MVP Studio is all about connecting people with tech ideas with people who can help make those ideas a reality. Teamwork is where all the action is, so we desire to be a means of connections and provisions. Interns work with developers in the tech field to learn coding and software development. Our developers are experienced and up–to–date with the technology trends, therefore they would provide key insights into your ideas to start making awesome things happen.
You can rest assured that MVP Studio holds all information given completely confidential. We desire this to be where you can have fun and take part in seeing your ideas come to life. Clients will be required to attend a meeting at least once a week to help keep things moving. Communication is key to your project's success. And it is totally free to join! MVP Studio wants your project–making process to be the best it can be.
If you are interested in becoming a client, we would love for you to apply and to tell us about your idea(s). However, if you have any questions, please feel free to contact us and we'll help you out.


|
Some of My Precious Personality
|
|||||
|
LANGUAGES - ENGLISH - РУССКИЙ |
SKILLS - NEGOTIATING - COUNTING - REMODELING - WRITING - ACING - SINGING - REPORTING - INCORPORATING - SELLING - ADVERTISING - HOSTING - COMPETING - TWITTING - PARENTING - GAMBLING - DATING - SUING - ORGANIZING - FUNDING - DEBATING - ADVISING -PRESBYTERIANING |
CHARACTER - ADJUSTED - CALM - AMBITIOUS - COMPETITIVE - SOCIABLE - DIRECT - HOSTILE - CARELESS - IGNORANT - INQUISITIVE - BOLD - CONFIDENT - MISCHIEVOUS - CHARMING - INTERESTING - DARING - COLORFUL - LAZY - SELF-PROMOTING |
LIKES - BILLIONS - FILET-O-FISH - BIG MAC - CHINA - QUARTER - POUNDER DIET - COKE TARIFFS - BIBLE - ART OF DEAL - PATRIOTS - BEAUTIFUL MODELS - GOLD - REAL ESTATE - WAR - PROTESTERS - CONSTRUCTION - RUSSIANS - BANKS - SPORTS - WOMEN - MEXICO - RED HAT - KOREANS - GOLF - EVERYBODY [1] [2] |
DISLIKES - DOGS - MEDIA - CNN - GERMANS - MEXICANS - IMMIGRANTS - SHITHOLE COUNTRIES - UNATTRACTIVE WOMEN - BLACKS - SUSHI - HOT-DOGS - ALCOHOL - FRUIT - CHRISTMAS - PARTIES - LIBERALS |
WANTS - BE HEALTHY - GROW A TREE - BUILD TRUMP AIRPORT - HAVE A TASTE - DO A HARLEM - SHAKE IN CONGRESS TO GOLF MORE OFTEN - WIN ALL THE COURT SUITS |


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The following json files contain metadata that helps install specific versions of FIAAS on a Kubernetes cluster and keep it up to date. More info can be found on github.
Latest version
Download »
Stable version


| Mobile <= 480px |
Mobile <= 480px |
Tablet <= 1024px |
Tablet <= 1024px |
Desktop > 1024px |
Desktop > 1024px |
| Small <= 768px |
Small <= 768px |
Medium <= 1280px |
Medium <= 1280px |
Large > 1280px |
Large > 1280px |
| Landscape |
Landscape |
Portrait |
Portrait |
|
| Print |
Print |
Retina |
Retina |
|


