum would (hopefully) allow for more granularity in the corresponding performance behavior between the different MCC classes, and thus more distinctive correlation.
Fitness Maps
If we draw the parameterization maps for different number of neighbors and color the points/models according to their fitness to the AGS steady state (24 nodes), we get these images (see fit_figures.R script):
knitr::include_graphics(path = "img/fit_maps/2nn.png")
knitr::include_graphics(path = "img/fit_maps/4nn.png")
knitr::include_graphics(path = "img/fit_maps/11nn.png")
knitr::include_graphics(path = "img/fit_maps/14nn.png")
Higher fitness models manifest in both superclusters, suggesting again the need for more literature-curated nodes in the training data (AGS steady state). Also, closely parameterized models tend to have same fitness (existence of smaller parameterization clusters of same fitness models).
No apparent correlation can be observed between fitness and performance (MCC) maps.
Performance biomarkers
We assess important nodes (biomarkers) whose activity and/or link-operator (parameterization) affects the models performance in terms of the achieved MCC score.
emba biomarkers
We use the emba
results with \(4\) MCC classes (see script emba_analysis.R and MCC classification histograms above).
What emba (Zobolas, Kuiper, and Flobak 2020) does is to group the models to \(4\) MCC Classes and compare the average stable state activities and link operator values for each node between every possible pair of MCC classes.
Nodes that have higher average differences (either positively or negatively and between any comparison pair) are considered as more important and are thus annotated as biomarkers based on a given threshold.
In the next heatmap, a positive (resp. negative) state difference for a node denotes that its activity value was larger (resp. lower) in the higher MCC class of the corresponding comparison pair:
mcc_res = readRDS(file = "data/emba_mcc_res/mcc_4_res.rds")
# color some important nodes and lower the threshold
activity_biomarkers = emba::get_biomarkers(mcc_res$diff.state.mcc.mat, threshold = 0.48)
imp_nodes_colors = rep("black", ncol(mcc_res$diff.state.mcc.mat))
names(imp_nodes_colors) = colnames(mcc_res$diff.state.mcc.mat)
imp_nodes_colors[names(imp_nodes_colors) %in% activity_biomarkers$biomarkers.pos] = "green4"
imp_nodes_colors[names(imp_nodes_colors) %in% activity_biomarkers$biomarkers.neg] = "red4"
# define coloring function of state differences
col_fun = circlize::colorRamp2(c(min(mcc_res$diff.state.mcc.mat), 0, max(mcc_res$diff.state.mcc.mat)),
c("red", "white", "green"))
set.seed(42)
state_heat = ComplexHeatmap::Heatmap(matrix = mcc_res$diff.state.mcc.mat,
name = "State Difference", col = col_fun,
row_title = "MCC Classes Pairs", row_names_side = "left",
row_title_side = "left", row_dend_side = "right",
column_title = "Average State Differences between MCC Classes",
column_names_gp = gpar(fontsize = 6, col = imp_nodes_colors),
heatmap_legend_param = list(direction = "horizontal"))
biomarkers_legend = Legend(title = "Activity State Biomarkers",
labels = c("Active", "Inhibited"),
legend_gp = gpar(fill = c("green4", "red4")))
draw(state_heat, annotation_legend_list = biomarkers_legend,
heatmap_legend_side = "bottom", annotation_legend_side = "bottom",
merge_legend = TRUE)
So, the following nodes are identified as active biomarkers (they tend to be active in the higher performance models):
7 nodes: MAP3K7, GRAP2, MAP2K7, MAP2K3, TAB_f, ERK_f, NLK
and the following as inhibited biomarkers:
1 node: MAPK14
Note that some nodes tend to have either no actual state differences between the MCC classes (so they are not important) or they take both negative and positive differences between the different MCC class pair comparisons (without surpassing the given threshold, so also not important for us here).
# color some important nodes and lower the threshold
param_biomarkers = emba::get_biomarkers(mcc_res$diff.link.mcc.mat, threshold = 0.48)
imp_nodes_colors = rep("black", ncol(mcc_res$diff.link.mcc.mat))
names(imp_nodes_colors) = colnames(mcc_res$diff.link.mcc.mat)
imp_nodes_colors[names(imp_nodes_colors) %in% param_biomarkers$biomarkers.pos] = "green4"
imp_nodes_colors[names(imp_nodes_colors) %in% param_biomarkers$biomarkers.neg] = "red4"
# define coloring function of state differences
col_fun = circlize::colorRamp2(c(min(mcc_res$diff.link.mcc.mat), 0, max(mcc_res$diff.link.mcc.mat)),
c("red", "white", "green"))
set.seed(42)
state_heat = ComplexHeatmap::Heatmap(matrix = mcc_res$diff.link.mcc.mat,
name = "LO Difference", col = col_fun,
row_title = "MCC Classes Pairs", row_names_side = "left",
row_title_side = "left", row_dend_side = "right",
column_title = "Average Link Operator Differences between MCC Classes",
column_names_gp = gpar(fontsize = 11, col = imp_nodes_colors),
heatmap_legend_param = list(direction = "horizontal"))
biomarkers_legend = Legend(title = "Link Operator Biomarkers",
labels = c("OR-NOT (1)", "AND-NOT (0)"),
legend_gp = gpar(fill = c("green4", "red4")))
draw(state_heat, annotation_legend_list = biomarkers_legend,
heatmap_legend_side = "bottom", annotation_legend_side = "bottom",
merge_legend = TRUE)
We observe that ERK_f
will have the OR-NOT
link operator in most of the higher performance models and the MAPK14
the AND-NOT
link operator.
This of course relates to the fact that these nodes were found also as active and inhibited biomarkers respectively and that they have a very large observed agreement between stable state activity value and link operator parameterization (see analysis here).
Interestingly, these two nodes (ERK_f
and MAPK14
) were 2 of the top most important nodes influencing the change of dynamics (number of attractors) in the link operator parameterization space of the CASCADE 1.0 network.
Random Forest biomarkers
We use the ranger
R package (Wright and Ziegler 2017) to find important nodes/variables that determine the difference in performance (MCC score) between the input models.
Both the stable state data as well the link operator parameterization data will be used as training sets (see the script perf_biomarkers_ranger.R).
ranger_ss_mcc_res = readRDS(file = "data/ss_mcc_ranger_res.rds")
imp_ss_rf = tibble(nodes = names(ranger_ss_mcc_res$variable.importance),
gini_index = ranger_ss_mcc_res$variable.importance)
# color first 12 nodes in x axis
imp_col = c(rep("green4", 14), rep("grey30", nrow(imp_ss_rf) - 14))
imp_ss_rf %>%
mutate(nodes = forcats::fct_reorder(nodes, desc(gini_index))) %>%
ggplot(aes(x = nodes, y = gini_index, fill = gini_index)) +
geom_bar(stat = "identity", show.legend = FALSE) +
scale_fill_gradient(low = "steelblue", high = "red") +
theme_classic(base_size = 14) +
theme(axis.text.x = element_text(angle = 90, size = 7, colour = imp_col)) +
labs(title = "RF Importance for MCC Classification (stable states)",
x = "Nodes", y = "Mean Decrease in Node Impurity")
We observe a lot of common important activity biomarkers (important nodes) between the random forest results and the emba
results: ERK_f
,MAP3K7
,MAP2K7
,NLK
,TAB_f
to name a few.
ranger_lo_mcc_res = readRDS(file = "data/lo_mcc_ranger_res.rds")
imp_lo_rf = tibble(nodes = names(ranger_lo_mcc_res$variable.importance),
gini_index = ranger_lo_mcc_res$variable.importance)
# color first 8 nodes in x axis
imp_col = c(rep("green4", 8), rep("grey30", nrow(imp_lo_rf) - 8))
imp_lo_rf %>%
mutate(nodes = forcats::fct_reorder(nodes, desc(gini_index))) %>%
ggplot(aes(x = nodes, y = gini_index, fill = gini_index)) +
geom_bar(stat = "identity", show.legend = FALSE) +
scale_fill_gradient(low = "steelblue", high = "red") +
theme_classic(base_size = 14) +
theme(axis.text.x = element_text(angle = 90, size = 12, colour = imp_col)) +
labs(title = "RF Importance for MCC Classification (parameterization)",
x = "Nodes", y = "Mean Decrease in Node Impurity")
ERK_f
is a major link operator biomarker in both emba
and random forest results
emba vs RF Results
We check the correlation between emba
(sum of absolute state differences for all comparison pairs per node, i.e. summing up the absolute values of the columns in the heatmaps above) and random forest (Gini Index) results:
# compute total emba importance
imp_ss_emba = mcc_res$diff.state.mcc.mat %>% abs() %>% colSums()
imp_lo_emba = mcc_res$diff.link.mcc.mat %>% abs() %>% colSums()
# make sure it's in the same order
imp_ss_emba = imp_ss_emba[imp_ss_rf %>% pull(nodes)]
imp_lo_emba = imp_lo_emba[imp_lo_rf %>% pull(nodes)]
imp_ss_rf %>% add_column(emba_score = imp_ss_emba) %>%
ggplot(aes(x = emba_score, y = gini_index)) +
geom_point() +
geom_smooth(formula = y ~ x, method = "lm") +
ggpubr::stat_cor(method = "kendall", cor.coef.name = "tau") +
theme_classic() +
labs(title = "Random Forest vs emba variable importance results (Activity State)",
x = "emba (Sum of absolute state differences)", y = "RF (Gini Index)")
imp_lo_rf %>% add_column(emba_score = imp_lo_emba) %>%
ggplot(aes(x = emba_score, y = gini_index)) +
geom_point() +
geom_smooth(formula = y ~ x, method = "lm") +
ggpubr::stat_cor(method = "kendall", cor.coef.name = "tau") +
theme_classic() +
labs(title = "Random Forest vs emba variable importance results (Parameterization)",
x = "emba (Sum of absolute link operator differences)", y = "RF (Gini Index)")
- We use the non-parametric Kendall correlation method and it’s rank-based correlation coefficient, \(\tau\).
- There is a correlation between the importance results from the two different methods, even though the
emba
results are more artificially-made (like adding up absolute coefficient scores from linear models as a measure of importance) compared to the impurity metric which is a built-in feature of the tree-based algorithms such as random forests. As a result for example, we observe a much greater granularity of the importance measurements in the activity state correlation figure. emba
results are more verbose though in the sense that they allow directionality in their interpretation: we get which nodes must be activated or inhibited in the higher performance models or which should be parameterized withOR-NOT
orAND-NOT
, information which the random forests do not provide.- Larger correlation is observed for the activity state compared to the parameterization results.