Gearpump Basic Concepts
System timestamp and Application timestamp
System timestamp denotes the time of backend cluster system. Application timestamp denotes the time at which message is generated. For example, for IOT edge device, the timestamp at which field sensor device creates a message is type of application timestamp, while the timestamp at which that message get received by the backend is type of system time.
Master, and Worker
Gearpump follow master slave architecture. Every cluster contains one or more Master node, and several worker nodes. Worker node is responsible to manage local resources on single machine, and Master node is responsible to manage global resources of the whole cluster.
Application
Application is what we want to parallel and run on the cluster. There are different application types, for example MapReduce application and streaming application are different application types. Gearpump natively supports Streaming Application types, it also contains several templates to help user to create custom application types, like distributedShell.
AppMaster and Executor
In runtime, every application instance is represented by single AppMaster and a list of Executors. AppMaster represent the command and control center of the Application instance, it communicate with user, master, worker, and executor to get the job done. Each executor is a parallel unit for distributed application. Typically AppMaster and Executor will be started as JVM processes on worker nodes.
Application Submission Flow
When user submits an application to Master, Master will first find an available worker to start the AppMaster. After AppMaster is started, AppMaster will request Master for more resources (worker) to start executors. The Executor now is only an empty container, after the executors are started, the AppMaster will then distribute real computation tasks to the executor and run them in parallel way.
To submit an application, a Gearpump client specifies a computation defined within a DAG and submits this to an active master. The SubmitApplication message is sent to the Master who then forwards this to an AppManager.
Figure: User Submit Application
The AppManager locates an available worker and launches an AppMaster in a sub-process JVM of the worker. The AppMaster will then negotiate with the Master for Resource allocation in order to distribute the DAG as defined within the Application. The allocated workers will then launch Executors (new JVMs).
Figure: Launch Executors and Tasks
Streaming Topology, Processor, and Task
For streaming application type, each application contains a topology, which is a DAG (directed acyclic graph) to describe the data flow. Each node in the DAG is a processor. For example, for word count it contains two processors, Split and Sum. The Split processor splits a line to a list of words, then the Sum processor summarize the frequency of each word. An application is a DAG of processors. Each processor handles messages.
Figure: Processor DAG
Streaming Task and Partitioner
For streaming application type, Task is the minimum unit of parallelism. In runtime, each Processor is paralleled to a list of tasks, with different tasks running in different executor. You can define Partitioner to denote the data shuffling rule between upstream processor tasks and downstream processor tasks.
Figure: Task Data Shuffling


Diagram from blog post HERE
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It has always been pleasure assisting you! Register quickly and we will let you know the adventure spots.
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{% extends "base.html" %} {% block title %}start{% endblock %} {% block containercontent %}
We've generated the OAuth request token ({{ oauth_token }}
) and are ready for you to authorize twauth-web!
Just click the button below, you'll be taken to Twitter to login (if you aren't already) and to grant access to
twauth-web. Afterward you'll be redirected back to us.
What did we just do?
We called {{ request_token_url }} with our application API key and API secret to get a temporary OAuth request token. We passed our desired callback URL to this method so that Twitter knows where to redirect you after you authorize our app. Note that we had previously already added the callback URL value to the whitelist for our app on the Twitter developer portal.


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--- title: Project Lead company: Value Connect location: Guelph, Ontario dates: Aug 2016 - May 2017 category: work projects: - valueconnect technologies: - Java - Spring - Angular 1.5 - JHipster - MySql - QueryDSL - MapStruct - LESS - AngularJS Material - Gulp.js - Gradle - Beanstream - Google Maps API (Static Maps, Distance Matrix, Geocoding) - BitBucket - JIRA - Jenkins ---
Value Connect is a property appraisal management company, harnessing technology to facilitate speedy turnaround times and build advanced, consistent reports.
As team lead on this project, responsibilities included


class
RGameObjectA game object. Main class responsible for player interactions.
Contents
Base classes
- class RMasterComponent
- A master component. Serves as parent class for all components.
Public static variables
- static std::unordered_map<std::string, RGameObject*> InstantiatedGameObjects
- The singular array that tracks all objects that were instantiated of this class.
Constructors, destructors, conversion operators
- ~RGameObject()
- Destructor.
- RGameObject() protected
- Protected constructor. Only used to create groups.
Public functions
- void begin() virtual
- Called by the engine once before the level starts to run.
- void tick() override
- Tick function.
-
void onHit(RGameObject* hammer,
RGameObject* nail,
const Awerere::
ACollideData& collisionData) virtual - User defined function called whenever a hit event is generated.
- void enableTick()
- Set this object for tick by update function.
- auto add(RGameObject* gameObject) -> RGameObject&
- Adds a game object as the child of this object.
- void gameobject_ok(var data = NULL)
- Checks the state of game object.
Public variables
- std::string m_Name
- Name of this game object.
- RLevel* m_ParentLevel
- The level context during which this object lives.
-
GraphicComponents::
RSprite* m_Sprite - Sprite used for the rendering process.
-
Awerere::
APhysicsObject* m_PhysicsObject - Physics object that handles collisions/response.
-
RML::
Matrix4 m_TransformationMatrix - The transform used to place the children of this game object.
- std::vector<RGameObject*> m_Children
- Vector array of child objects.
- bool m_ThisTicks
- Whether this gameobject ticks.
- bool m_UsesTexture
- Whether this gameobject uses a texture.
- bool m_IsGroup
- Whether this gameobject is a group of other child gameobjects.
- bool m_HasPhysics
- Whether this gameobject obeys physics and responds to collisions.
- bool m_GeneratesHit
- Whether this gameobject generates hit events.
Function documentation
void Rubeus:: RGameObject:: tick() override
Tick function.
void Rubeus:: RGameObject:: onHit(RGameObject* hammer,
RGameObject* nail,
const Awerere:: ACollideData& collisionData) virtual
User defined function called whenever a hit event is generated.
| Parameters | |
|---|---|
| hammer | The hitting object. |
| nail | The object getting hit |
| collisionData | Details of the collision |
RGameObject& Rubeus:: RGameObject:: add(RGameObject* gameObject)
Adds a game object as the child of this object.
| Parameters | |
|---|---|
| gameObject | The game object to be added to this group. |
| Returns | Reference to this group object. Allows chaining add() calls. E.g. object.add(r1).add(r2); |
void Rubeus:: RGameObject:: gameobject_ok(var data = NULL)
Checks the state of game object.
| Parameters | |
|---|---|
| data | NULL |


Global Functions in Namespace cppu
in Sourcefile component_context.hxx
- createComponentContext
- ::com::sun::star::uno::Reference< ::com::sun::star::uno::XComponentContext > createComponentContext(
| const ContextEntry_Init * |
pEntries, |
| sal_Int32 |
nEntries, |
| const ::com::sun::star::uno::Reference< ::com::sun::star::uno::XComponentContext > & |
xDelegate = :: com :: sun :: star :: uno :: Reference < :: com :: sun :: star :: uno :: XComponentContext > ) throw(); |
| virtual |
abstract |
const |
volatile |
template |
static |
inline |
C-linkage |
| NO |
NO |
NO |
NO |
NO |
NO |
NO |
NO |
- Summary
Creates a component context with the given entries.
- Parameters
| pEntries |
array of entries
|
| nEntries |
number of entries
|
| xDelegate |
delegation to further context, if value was not found
|
- Return
new context object
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Copyright © 2012, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


1 reference to StreamingDequeue
Microsoft.PowerShell.Commands.Utility (1)
commands\utility\Select-Object.cs (1)
716
PSObject streamingInputObject = _selectObjectQueue.
StreamingDequeue
();


amazon-chime-sdk
/
com.amazonaws.services.chime.sdk.meetings.audiovideo
/
VolumeLevel
/
High
High
High
The attendee is speaking at high volume


Sean Quinn
Postdoc at UCLA
Welcome! I’m an experimental astro-particle physicist working on the General Antiparticle Spectrometer cosmic ray experiment. I joined the GAPS group at UCLA in 2018 and am involved in designing and building the time of flight system, serving as the main electronics lead.
Research interests
I am broadly interested in the direct detection of slow cosmic rays (a compelling and clean channel for indirect dark matter searches) and indirect detection of the ultra-high energy cosmic rays ( eV) as a test of the standard model at the high energy frontier.
More specifically: (1) the design and construction of particle detectors with a focus on data acquisition systems, electronics and associated simulations for cosmic ray physics (2) calibration and comparative studies of surface detectors used by super giant arrays studying UHECRs and (3) the propagation of ultra-high energy cosmic rays through the cosmic environment and deflections by astrophysical magnetic fields.
Other interests
I enjoy traveling and the outdoors. Since moving to California I’ve tried to take advantage of the many beautiful trails. Here are a few of my hikes: Gaia
PAB 3-357
475 Portola Plaza
Los Angeles, CA 90095
news
| Aug 6, 2020 | Check out my talk on GAPS for the virtual ICHEP2020 at https://youtu.be/agaFUFpGEGA?t=9161 |
|---|


Pin a fixed-height footer to the bottom of the viewport in desktop browsers with this custom
HTML and CSS. A fixed navbar has been added within #wrap
with padding-top: 60px;
on the .container
.
Back to the default sticky footer minus the navbar.