Embedding Link Operator Biomarkers in the Map
Using as a base the performance parameterization maps (supervised and unsupervised) for the 1 stable state models, we color the points (models) according to the link operator value of some of the performance biomarker that we found in the previous analysis using emba
and random forests.
We give some examples of how the distribution of link-operator values looks like in the parameterization maps for the more important nodes (e.g. ERK_f
, MAPK14
) but also for the least important ones (e.g. CYCS
, CFLAR
).
See the script perf_biomarkers_embedding.R for more details.
All the produced images by the script are accessible here.
Using as a base the unsupervised parameterization MAP with 14 neighbors, we have:
knitr::include_graphics(path = "img/nodes_lo_maps/unsup_MAPK14.png")
knitr::include_graphics(path = "img/nodes_lo_maps/unsup_ERK_f.png")
knitr::include_graphics(path = "img/nodes_lo_maps/unsup_CFLAR.png")
knitr::include_graphics(path = "img/nodes_lo_maps/unsup_CYCS.png")
Using as a base the supervised parameterization MAP with 14 neighbors (MCC as continuous variable), we have:
knitr::include_graphics(path = "img/nodes_lo_maps/sup_cont_MAPK14.png")
knitr::include_graphics(path = "img/nodes_lo_maps/sup_cont_ERK_f.png")
knitr::include_graphics(path = "img/nodes_lo_maps/sup_cont_CFLAR.png")
knitr::include_graphics(path = "img/nodes_lo_maps/sup_cont_CYCS.png")
Using as a base the supervised parameterization MAP with 14 neighbors (MCC as discrete class variable), we have:
knitr::include_graphics(path = "img/nodes_lo_maps/sup_MAPK14.png")
knitr::include_graphics(path = "img/nodes_lo_maps/sup_ERK_f.png")
knitr::include_graphics(path = "img/nodes_lo_maps/sup_CFLAR.png")
knitr::include_graphics(path = "img/nodes_lo_maps/sup_CYCS.png")
We observe that both in the unsupervised parameterization maps and the MCC-supervised ones (especially the continuous one), the less important a node is, the more chaotically it’s link operator value manifests across the map.
Synergy Maps
As stated in a previous section, the CASCADE 1.0 models produced by abmlog
were tested for synergy against the drug combination dataset in Flobak et al. (2015).
Using the HSA method to define a drug combination as synergistic or not (antagonistic), we first present some useful statistics (see script synergy_maps.R):
For the ‘NA’ models, either one of the two single-drug perturbed models or the double-perturbed model, did not have any fixpoint attractors.
We observe that only a very few models (compared to the total parameterization space) can predict the observed drug combinations as synergistic. This can be visualized also in the next synergy maps:
knitr::include_graphics(path = "img/synergy_maps/AK-5Z_2nn.png")
knitr::include_graphics(path = "img/synergy_maps/AK-5Z_14nn.png")
knitr::include_graphics(path = "img/synergy_maps/PD-AK_2nn.png")
knitr::include_graphics(path = "img/synergy_maps/PD-AK_14nn.png")
knitr::include_graphics(path = "img/synergy_maps/PI-PD_2nn.png")
knitr::include_graphics(path = "img/synergy_maps/PI-PD_14nn.png")
knitr::include_graphics(path = "img/synergy_maps/PI-5Z_2nn.png")
knitr::include_graphics(path = "img/synergy_maps/PI-5Z_14nn.png")
As we have seen, the UMAP results with higher number of neighbors cluster the similarly parameterized models better. This makes it easier to spot the small clusters which include models that can predict each of the respective observed synergies as synergistic.
We note that all of these synergistic sub-clusters are part of the high-performance supercluster (right one in the images above).
Interestingly, if we see consider all possible observed synergy subsets we see that there are models that can predict the double synergy set PI-PD,PD-AK
and the PI-5Z,AK-5Z
, but there is literally no model that can predict three or four (all) of the observed synergies:
synergy_subset_stats = readRDS(file = "data/synergy_subset_stats.rds")
usefun::pretty_print_vector_names_and_values(synergy_subset_stats)
: 2675360, PI-PD: 59616, PI-5Z: 31968, PD-AK: 1536, AK-5Z: 85248, PI-PD,PD-AK: 1536, PI-5Z,AK-5Z: 31968
This is another proof that this parameterization is restrictive since there is not even one possible parameterization which represents biological reality (i.e. treatment with the previously mentioned four drug combinations is synergistic).
Also, looking at the numbers of models for each set of synergies, we see that the models predicting the PD-AK
synergy form a proper subset of the PI-PD,PD-AK
models and the same for PI-5Z
and PI-5Z,AK-5Z
.
Thus we include only the parent sets in the next synergy maps, where we have combined all synergies in one:
knitr::include_graphics(path = "img/synergy_maps/all_syn_11nn.png")
knitr::include_graphics(path = "img/synergy_maps/all_syn_14nn.png")
We observe that there are two synergy sub-clusters, one primarily related to the PD
drug and the other to the 5Z
drug.
Since the synergies belong to \(2\) mutually exclusive (in terms of parameterization) clusters, this is a visual cue that there cannot be a model that predicts all 4 of these synergies.
Stable State Patterns in Synergistic models
We want to identify if there are activity patterns in the models that predict the observed synergies.
To do this, we take a random sample of such synergistic models that predict each of the \(4\) observed synergies and show the corresponding heatmaps of stable state activities. More details on the synergy_heatmaps.R script.
- All the models that predict the
PD-AK
synergy show one distinct activity state pattern - The
PI-PD
heatmap is more heterogeneous (though still patterns do exist). The reason for this might be because the parameterization is more spread out across the map in the combined synergy figure above - i.e. some models are in the low-performance supercluster.
All the synergistic models in the 5Z
sub-cluster seem to follow the same stable state activity patterns (pretty much we get the same node names in the columns after the clustering in the heatmaps).
There exist \(2\) main such patterns, specified by the state vector (ON,{ON/OFF},{OFF/ON},ON) - where ON and OFF denote vectors of active/inhibited nodes that are clustered together.
Note that in all \(4\) heatmaps above, the Prosurvival
node is always active, denoting proliferating activity patterns in the boolean models where synergies can manifest.
Synergy Biomarkers
We assess important nodes (biomarkers) whose activity and/or link-operator (parameterization) affects the manifestation of the \(4\) observed synergies.
Here, emba
(Zobolas, Kuiper, and Flobak 2020) performs the classification to synergistic and antagonistic model groups per synergy observed (as exemplified by the figure above - the NA
models are discarded) and compares the average stable state activities and link operator values of each node in the two groups.
In the next heatmap, a positive (resp. negative) state difference for a node denotes that its activity value was larger (resp. lower) in the corresponding synergistic model group:
syn_res = readRDS(file = "data/synergy_res.rds")
# define coloring function of state differences
col_fun = circlize::colorRamp2(c(min(syn_res$diff.state.synergies.mat),
0, max(syn_res$diff.state.synergies.mat)), c("red", "white", "green"))
set.seed(42)
state_syn_heat = ComplexHeatmap::Heatmap(matrix = syn_res$diff.state.synergies.mat,
name = "State Difference", col = col_fun,
row_title = "Synergistic Drug Combinations", row_names_side = "left",
row_title_side = "left", row_dend_side = "right",
column_title = "Average State Differences (Synergistic vs Antagonistic)",
column_names_gp = gpar(fontsize = 5),
heatmap_legend_param = list(direction = "horizontal"))
biomarkers_legend = Legend(title = "Activity State Biomarkers",
labels = c("Active", "Inhibited"),
legend_gp = gpar(fill = c("green4", "red4")))
draw(state_syn_heat, annotation_legend_list = biomarkers_legend,
heatmap_legend_side = "bottom", annotation_legend_side = "bottom",
merge_legend = TRUE)
- The activity state biomarkers are clustered in two categories representing the two synergy sub-clusters we found previously, namely the
5Z
sub-cluster and thePD
sub-cluster. - The models predicting the
5Z
synergies seem to have more active state biomarkers, whereas the models predicting thePD
synergies seem to have more inhibited state biomarkers. MAPK14
seems to be a definitive node distinguishing the two synergy subgroups, since it’s a strong inhibited biomarker for the5Z
synergies, but a fairly unimportant node for thePD
synergies.ERK_f
is an active state biomarker (same observation was found in the performance biomarkers analysis)
In the next heatmap, a positive (resp. negative) link operator difference for a node denotes that its link operator was mostly OR-NOT
(resp. AND-NOT
) in the corresponding synergistic model group:
# define coloring function of state differences
col_fun = circlize::colorRamp2(c(min(syn_res$diff.link.synergies.mat),
0, max(syn_res$diff.link.synergies.mat)), c("red", "white", "green"))
set.seed(42)
lo_syn_heat = ComplexHeatmap::Heatmap(matrix = syn_res$diff.link.synergies.mat,
name = "Link Operator Difference", col = col_fun,
row_title = "Synergistic Drug Combinations", row_names_side = "left",
row_title_side = "left", row_dend_side = "right",
column_title = "Average Link Operator Differences (Synergistic vs Antagonistic)",
column_names_gp = gpar(fontsize = 11),
heatmap_legend_param = list(direction = "horizontal"))
biomarkers_legend = Legend(title = "Link Operator Biomarkers",
labels = c("OR-NOT (1)", "AND-NOT (0)"),
legend_gp = gpar(fill = c("green4", "red4")))
draw(lo_syn_heat, annotation_legend_list = biomarkers_legend,
heatmap_legend_side = "bottom", annotation_legend_side = "bottom",
merge_legend = TRUE)
ERK_f
is a strong synergy OR-NOT biomarker for all synergies (same as in the performance link-operator biomarkers section and shown in this figure)MAPK14
is again the node that mostly distinguishes between the two synergy sub-clusters ofPD
and5Z
. This also relates to the embedding of theMAPK14
link-operator value as demonstrated by this figure, where theMAPK14
node has theOR-NOT
link-operator in thePD
sub-cluster andAND-NOT
in the5Z
sub-cluster.


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public interface ThreadPoolConfig
The thread pool configuration.
| Nested Class Summary | |
|---|---|
static class |
ThreadPoolConfig.ThreadPoolPolicy
The thread pool policies. |
static class |
ThreadPoolConfig.ThreadPriority
|
| Method Summary | |
|---|---|
ThreadPoolConfig.ThreadPoolPolicy |
getBlockPolicy()
Return the block policy. |
java.util.concurrent.ThreadFactory |
getFactory()
Return the thread pool factory. |
long |
getKeepAliveTime()
Return the keep alive time. |
int |
getMaxPoolSize()
Return the maximum pool size |
int |
getMinPoolSize()
Return the minimum pool size. |
ThreadPoolConfig.ThreadPriority |
getPriority()
Return the priority for the threads. |
int |
getQueueSize()
Return the queue size. |
int |
getShutdownWaitTimeMs()
Return the shutdown wait time in ms. |
boolean |
isDaemon()
Return if daemon threads should be created. |
boolean |
isShutdownGraceful()
Should this pool shutdown graceful. |
| Method Detail |
|---|
int getMinPoolSize()
int getMaxPoolSize()
int getQueueSize()
long getKeepAliveTime()
ThreadPoolConfig.ThreadPoolPolicy getBlockPolicy()
boolean isShutdownGraceful()
true
if the pool should shutdown graceful.int getShutdownWaitTimeMs()
java.util.concurrent.ThreadFactory getFactory()
null
ThreadPoolConfig.ThreadPriority getPriority()
boolean isDaemon()
true
if daemon threads should be created.
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"SILVER "used for a great variety of purposes, as may be judged from the frequent references to it in Scripture. It first appears in commerce in Genesis 13:2; 23:15, 16. It was largely employed for making vessels for the sanctuary in the wilderness (Exodus 26:19; 27:17; Numbers 7:13, 19; 10:2). There is no record of its having been found in Syria or Palestine. It was brought in large quantities by foreign merchants from abroad, from Spain and India and other countries probably.
"SILVER "used for a great variety of purposes, as may be judged from the frequent references to it in Scripture. It first appears in commerce in Genesis 13:2; 23:15, 16. It was largely employed for making vessels for the sanctuary in the wilderness (Exodus 26:19; 27:17; Numbers 7:13, 19; 10:2). There is no record of its having been found in Syria or Palestine. It was brought in large quantities by foreign merchants from abroad, from Spain and India and other countries probably.