Anyone that uses the internet regularly knows that storing online information can be messy. Keeping track of bookmarked pages, saved photos, notes, and messages can waste time and cause the loss of important information.
Blocbox is an SaaS application that provides a solution by offering users a simple way to store links, photos, and notes all in one place. Not only can users easily store online information, but they can also share and collaborate with others as needed.
User Surveys provide information about preferred devices, internet browsing patterns, and potential competitors. Drawing characteristics from survey participants contributed to the development of user personas for potential clients.
This information helped me create features and wireframes that best served my target audience.
Because Blocbox is an application intended to increase the efficiency of storing content electronically, the appearance of the website needed to be as clear and simple as possible. User flow charts illustrated the exact direction each page would move in, eliminating the clutter of any unnecessary features while ensuring that no vital functions went missing.
Storage websites can sometimes appear sterile and lack communication. I wanted Blocbox to be both visually stimulating and direct, so I made two style guide variations to test.
Because Usability Testing is essential throughout the design process, I didn’t hesitate to send out multiple tests including the prototypes of my designs.
Initially, the teal and orange color scheme were preferred, but user feedback stated that it was too cartoonish. I corrected this by changing the color scheme from teal and orange to green and orange, where green better reinforces a more organized, secure feeling.
Results from user testing told me that my first home page design was confusing; the carousel reviews weren’t the most intuitive choice, and the color pattern in the pricing section was slightly too confusing for users.
The revised version of the home page included a simplified reviews section and added a third color to the pricing section.
The design process, especially implementing user research, showed me how less is more. Simplicity allows for the clearest communication, the most beautiful design, and the highest level of functionality.


Knoxville child molester convicted of raping 7-year-old
KNOXVILLE, Tenn. (WVLT) — According to a release from the Knox County District Attorney, a Knoxville man has been convicting of raping a 7-year-old child.
The DA's Office said a jury found James Theodore Menard, 38, guilty-as-charged and convicted him of Rape of a Child, two counts of Aggravated Sexual Battery, and Distribution of Unlawful Material to Minors.
Menard is being held without bond awaiting sentencing on January 9.
Investigators explained during a trial that Menard sexually assaulted his girlfriend's child while they lived at Montgomery Village. The child was seven and eight during the time they were being abused.
The victim told a teacher about the abuse which sparked the investigation.
KPD officers learned the abuse had taken place over several years, but the victim revealed three specific incidents that t resulted in the Rape of a Child and two Aggravated Sexual Battery counts. As a way to groom the victim for the abuse, Menard also showed the victim pornographic materials, resulting in the conviction for Distribution of Unlawful Material to Minors.
“One of the most important roles we have in this Office is to protect children from predators,” said DA Charme Allen. “Children in this community are safer because of this conviction.”
Menard could face up to 40 years in prison without the possibility of parole. Prosecutors will seek an enhanced sentence due to the nature of the offense and Menard’s multiple prior convictions out of several states.


Non-ergodicity in a simple coin game
Based on a seminal talk by Ole Peters, I illustrate the fundamental difference between an ergodic and non-ergodic process using a simple coin game.
(If you download this notebook, you can use the interactive widgets to explore this yourself)
Why is this important/exciting?
Ole Peters revisits the use of the expectation operator in economics. He does this by applying novel insights from dynamics of stochastic processes. The question he is trying to answer is fundamentally the same that motivated the St. Petersbrug Paradox. This is closely related to Daniel Bernoullis formulation of expected utility that lies as the foundation of all modern macro and micro economics. In the following I illustrate the essence of Peters talk using a simple coin game.
Firstly, lets outline the game dynamics.
Dynamics:
- At time you start of with wealth .
- At each time step a fair coin is flipped and lands either heads or tails.
- For heads wealth increases with 50% e.g.
- For tails wealth decreases by 40% e.g.
The question we’re trying to answer: Is this gamble worth taking?
Simulations:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
sns.set(style="white", palette="muted", color_codes=True)
%matplotlib inline
def multiplicativeW( T, N, W, E, A, mu, phi ):
w0 = 1
c1 = 0.5
c2 = -0.4
j = N
k = T
size = (N,T)
coinToss = np.zeros(size)
W_ = np.zeros(size)
E_ = np.zeros(T)
A_ = np.zeros(T)
mu_ = np.zeros(T)
phi_ = np.zeros(size)
np.random.seed(1);
for j in range(N):
for k in range(T):
if k == 0:
W_[j,k] = w0
E_[k] = w0
A_[k] = w0
mu_[k] = w0
else:
# coin dynamics
coinToss[j,k] = np.random.choice([c1,c2])
# wealth dynamics
W_[j,k] = W_[j,k-1] * (1+coinToss[j,k])
# theoretical constants
E_[k] = w0 + np.power(1+((0.5*c1)+(0.5*c2)),[k])
A_[k] = np.power(np.exp((np.log(1+c1)+np.log(1+c2))/2),[k])
# emperical mean
mu_[k] = np.mean(W_[:,k])
fig = plt.figure(figsize=(12,5))
plt.yscale('log', nonposy='clip')
NUM_COLORS = N
cm = sns.cubehelix_palette(8, start=.5, rot=-.75,as_cmap = True)
ax1 = fig.add_subplot(1,1,1)
ax1.set_color_cycle([cm(1.*i/NUM_COLORS) for i in range(NUM_COLORS)])
if W is True:
for j in range(N):
ax1.plot(W_[j],linewidth = 1)
if E is True:
ax1.plot(E_,linewidth = 2,color='k',label = (r'$\mathbb{E}$'))
plt.legend(loc='upper left', fontsize=14)
if A is True:
ax1.plot(A_,linewidth = 2,color='r', label = (r'$\mathcal{A}$'))
plt.legend(loc='upper left', fontsize=14)
if mu is True:
ax1.plot(mu_,linewidth = 2,color='r', label = (r'$\mu$'))
plt.legend(loc='upper left', fontsize=14)
Assume you’re playing the coin game once every minute for an hour. That is, you iterate the specified dynamics 60 times and plot the resulting data (notice the y-axis is a log scale).
interact(multiplicativeW, T = 60, N = 1, W = True, E = False, A = False, mu = False, phi = False)
This seems quite noisy and difficult to deduct any real trend. Perhaps this one trajectory was just a bit unfortunate. To convince yourself, repeat the game 100 times and plot the result.
interact(multiplicativeW, T = 60, N = 100, W = True, E = False, A = False, mu = False, phi = False)
Still not getting much smarter. Looks like half on the trajectories are increasing, while the other half are decreasing - but it remains difficult to tell if this game is really beneficial.
Perhaps removing some noise by considering the average of the processes would be helpful.
interact(multiplicativeW, T = 60, N = 100, W = True, E = False, A = False, mu = True, phi = False)
Which looks very flat at at around zero.
Increase the number of trajectories to . Hopefully this will give us a more conclusive graphical result.
interact(multiplicativeW, T = 60, N = 10000, W = False, E = False, A = False, mu = True, phi = False)
It seems like the empirical average steadily increases. If you feel like removing more noise, you can increase (however this script is quite slow due to all the for-loops).
As we have explicit knowledge of the dynamics, we can do much better than simulations. We can simply calculate the mean or expectation operator over the trajectories. Formally, the expectation operator is the large-N limit of the empirical mean
where denotes the average over N, as this goes to infinity. We denote this .
Let’s calculate this. By using our knowledge that as the probability of a fair coin asymptotes to the same ratio (here 0.5). This means we can calculate as which is a number larger than one, reflecting positive growth over the ensemble of trajectories.
Below is a graphical result of the expectation operator and the empirical mean ( for a finite N).
interact(multiplicativeW, T = 60, N = 10000, W = False, E = True, A = False, mu = True, phi = False)
The graph supports our theoretical analysis: The empirical mean seems to asymptote to the theoretical mean - which grows exponentially towards infinity (recall that y-scale is in logs).
The conclusion is simple: Since this process has a positive expectation it will grow exponentially towards infinity, which means that you (or anyone rational) should be willing to pay any ticket price for playing this game.
The time-perspective
Before you go off and buy your ticket, let’s plot just one ‘person’ () playing the game for a long time () and contrast this to the theoretical mean.
interact(multiplicativeW, T = 10000, N = 1, W = True, E = True, A = False, mu = False, phi = False)
This single trajectory tells quite a different story from what we concluded above. While the expectation operator goes to (which is a very, very large number), the realised trajectory ends up at .
Let’s try to convince ourselves that this was just one unlucky trajectory. Again, we wish to remove noise using the same strategy of increasing the number of ‘players’ that are gambling over a very long time.
interact(multiplicativeW, T = 10000, N = 100, W = True, E = True, A = False, mu = False, phi = False)
Immediatly there seems to be a fundamental difference between the theoretical prediction and the hundred realised trajectories (you can keep adding more trajectories by increasing or more time by increasing - it will only make you more certain of the above).
This difference can be explained by considering a colloquial definition of ergodicity:
A process is non-ergodic if the time-average does not equal the ensemble-average.
Lets consider the definition of the finite-time time-average
where denotes the average of process over time . Similar to the above, taking the time-limit of this gives us the theoretical time-average
which we denote .
Plotting both , and some data to see which theoretical prediction suits the simulated data the best (you probably already know the answer).
interact(multiplicativeW, T = 10000, N = 100, W = True, E = True, A = True, mu = False, phi = False)
So what is happening here? Well, hopefully you’ve just realised that a process that grows multiplicatively - just like - is non-ergodic.
This means that the ensemble average, mean or expectation operator does not yield the same as the time-average, which is what we’ve shown in the figures above.
There’s much more to this story, and if you find it compelling, I suggest you see the video in the introduction, or read the follow papers:


Running Verilog code on Linux/Mac
Step by step instructions of to setup and install tools to run and visualize verilog HDL designs on *nix systems.
Install
Note: The steps below have been tested on Ubuntu 16.04 and MacOS 10.14
1. Compile from source on Linux/Mac or in Cygwin on Windows
You will need make, autoconf, gcc, g++, flex, bison
to compile (and maybe more depending on your
system).
Clone the git repo.
$ git clone https://github.com/steveicarus/iverilog.git
cd into the directory
$ cd iverilog
Build configuration files
$ sh autoconf.sh
If this gives autoconf.sh: line 10: autoconf: command not found
then do this on MacOS:
$ brew install autoconf automake libtool
    to install the needed packages
and on Ubuntu do:
$ sudo apt-get install autoconf
Successful run should be like:
Autoconf in root...
Precompiling lexor_keyword.gperf
Precompiling vhdlpp/lexor_keyword.gperf
Configure
$ ./configure             # for default settings - installs to /usr/local/bin
$ ./configure --prefix=<your directory>   #installs to specific directory provided
Compile the source
$ make
This command will require gcc, g++, bison and flex.
You can install them using Homebrew or apt-get on Mac and Ubuntu respectively.
Get into superuser mode and install
$ sudo su
$ make install
2. Install on MacOS using Homebrew
$ brew install icarus-verilog
(Optional) Install iverilog waveform dumpfile viewer - gtkwave
$ sudo port -v install gtkwave
If gtkwave installation gives
Error: Failed to configure gtk-osx-application-common-gtk2: gtk2 +quartz not installed.
Do:
$ sudo port install gtk2 +quartz
3. Install on Ubuntu using aptitude
$ sudo add-apt-repository ppa:team-electronics/ppa
$ sudo apt-get update
$ sudo apt-get install verilog
$ sudo apt-get install gtkwave
    (Optional)
Note: Windows binaries are avialable at http://bleyer.org/icarus/
For more details on installation, visit https://iverilog.fandom.com/wiki/Installation_Guide
Run
Example 1
Save this verilog code as hello.v
Compile using
$ iverilog -o hello hello.v
Run the compiled executable
$ vvp hello
Hello World!
Example 2
Save the code below as alu.v
Save the code below as alu_tb.v
Compile using
$ iverilog -o alu alu_tb.v alu.v
$ vvp alu +a=3 +b=2 +s=0
y= 5
Here s=0 selects addition operation which is performed over a=3 and b=2.
So the output y is 3+2=5.
Show timing diagram using gtkwave (Optional)
Save this code as counter.v
Save this code as counter_tb.v
Compile using
$ iverilog -o counter counter_tb.v counter.v
Run the executable
$ vvp counter
Show the timing diagram
$ gtkwave test.vcd &
Expand 'test' in the left sidebar and click on 'c1'. Then drag and drop 'out' reg on the signal viewing area.
Output