US hopes to woo more Pakistani students
by AFPISLAMABAD: The US is hoping to attract more students from the country and for this purpose, it organised a ceremony to recognize high-achieving local students.
The US Embassy, Education USA and the US Educational Foundation in Pakistan (USEFP) celebrated International Education Week (IEW) from November 18 to 22. During the week they encouraged potential Pakistani leaders to study in the United States.
“Over 7,900 Pakistanis are currently studying at universities across the US, and the US government will like to increase that number,” said US Embassy Minister Counselor for Public Affairs Lisa Heller during the annual High Achievers’ and Counselors’ Recognition Ceremony in Islamabad.
“A five per cent increase has already been recorded in Pakistani student enrollments this year as compared to last year,” Heller said, adding that Pakistan is amongst the fastest growing pool for international students in the US and its government is committed to supporting educational opportunities and academic exchanges for Pakistani students from all backgrounds.
“IEW activities are planned and organised by Education USA advisers who have firsthand experience studying in the US and are well-positioned to guide students through their applications,” said USEFP Executive Director Rita Akhtar.


{% extends 'partials/pane.html' %} {% block id %}index_example_pane{% endblock %} {% block title %}
Click here for a sample board.
Only you can call numbers on the board you have created.


Click the spinning cog above to convert between F and C
Design & Development by Jonathan M. Brunt
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Got to make them pay for let-off: Mayank
by Amol KarhadkarWhile some of his teammates were having a net session under lights with the pink ball after stumps on the second day, Mayank Agarwal addressed the media with a content look.
Having ground his way through the domestic system to earn his maiden Test cap, he wasn’t excited at scoring his second double hundred in as many series. The opening batsman showed no signs of fatigue, even after batting for almost four sessions while plundering 243 runs against Bangladesh, indicating his fitness regime.
Excerpts from his interaction:
On whether he thanked Imrul Kayes for dropping him on the first evening
That’s not in my control but I was thinking that if I have got a chance, I have got to make them pay. When you get a let-off, you make sure that you make them pay.
On his mindset to play long innings
I think it goes down to understanding that there had been times when I have not got runs, so I must respect that I am batting well. When I am doing that I have to make sure that I make it big and put the team in the driver’s seat or in such a position that they cannot lose from there.
On his thoughts when he was given lbw paddle-sweeping before it was overturned through DRS
Initially when I was hit on the pads, I thought I wasn’t out. But then when I was given out, I had a quick flashback. I got out the same way to Roston Chase in the West Indies, so I said I can’t make that mistake again. I am glad and happy that (replays showed) it was missing (the stumps). At some point I thought, may be it will be umpires call and I will be doomed.
On reaching the double with a six
I was not thinking of that. I was thinking to give 4-5 overs and go 1, 1, 1, 1 unless something was in my half. Because I was batting on 196, I was confident. I got one in my slot and I hit it for six.
On how he unwinds in the evenings after a superb knock
I play PUBG.


This is an unofficial Alexa edition of the Elevation of Privilege threat modelling card game
https://www.amazon.co.uk/0x10-Threat-Model-Game/dp/B01NCV9EPB/ref=cm_cr_arp_d_bdcrb_top?ie=UTF8
https://github.com/zeroXten/alexa_threat_model_game
https://github.com/zeroXten/alexa_threat_model_game/issues
https://www.microsoft.com/en-us/SDL/adopt/eop.aspx


Help for Virtual Scene Constructor
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Dojo’s Grid
is a reactive lightweight, customizable grid widget.
Features
- On-demand virtual rendering with supports for large data-sets
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Backed by
@dojo/framework/stores
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Unlike the PayPal API, we keep things nice and simple. For basic usage just provide a username:

You can also set a size. We use pixels and we only need the width. Just add it to the end.

And because some services require an extension, we've added simple support for it. Just add .png to the end.

Sometimes you want to display a helm too, that's fine with this endpoint:

You can even use Minotar's API to get a users skin. We're adding more soon!

You can also set the browser to download the image by using:
https://minotar.net/download/clone1018
Need Steve? Use "char" as the username:


Identifies the optimal number of clusters by calculating the similarity between two clustering methods at the same number of clusters using the corrected indices of Rand and Jaccard as described in Albatineh and Niewiadomska-Bugaj (2011). The number of clusters at which the index attain its maximum more frequently is a candidate for being the optimal number of clusters.
| Version: | 1.0 |
| Depends: | R (≥ 3.1.0) |
| Imports: | MASS, CircStats, stats, graphics |
| Published: | 2018-10-08 |
| Author: | Ahmed N. Albatineh, Meredith L. Wilcox, Bashar Zogheib, Magdalena Niewiadomska-Bugaj |
| Maintainer: | Ahmed N. Albatineh <aalbatineh at hsc.edu.kw> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | MCSim results |
| Reference manual: | MCSim.pdf |
| Package source: | MCSim_1.0.tar.gz |
| Windows binaries: | r-devel: MCSim_1.0.zip, r-release: MCSim_1.0.zip, r-oldrel: MCSim_1.0.zip |
| OS X binaries: | r-release: MCSim_1.0.tgz, r-oldrel: MCSim_1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=MCSim to link to this page.


An exciting, diverse, and winding road has led me to full stack web development, and to Austin, TX. I graduated from Gettysburg College (PA) with a degree in Native American Studies, Art History, and Environmental Studies. I have worked as a National Park Ranger, math teacher, event director, operations manager, and wildland firefighter.
I have lived and traveled globally and am a dog mama to the cutest mini dachshund in Austin. I am also an avid reader, math geek, outdoor enthusiast, thrifter, coffee connoisseur, and an obsessive dancer of all sorts, including a dozen “couples” dances. Some of my favorite dances are west coast swing, country two-step, and Argentine tango fusion.
Javascript, Node.JS, React, HTML5, CSS3, MySQL, MongoDB, Responsive Design
Boxer is a content aggregator that combines a customer's previous search history on the Boxer site with user entered details about their dog and creates boxes of different values. Each box contains 4 unique pet-centric products tailored to the customer and their chosen price point. Customers may choose a mystery box, or they may decide to select their products from a list generated from an API call to Etsy. If a user navigates away from the choice page they will likely not see the same products upon return. Try it out on Heroku. Peek under the hood on Github.
Developers:
Technology/ Frameworks Used:
TripAround allows users to make informed choices about whether to fly or drive when making a trip. The application uses origin and destination data to calculate driving costs (based on 25 mpg and the national average gas price) for a road trip, then compares the driving cost to real-time airfare costs. If the travel route does not start or end close to an airport then no flight results will populate. Check out the vanilla JavaScript version, as well TripAround V2.0, developed with React.
Developers:
Technology/ Frameworks Used:
How good is your memory? Test it with Click, Click, Click, a full-stack web application built with React. The app randomly renders images to the screen, keeping track of their clicked state - true or false - depending on whether they they have been selected by the user. The user's score increases as new images are clicked. The game ends if an image is selected twice. Sound easy? There's a twist - each time an image is clicked, all of the images are shuffled and their locations are randomly changed. Peek under the hood on Github. Image by Joseph Chan.
Technology/ Frameworks Used:
Mabel's Mega Milkshakes is a full-stack web application that allows users to manipulate data on a dynamic restaurant menu. Users may add, delete, or move milkshakes between the current and past milkshake menus. Applying the MCV design pattern to query and route data, Mabel's Mega Milkshakes uses MySQL, Node, Express, and Handlebars and a homemade ORM. Mabel's Mega Milkshakes Data is called, stored, or deleted from a MySQL database. HTML templating is assisted with Handlebars. Try it out on Heroku. Peek under the hood on Github. Image by Toa Heftiba
Technology/ Frameworks Used:
This JS and Express-Node.js app finds a best furrier friend for your best furry friend! The app takes in survey data, calculates and compares this data to stored and new user data, and returns a pal whose survey score closest matches the score of your pal. Try it out on Heroku. Peek under the hood on Github. Image by Wynand van Poortvliet.
Technology/ Frameworks Used:
This app uses Firebase to host arrival and departure data for a fictional train. Using Moment.js, the app provides to-the-minute arrival times calculated upon their morning departures. Peek under the hood on Github. Suggest or view a train for Charlie. Image by Michael Kvrivishvili.
Technology/ Frameworks Used:
Julie Anne’s Zon, or Jamazon, is a command line application that functions a bit like Amazon. It uses Node.JS and MySQL to create and manipulate a product database. Peek under the hood on Github.
Technology/ Frameworks Used:
This entertainment-node-app is a Language Interpretation and Recognition Interface, or LIRI for short. LIRI takes specific parameters for simple entertainment-related search requests and returns relevant results. Feed LIRI a movie title, band/ artist name, song title, or Tweet request and she will provide you with more details regarding these queries. LIRI is coded with Javascript and uses Node packages to work her magic. She provides her search results by making API calls to Bands in Town, Spotify, OMDB, and Twitter. LIRI also keeps a log of results from her searches. Peek under the hood on Github. Image by John Carlisle.
Technology/ Frameworks Used:
This app uses JavaScript and jQuery to dynamically populate search results from GIPHY API. Use the search bar to find additional dance giphys or enjoy the dances that are suggested. Peek under the hood on Github. Image by Ardian Lumi.
Technology Used:
This word guess game is a take on the primary school game "Hangman." Upon page load, national park names are randomly selected from an array, separated into individual letters and spaces that represent each word are displayed to the player. The player then uses the letter pad to guess possible letters in the park name. The players accurate and inaccurate guesses are tracked. Resultant wins or losses are continually tallied as the player continues to play subsequent rounds. Can you guess the names of these national parks? Hints are provided! Play it out on Github. Peek under the hood on Github. Image by Sasha Stories.
Technology Used:
This timed multiple choice trivia game uses Javascript for the logic and jQuery to manipulate its HTML. How well do you know your odd and unusual presidential trivia? Peek under the hood on Github. Image by Ben Noble.
Technology Used:
Upon page load, this fun math game randomly chooses a target number between 30 and 120, as well as randomly assigns values to each of four crystals. The player's goal is to figure out the value of each crystal, then use a combination of those values to reach the target score. Try your luck! Peek under the hood on Github. Image by Carole Smile.
Technology Used:


Hello and welcome! I am a sexuality researcher turned data scientist. As a data scientist with a social science background, I made this website for people who want to utilize their analytical skill set in the business world, but have no idea where to start. Or perhaps you are interested in data science but are intimidated by the computer programming and digital marketing requirements - skill sets that most social scientists know nothing about. Never fear! If you have a solid foundation of statistics and a passion for data, than you have the most important requirements as a data scientist. This website will tell you what skills you need to learn, how to learn them, and how you can apply them in the field.
I am the head of digital analytics at Creative Agency Chicago (CACH). CACH is a group of creative freelancers from around the world that specialize in corporate identity, website development, e-commerce, and marketing strategies. In short, my department aims to glean consumer insight to optimize product performance.
When I enrolled in the clinical psychology doctoral program at Northwestern University in 2012, my end goal was to join the academic ranks of the famous sexuality researchers that came before me. After all, I had the privilege of working with one of the greatest minds in the field - Michael Bailey. However, halfway through my doctoral program, I began to question whether academia was the right fit for me when I became involved with CACH by happenstance.
For a year, I peripherally watched a group of my friends build CACH from the groud up. I learned how account managers, web-designers, and web-developers interacted to produce digital content. In contrast to academia, CACH was fast-paced and success was based on collaboration and networking. One successful project would lead to a referral for another. The growth of CACH imbued my entrepreneurship spirit.
During this time, a friend in my doctoral program introduced me to the field of data science. As a recently hired data scientist, he showed me how to supplement my analytical skills for the digital market. In 2015, I created the analytics department at CACH. My success at CACH in the past year and my advisor's blessing lead me to leave academia in 2016.
I believe that my unique path to the world of data science is what sets me apart from other data scientists. The areas of the field in which I need more experience are balanced by my strong communication and analytical skills I refined in the four years I trained as an empirically-oriented researcher. As a result, I am confident and eager to exceed the needs of my clients.
No matter what kind of business I work with, I believe that the analytical process to a successful business strategy remains the same.


2 Corinthians 8:21
Providing for honest things, not only in the sight of the Lord, but also in the sight of men.


CrimPapers
Articles published on 11 September 2018
On 11 September 2018, 10 new articles were published in 9 journals.
Correction
Today Was Not A Good Day: Offender Accounts of the Incidents That Led to Their Admission to Drug Court by O. Hayden Griffin, Vanessa Woodard Griffin, Heith Copes, John Andrew Dantzler
Adolescent Male Sports Participation and Violence in Emerging Adulthood: Examining Variation By Gender-Typed Behavior by Jessica G. Finkeldey
Examining Racial and Ethnic Variations in Reasons For Leaving A Youth Gang
Ryse Youth Center: Youth Participatory Action Research
Treading the Front-Line: Tartanization and Police–academic Partnerships
Police Chiefs and Their Political Bosses: Discussions in Ontario
Credibility Judgments in Context: Effects of Emotional Expression, Presentation Mode, and Statement Consistency by Sara Landström, Karl Ask, Charlotte Sommar
Note: although this article was published on 5 September 2018, the publisher only added it to the journal RSS feed on 11 September 2018, which is why it is shown in the list of articles for that date.
Military Counterterrorism Measures, Civil–military Relations, and Democracy: the Cases of Turkey and the United States by Nil S. Satana, Tijen Demirel-Pegg
The Views of Women in Prison About Help-Seeking For Intimate Partner Violence: At the Intersection of Survivor and Offender by Andrew Day, Adam Gerace, Candice Oster, Deb O’kane, Sharon Casey


{% trans "Please enter your old password, for security's sake, and then enter your new password twice so we can verify you typed it in correctly." %}
{% extends "account/base.html" %} {% load i18n bootstrap3 %} {% block content %}
{% trans "Please enter your old password, for security's sake, and then enter your new password twice so we can verify you typed it in correctly." %}


We offer numerous services specific to DataDreamers customers and the Wisdom platform, which are broken down into three levels of support.
Select any item for more information.
If you'd like to work with us, please fill out this brief form, and we will contact you within 24 to 48 hours.
Please send all general inquiries to: info@stonecaststudio.com


Apache HTTP Server 版本2.2
| 说明 | 为mod_dav 锁定服务器上的文件提供支持 |
|---|---|
| 状态 | 扩展(E) |
| 模块名 | dav_lock_module |
| 源文件 | mod_dav_lock.c |
| 兼容性 | 仅在 Apache 2.1 及以后的版本中可用 |
This module implements a generic locking API which can be used by any
backend provider of mod_dav
. It requires at least
the service of mod_dav
. But without a backend provider
which makes use of it, it's useless and should not be loaded into the
server. A sample backend module which actually utilizes
mod_dav_lock
, is mod_dav_svn, the subversion provider module.
Note that mod_dav_fs
does not need this
generic locking module, because it uses it's own more specialized
version.
In order to make mod_dav_lock
functional, you just have
to specify the location of the lock database using the DavGenericLockDB
directive described
below.
In order to retrieve the pointer to the locking provider function, you
have to use the ap_lookup_provider
API with the arguments
dav-lock
, generic
和0
.
| 说明 | Location of the DAV lock database |
|---|---|
| 语法 | DavGenericLockDB file-path |
| 作用域 | server config, virtual host, directory |
| 状态 | 扩展(E) |
| 模块 | mod_dav_lock |
Use the DavGenericLockDB
directive to specify
the full path to the lock database, excluding an extension. If
the path is not absolute, it will be taken relative to ServerRoot
. The implementation of
mod_dav_lock
uses a SDBM database to track user
locks.
DavGenericLockDB var/DavLock
The directory containing the lock database file must be
writable by the User
和Group
under which
Apache is running. For security reasons, you should create a
directory for this purpose rather than changing the permissions on
an existing directory. In the above example, Apache will create
files in the var/
directory under the ServerRoot
with the base filename
DavLock
and extension name chosen by the server.