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a JavaScript performance companion
This tool helps you analyze V8 internal representation and assembly online
1 Enter a JavaScript file
3 See the result directly imported in IRHydra2
3a Browse intermediate representation / disassembly
3b See graph view
Analyze code
(IRHydra2 for V8)
see all
V8 versions


SDM 2018, San Diego
Han Xiao
![](figs/hotel.jpg)
![](figs/grass.jpg)
![](figs/plant1.png)
![](figs/plant2.jpg)
## sessions - applied data science - health informatics - personalization - classification - matrix factorization and topic models - sequence data - clustering - networks - social-media - graphs - novel learning methods - time series data
### selected papers
![](figs/graphdist-1.png)
### metric property vs tractability - CKS / chemical distance: metric but *non-tractable* - heuristics: tractable but *non-metric* - metric usefulness: nearest neighbor search, clustering, etc
### motivation - distances that are both metric and tractable - computing $P$ between $A \text{ and } B$ is not the primary goal - via **relaxation** of the problem
### problem relaxation ![](figs/graphdist-relaxation.png)
### contribution 1: metric ![](figs/graphdist-contrib-1.png) ![](figs/graphdist-contrib-2.png)
### contribution 1: tractbility - (ii) convex optimization - (iii) non-convex but reduces to spectral decomposition
### contribution 2: include node attributes - important in practice - modeled via hard/soft constraints - hard constraint: e.g, male to male, female to female - soft constraint: e.g, user profile, vector distance
### a glimpse of experiment..
![](figs/graphdist-experiment.png)
![](figs/wp-paper.png) - [paper](https://www.francois-petitjean.com/Research/Petitjean2018-SDM-learn-warp-window.pdf) - [supp](https://www.francois-petitjean.com/Research/Petitjean2018-SDM-learn-warp-window-sup.pdf)
### motivation - consider time-series classification problem - nearest neighbour algorithm (NN) using DTW is a leading algorithm for the classification task - Dynamic Time Warping (DTW): popular similarity measure - involves learning the warping window (later) - learning warping window is time-consuming - naively, $O(N^2 L^3)$ (later) - **goal: speed up?**
### DTW basics - distance measure between two time series by aligning them - solvable by dynamic programming $O(L^2)$ - warping path: $p=[w_1, \ldots, w_k]$, where $w=(i, j)$ ![](figs/wp-dtw.png)
### warping window (WW) - limit the search within window $[-w, w]$ - speed up to $O(wL)$ - also avoid pathelogical alignment ![](figs/wp-ww.png)
### learning WW - why? has a strong influence on classification accuracy - how to learn? hyper-parameter tuning problem - x-fold cross validation (CV) - in this paper, leave-one-out CV (LOO-CV)
### naive CV ![](figs/wp-naive-cv.png)
### main theorems ![](figs/wp-theorem-3.1.png) ![](figs/wp-theorem-3.2.png)
### algorithm - main task: fill in $N \times L$ NN table ![](figs/wp-nn-table.png)
### algorithm cont'd ![](figs/wp-algo.png)
![](figs/eeg-paper.png)
### motivation - consider EEG classification (brain disease diagnosis) - performance degraded by *invalid EEGs* - environmental noise - non-target activity, etc - question: how to select relevant EEGs?
### formulation - build a EEG similarity graph - node: one EEG (time series, with node weight) - edge weight: modifed version of Frechet distance - problem: extract $m$ disjoint cliques with maximum weight - (unselected nodes/EEGs are removed from classification)
![](figs/leader-paper.png)
### take-home message
- at least two "San Jose"s (SJO vs SJC) in the world - be careful when booking flights


AMAZING PEOPLE ARE MAKING AMAZING DESIGNS IN A FUN ENVIRONMENT
When you work with us, your part of commited collaborators.All aspects of our software development process are intergrated, interactive
and agile.
Our aproach unifies design, development and product management to create exceptional products.
We start each project by understanding your business goals, the user's needs and the product requirements.
Then we help translate into effective and elogant solutions.
Before we sign a contract or write a line of code our team will spend a necessary time needed to understand the product vision
The outcome of this scoping session is a propasal or high-level statementwork
Based on your requirements, we can provide a complete offering or just the pieces you need