Properties for the Run Sql component can be viewed by Double click component on canvas. The properties contain a 'General' tab. Common and mandatory properties are present in the General tab.
- Name - The Name field will specify the name of the component as desired by the user.
- ID - ID field will specify unique id for every component.
- Type - Type further categorizes the component to detailed operation that it intends to perform. Here it is of type command Run Sql.
- Database - Database Name used to specify to select database from combo box. By default this is Oracle.
if user selects "Parameter", provide a textbox where user can input the Database Name.
- Database Name - Database Name used to specify the database. This field should be mandatory to user.
- Host Name - Host Name used to specify the Host server IP address or localhost name. This field should be mandatory to user.
- Port - Port simply accepts a numeric number starting from 0 to maximum 65535. By default this is 1521.
- User - User used to specify the User name Logon to specify Database.
- Password - Password used to specify the password.
- Sql Query - Sql Query used to specify a multiline text box where user can type the query to be executed
- Batch - Batch simply accepts a numeric number starting from 0 to maximum 99 and signifies the batch this component will execute in. By default this is 0.
The Run Sql components applies validations to the mandatory fields. Upon placing the
Run Sql component on job canvas for the first time (from component palette), the component shows up a warning icon as
mandatory properties are not provided.
The properties window also displays error icon on mandatory fields if it has an incorrect value. The error icon
is displayed on the tab as well, if any of the field within the tab has some error.
If the properties window has some error even after user visit's it once, then the warning icon on the Run Sql
component on the job canvas changes to error icon. This error icon is removed only when all the mandatory fields are supplied
with correct values.


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LEARN, COLLABORATE, HANG OUT
Not everything can be learned in a class. Listen as professionals teach you tips, tricks, and techniques they use in their fields. Expose yourself to new ideas outside of your major. Grab your laptop and a sketchbook to get most out of your conference experience!
Come ready to meet, be involved, and get feedback from people who know what they’re doing. Leave the formalities behind and engage with top designers. Ask anything you want, from what it’s like to work in the field to the meaning of life. Remember they used to be students, too.
Kick back and hang out with speakers, sponsors, and students from all over. Make new friends and reunite with old ones. The conference is built for you to have a good time. Relax and have fun over free food, in between workshops or at one of the after-parties.


The estimated work remaining in the sprint is calculated daily and graphed, resulting in a Sprint Burndown Chart The vertical axis displays the hours of effort remaining for the Sprint. The horizontal axis displays
the days of the Sprint. The burndown is supposed to be shown by the line of descent from the start of the Sprint
with the starting hours, down to the end of the Sprint with no hours remaining.
The team does its best to pull the right amount of work into the sprint but sometimes too much or too little work is
pulled in during the Sprint Planning Meeting in this case the team needs to add or remove tasks. In the
above sprint burndown chart you can see that the team had pulled in too much work initially and still had nearly 600
hours to go on 5/16/02. In this case the Product Owner was consulted and it was agreed to remove some work from the sprint,
which resulted in the big drop on the chart between 5/16/02 (619 hours) and 5/17/02. From there the team made good
consistent progress and finished the sprint successfully.
|


Ingredients
{{instructions}}
Hover over a spirit to see what you can make with it. Click on a cocktail to view the recipe.
gin
whisky
rum
brandy
{{ item.name }}


|
CubeAnimator
An animation tool for cube map to fisheye conversion.
|
Public Slots | |
| void | changeSelection () |
| void | addPoint () |
| void | deletePoint () |
| void | setBezierInterpolation () |
| void | setLinearInterpolation () |
| void | setConstantInterpolation () |
| void | changeScale (QPointF scaleFactor) |
| void | zoomTo (float z) |
| void | setSnap (float sX, float sY) |
| void | moveFrameCursor (float at) |
Signals | |
| void | scaleChanged (QPointF newScale) |
| void | zoomChanged (QPointF, QPointF) |
| void | curvesChanged (float min, float max) |
Public Member Functions | |
| CurvesView (QObject *parent=0) | |
| void | addCurve (CurveItem *curve) |
| void | setModel (CurveVariableListModel *model) |
| void | setSelectionModel (QItemSelectionModel *selectionModel) |
| CurveCursor * | getCursor () const |
| CurveCursor * | getFrameCursor () const |
Protected Member Functions | |
| void | contextMenuEvent (QGraphicsSceneContextMenuEvent *contextMenuEvent) |
| void | mouseMoveEvent (QGraphicsSceneMouseEvent *mouseEvent) |
| void | wheelEvent (QGraphicsSceneWheelEvent *event) |
| void | mouseReleaseEvent (QGraphicsSceneMouseEvent *mouseEvent) |
| void | revertSelection () |
Protected Attributes | |
| CurveHandleItem * | lastSelected |
| CurveHandleItem * | currentSelection |


Decentralized Video Platforms
If you are currently using a platform like YouTube or Vimeo, you should pick an alternative here.
{% include cardv2.html
title="PeerTube"
image="/assets/img/svg/3rd-party/peertube.svg"
description='PeerTube, developed by Framasoft, is a free and decentralized alternative to video platforms, with over 400,000 videos published by over 50,000 users and viewed over 10 million times. If you are looking for a server to join, you are welcome to join our hosted instance: tube.privacytools.io'
website="https://joinpeertube.org"
github="https://github.com/Chocobozzz/PeerTube"
web="https://joinpeertube.org/#what-is-peertube"
%}


Splits the text by a separator. If the separator is set to Custom you can add a customSeparator to split by any character you want.
splitText(text: <#Array or String#>, separator: <#Type (WFTextSeparator)#>, customSeparator: <#String (Allows Variables)#>)
splitText(text: "["Hello-World", "Jello-World"]", separator: Custom, customSeparator: "-")


|
Pebble Foundation Classes
0.2.0
C++ for Pebble
|
#include <pebble.h>
Public Attributes | |
| struct { | |
| PublishedId icon | |
| const char * subtitle_template_string | |
| } | layout |
| Describes how the slice should be visualized in the app's glance in the launcher. | |
| time_t | expiration_time |
An app's glance can change over time as defined by zero or more app glance slices that each describe the state of the app glance at a particular point in time. Slices are displayed in the order they are added, and they are removed at the specified expiration time.
| time_t AppGlanceSlice::expiration_time |
The UTC time after which this slice should no longer be shown in the app's glance. Use APP_GLANCE_SLICE_NO_EXPIRATION if the slice should never expire.
| PublishedId AppGlanceSlice::icon |
The published resource ID of the bitmap icon to display in the app's glance. Use APP_GLANCE_SLICE_DEFAULT_ICON to use the app's default bitmap icon.
| const char* AppGlanceSlice::subtitle_template_string |
A template string to visualize in the app's glance. The string will be copied, so it is safe to destroy after adding the slice to the glance. Use NULL if no string should be displayed.


(Sized from iPhone screen)
Made with Phaser / Based upon tutorial at LessMilk
Sounds from SoundBible Images from Carnivius @ DeviantArt


We examine interfaces!
- engage value-added networks
- synthesize visionary deliverables
- deliver dynamic metrics
- enhance web-enabled e-services
- recontextualize next-generation applications
- integrate strategic channels
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- transform customized vortals
- exploit wireless vortals
- e-enable viral partnerships
- benchmark transparent synergies


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PuppetName
public enum PuppetName : String
Undocumented
-
Undocumented
Declaration
Swift
case monkey
-
Undocumented
Declaration
Swift
case robot
-
Undocumented
Declaration
Swift
case cat
-
Undocumented
Declaration
Swift
case dog
-
Undocumented
Declaration
Swift
case alien
-
Undocumented
Declaration
Swift
case fox
-
Undocumented
Declaration
Swift
case poo
-
Undocumented
Declaration
Swift
case pig
-
Undocumented
Declaration
Swift
case panda
-
Undocumented
Declaration
Swift
case rabbit
-
Undocumented
Declaration
Swift
case chicken
-
Undocumented
Declaration
Swift
case unicorn
-
Undocumented
Declaration
Swift
public static let all: [Animoji.PuppetName]


Here are some of the most recent tweets downloaded from the streaming API about the specified search terms.


Die (noun): a device for cutting or molding metal into a particular shape. an engraved device for stamping a design on coins or medals
New Oxford American Dictionary 3rd edition © 2010 by Oxford University Press, Inc.
Die.js is a amalgamation of eRuby (ERB) and Jinja templates. The implementation of the templating engine was originally derived from Underscore.js, but provides features from Jinja such as template inheritance, blocks, includes, and macros.
Note: Die.js is incomplete. Template inheritance and blocks have been implemented, but includes and macros are forthcoming.
The project is hosted on BitBucket in a Git repository.
Die compiles JavaScript templates into functions that can be evaluated for rendering. Templates are useful for rendering complicated bits of HTML from JSON data sources. Template functions can interpolate variables, using <%= … %>, as well as execute arbitrary JavaScript code, with <% … %>. If you wish to interpolate a value, and have it be HTML-escaped, use <%- … %>.
Templates are compiled by calling Die's compile method. The first argument to compile is the template's source, the second argument is an optional name that can later be used to reference the compiled template. The method returns the compiled template as a JavaScript function that takes an object as an argument.
When you evaluate a template function, pass in a data object that has properties corresponding to the template's free variables.
var template = Die.compile("hello: <%= name %>"); template({name : 'moe'}); => "hello: moe" var template = Die.compile("<%- value %>"); template({value : '
If eRuby-style delimiters aren't your cup of tea, you can change Die's template settings to use different symbols to set off interpolated code. Define an interpolate regex to match expressions that should be interpolated verbatim, an escape regex to match expressions that should be inserted after being HTML escaped, and an evaluate regex to match expressions that should be evaluated without insertion into the resulting string. You may define or omit any combination of the three. For example, to perform Mustache.js style templating:
Die.settings = { interpolate : /\{\{(.+?)\}\}/g }; var template = Die.compile("Hello {{ name }}!"); template({name : "Mustache"}); => "Hello Mustache!"
Portions of this project and its documentations are derived from other projects and their documentation.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.


Enter Concept
URI Saved
Saved Uri
Saved Uris
Similarity
Textual information
Disambiguation
Disambiguation
Classes
Display classes
Subject
Subject
Links & FOAF Info
Links & FOAF Info
Information
Textual information
Properties


[1]
extremely comfortable and great after walking all day , a choice of 3 waters -lrb-/-lrb- sparkling .
[2]
the jewish quarter and paid for some extra surprises since it was our honeymoon .


--- layout: post status: publish published: true title: OS X - How to find downloaded file list? author: display_name: hojunbaek login: hojunbaek email<EMAIL_ADDRESS>url: '' author_login: hojunbaek author_email<EMAIL_ADDRESS>wordpress_id: 1762 wordpress_url: http://ezcocoa.com/?p=1762 date: '2013-01-22 00:15:15 +0900' date_gmt: '2013-01-22 00:15:15 +0900' categories: - OSX tags: - mac - file - list - download comments: [] ---
sqlite3 ~/Library/Preferences/com.apple.LaunchServices.QuarantineEventsV* 'select LSQuarantineDataURLString from LSQuarantineEvent'


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Presentations
OpenStack Meetup Boston
Unit test in C using Cmockery2
OpenStack Summit Atlant 2014
Devconf.cz 2015
Blog
About me
Luis Pabon is a Principal Software Engineer member of Red Hat Storage and creator of Pblcache. Prior to joining Red Hat in February of 2013 he worked at NetApp Advanced Technology Group and at EMC on various storage products.


A P I
The use of API that is the most appropriate for my project, is Twitter. Since I would be able to tweet updates on how often my cat would be using the toy, with a connection of a sensor that will help send these updates. However, a way to tweet different sequence of sentences is needed, to avoid spamming and being blocked by that. Getting the API of Twitter took a while because Twitter needed to know all the details on why you would be needing the API and its keys.
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Actions, resources, and condition keys for AWS AppSync
AWS AppSync (service prefix:
appsync
) provides the following service-specific resources, actions, and condition context keys for use in IAM permission policies.
References:
-
Learn how to configure this service .
-
View a list of the API operations available for this service .
-
Learn how to secure this service and its resources by using IAM permission policies.
Topics
Actions defined by AWS AppSync
You can specify the following actions in the
Action
element of an IAM policy statement. Use policies to grant permissions to perform an operation in AWS. When you use an action in a policy, you usually allow or deny access to the API operation or CLI command with the same name. However, in some cases, a single action controls access to more than one operation. Alternatively, some operations require several different actions.
The
Resource types
column of the Actions table indicates whether each action supports resource-level permissions. If there is no value for this column, you must specify all resources ("*") to which the policy applies in the
Resource
element of your policy statement. If the column includes a resource type, then you can specify an ARN of that type in a statement with that action. If the action has one or more required resources, the caller must have permission to use the action with those resources. Required resources are indicated in the table with an asterisk (*). If you limit resource access with the
Resource
element in an IAM policy, you must include an ARN or pattern for each required resource type. Some actions support multiple resource types. If the resource type is optional (not indicated as required), then you can choose to use one of the optional resource types.
The
Condition keys
column of the Actions table includes keys that you can specify in a policy statement's
Condition
element. For more information on the condition keys that are associated with resources for the service, see the
Condition keys
column of the Resource types table.
For details about the columns in the following table, see Actions table .
| Actions | Description | Access level | Resource types (*required) | Condition keys | Dependent actions |
|---|---|---|---|---|---|
| AssociateApi | Grants permission to attach a GraphQL API to a custom domain name in AppSync | Write | |||
| AssociateMergedGraphqlApi | Grants permission to associate a merged API to a source API | Write | |||
| AssociateSourceGraphqlApi | Grants permission to associate a source API to a merged API | Write | |||
| CreateApiCache | Grants permission to create an API cache in AppSync | Write | |||
| CreateApiKey | Grants permission to create a unique key that you can distribute to clients who are executing your API | Write | |||
| CreateDataSource | Grants permission to create a data source | Write | |||
| CreateDomainName | Grants permission to create a custom domain name in AppSync | Write | |||
| CreateFunction | Grants permission to create a new function | Write | |||
| CreateGraphqlApi | Grants permission to create a GraphQL API, which is the top level AppSync resource | Write |
iam:CreateServiceLinkedRole |
||
| CreateResolver | Grants permission to create a resolver. A resolver converts incoming requests into a format that a data source can understand, and converts the data source's responses into GraphQL | Write | |||
| CreateType | Grants permission to create a type | Write | |||
| DeleteApiCache | Grants permission to delete an API cache in AppSync | Write | |||
| DeleteApiKey | Grants permission to delete an API key | Write | |||
| DeleteDataSource | Grants permission to delete a data source | Write | |||
| DeleteDomainName | Grants permission to delete a custom domain name in AppSync | Write | |||
| DeleteFunction | Grants permission to delete a function | Write | |||
| DeleteGraphqlApi | Grants permission to delete a GraphQL Api. This will also clean up every AppSync resource below that API | Write | |||
| DeleteResolver | Grants permission to delete a resolver | Write | |||
| DeleteResourcePolicy [permission only] | Grants permission to remove a resource policy | Write | |||
| DeleteType | Grants permission to delete a type | Write | |||
| DisassociateApi | Grants permission to detach a GraphQL API to a custom domain name in AppSync | Write | |||
| DisassociateMergedGraphqlApi | Grants permission to remove an associated source API from a merged API identified by the source API | Write | |||
| DisassociateSourceGraphqlApi | Grants permission to remove an associated source API from a merged API identified by the merged API | Write | |||
| EvaluateCode | Grants permission to evaluate code with a runtime and context | Read | |||
| EvaluateMappingTemplate | Grants permission to evaluate template mapping | Read | |||
| FlushApiCache | Grants permission to flush an API cache in AppSync | Write | |||
| GetApiAssociation | Grants permission to read custom domain name - GraphQL API association details in AppSync | Read | |||
| GetApiCache | Grants permission to read information about an API cache in AppSync | Read | |||
| GetDataSource | Grants permission to retrieve a data source | Read | |||
| GetDomainName | Grants permission to read information about a custom domain name in AppSync | Read | |||
| GetFunction | Grants permission to retrieve a function | Read | |||
| GetGraphqlApi | Grants permission to retrieve a GraphQL API | Read | |||
| GetIntrospectionSchema | Grants permission to retrieve the introspection schema for a GraphQL API | Read | |||
| GetResolver | Grants permission to retrieve a resolver | Read | |||
| GetResourcePolicy [permission only] | Grants permission to read a resource policy | Read | |||
| GetSchemaCreationStatus | Grants permission to retrieve the current status of a schema creation operation | Read | |||
| GetSourceApiAssociation | Grants permission to read information about a merged API associated source API | Read | |||
| GetType | Grants permission to retrieve a type | Read | |||
| GraphQL | Grants permission to send a GraphQL query to a GraphQL API | Write | |||
| ListApiKeys | Grants permission to list the API keys for a given API | List | |||
| ListDataSources | Grants permission to list the data sources for a given API | List | |||
| ListDomainNames | Grants permission to enumerate custom domain names in AppSync | List | |||
| ListFunctions | Grants permission to list the functions for a given API | List | |||
| ListGraphqlApis | Grants permission to list GraphQL APIs | List | |||
| ListResolvers | Grants permission to list the resolvers for a given API and type | List | |||
| ListResolversByFunction | Grants permission to list the resolvers that are associated with a specific function | List | |||
| ListSourceApiAssociations | Grants permission to list source APIs associated to a given merged API | List | |||
| ListTagsForResource | Grants permission to list the tags for a resource | Read | |||
| ListTypes | Grants permission to list the types for a given API | List | |||
| ListTypesByAssociation | Grants permission to list the types for a given merged API and source API association | List | |||
| PutResourcePolicy [permission only] | Grants permission to set a resource policy | Write | |||
| SetWebACL | Grants permission to set a web ACL | Write | |||
| SourceGraphQL [permission only] | Grants permission to send a GraphQL query to a source API of a merged API | Write | |||
| StartSchemaCreation | Grants permission to add a new schema to your GraphQL API. This operation is asynchronous - GetSchemaCreationStatus can show when it has completed | Write | |||
| StartSchemaMerge | Grants permission to initiate a schema merge for a given merged API and associated source API | Write | |||
| TagResource | Grants permission to tag a resource | Tagging | |||
| UntagResource | Grants permission to untag a resource | Tagging | |||
| UpdateApiCache | Grants permission to update an API cache in AppSync | Write | |||
| UpdateApiKey | Grants permission to update an API key for a given API | Write | |||
| UpdateDataSource | Grants permission to update a data source | Write | |||
| UpdateDomainName | Grants permission to update a custom domain name in AppSync | Write | |||
| UpdateFunction | Grants permission to update an existing function | Write | |||
| UpdateGraphqlApi | Grants permission to update a GraphQL API | Write |
iam:CreateServiceLinkedRole |
||
| UpdateResolver | Grants permission to update a resolver | Write | |||
| UpdateSourceApiAssociation | Grants permission to update a merged API source API association | Write | |||
| UpdateType | Grants permission to update a type | Write |
Resource types defined by AWS AppSync
The following resource types are defined by this service and can be used in the
Resource
element of IAM permission policy statements. Each action in the
Actions table
identifies the resource types that can be specified with that action. A resource type can also define which condition keys you can include in a policy. These keys are displayed in the last column of the Resource types table. For details about the columns in the following table, see
Resource types table
.
| Resource types | ARN | Condition keys |
|---|---|---|
| datasource |
arn:$
{
Partition}:appsync:$
{
Region}:$
{
Account}:apis/$
{
GraphQLAPIId}/datasources/$
{
DatasourceName}
|
|
| domain |
arn:$
{
Partition}:appsync:$
{
Region}:$
{
Account}:domainnames/$
{
DomainName}
|
|
| graphqlapi |
arn:$
{
Partition}:appsync:$
{
Region}:$
{
Account}:apis/$
{
GraphQLAPIId}
|
|
| field |
arn:$
{
Partition}:appsync:$
{
Region}:$
{
Account}:apis/$
{
GraphQLAPIId}/types/$
{
TypeName}/fields/$
{
FieldName}
|
|
| type |
arn:$
{
Partition}:appsync:$
{
Region}:$
{
Account}:apis/$
{
GraphQLAPIId}/types/$
{
TypeName}
|
|
| function |
arn:$
{
Partition}:appsync:$
{
Region}:$
{
Account}:apis/$
{
GraphQLAPIId}/functions/$
{
FunctionId}
|
|
| sourceApiAssociation |
arn:$
{
Partition}:appsync:$
{
Region}:$
{
Account}:apis/$
{
MergedGraphQLAPIId}/sourceApiAssociations/$
{
Associationid}
|
|
| mergedApiAssociation |
arn:$
{
Partition}:appsync:$
{
Region}:$
{
Account}:apis/$
{
SourceGraphQLAPIId}/mergedApiAssociations/$
{
Associationid}
|
Condition keys for AWS AppSync
AWS AppSync defines the following condition keys that can be used in the
Condition
element of an IAM policy. You can use these keys to further refine the conditions under which the policy statement applies. For details about the columns in the following table, see
Condition keys table
.
To view the global condition keys that are available to all services, see Available global condition keys .
| Condition keys | Description | Type |
|---|---|---|
| appsync:Visibility | Filters access by the visibility of an API | String |
| aws:RequestTag/${TagKey} | Filters access by the tag key-value pairs in the request | String |
| aws:ResourceTag/${TagKey} | Filters access by the tag key-value pairs attached to the resource | String |
| aws:TagKeys | Filters access by the presence of tag keys in the request | ArrayOfString |


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Please go back or go to Home


Big news: The Privly Foundation recently gained non-profit status!
This update has numerous advances in the user interface of the Privly system including performance and user experience fixes. See below for a list of changes.
Version 0.3.7 (this version)
Version 0.3.6
Version 0.3.5
Version 0.3.4
Version 0.3.3
Version 0.3.2
Version 0.3.1
Version 0.3.0
Version 0.2.0
Version 0.1.1
Sean McGregor, Daniel Reichert, Jason Jacob
<EMAIL_ADDRESS>PO Box 79
Corvallis, OR 97339
Often when you are viewing a web page, you will want to know if that content has been added by the Privly extension. To allow you to differentiate Privly and non-Privly content, your extension shows a random sequence of colors, a "glyph," that will only be displayed when your mouse is over Privly-type content. Often the Privly extension will display additional information next to the glyph like verified identity information, security properties, and more. Keep an eye on this little popup and remember to commit your specific glyph to memory.