BlackholeList
struct BlackholeList
Provides functions for downloading and creating JSON ContentBlocker lists
-
Stores metadata associated with a blacklist
See moreDeclaration
Swift
struct Blacklist
-
Convenience var for accessing group.com.refabricants.adscrubber
Declaration
Swift
private static let sharedContainer = NSUserDefaults.init(suiteName: "group.com.refabricants.adscrubber")
-
Convenience var for accessing the default container
Declaration
Swift
private static let defaultContainer = NSUserDefaults.standardUserDefaults()
-
Metadata for the bundled ContentBlocker blocklist
Declaration
Swift
static var preloadedBlacklist = Blacklist(withListName: "preloaded", url: "https://raw.githubusercontent.com/StevenBlack/hosts/master/hosts", fileType: "built-in", entryCount: "27167", etag: "9011c48902c695e9a92b259a859f971f7a9a5f75")
-
Metadata for the currently loaded custom blocklist
Declaration
Swift
static var currentBlacklist = Blacklist(withListName: "current")
-
Metadata for the blacklist stored in the TextView
Declaration
Swift
static var displayedBlacklist = Blacklist(withListName: "displayed")
-
Metadata for the blacklist stored in the TextView but not yet validated or loaded
Declaration
Swift
static var candidateBlacklist = Blacklist(withListName: "candidate")
-
Reads the value in the shared storage container that determines whether or not a blacklist should be used. Returns the value or, if no value is found in shared storage, sets it to false and returns false.
Returns
A boolean value indicating whether or not a custom blacklist should be usedDeclaration
Swift
static func getIsUseCustomBlocklistOn() -> Bool
Return Value
A boolean value indicating whether or not a custom blacklist should be used
-
Sets the value in the shared storage container that determines whether or not a custom blacklist shouldbe used
- Parameters:
- value: True if a custom blacklist should be used, false otherwise
Declaration
Swift
static func setIsUseCustomBlocklistOn(value: Bool)
Parameters
value
True if a custom blacklist should be used, false otherwise
- Parameters:
-
Reads the value in the shared storage container that contains the blacklist file type. Returns the file type (i.e.
built-in
,JSON
, orhosts
or, if no value is found in shared storage, sets the value tonone.
Returns
The file type of the currently-loaded blacklist, ornone
Declaration
Swift
static func getDownloadedBlacklistType() -> String
Return Value
The file type of the currently-loaded blacklist, or
none
-
Sets the value in the shared storage container that contains the blacklist file type.
- Parameters:
- value: The file type of the blacklist (i.e.
built-in
,JSON
, orhosts
)
- value: The file type of the blacklist (i.e.
Declaration
Swift
static func setDownloadedBlacklistType(value: String)
Parameters
value
The file type of the blacklist (i.e.
built-in
,JSON
, orhosts
) - Parameters:
-
Reads the value in the shared storage container that indicates whether a new blacklist is in the process of being downloaded. Returns the value or, if no value is found in shared storage, sets it to false and returns false.
Returns
A boolean value indicating whether or not a blacklist is in the process of being downloadedDeclaration
Swift
static func getIsReloading() -> Bool
Return Value
A boolean value indicating whether or not a blacklist is in the process of being downloaded
-
Sets the value in the shared storage container that indicates whether or not a new blacklist is in the process of being downloaded.
- Parameters:
- value: A boolean value indicating whether or not a blacklist is in the process of being downloaded
Declaration
Swift
static func setIsReloading(value: Bool)
Parameters
value
A boolean value indicating whether or not a blacklist is in the process of being downloaded
- Parameters:
-
Reads the value in the shared storage container that indicates whether the
wildcardBlockerList.json
file should be loaded instead of the defaultblockerList.json
file.Returns
A boolean value indicating whether or not thewildcardBlockerList.json
file should be loadedDeclaration
Swift
static func getIsBlockingSubdomains() -> Bool
Return Value
A boolean value indicating whether or not the
wildcardBlockerList.json
file should be loaded -
Sets the value in the shared storage container that indicates whether the
wildcardBlockerList.json
file should be loaded instead of the defaultblockerList.json
file.- Parameters:
- value: A boolean value indicating whether or not the
wildcardBlockerList.json
file should be loaded
- value: A boolean value indicating whether or not the
Declaration
Swift
static func setIsBlockingSubdomains(value: Bool)
Parameters
value
A boolean value indicating whether or not the
wildcardBlockerList.json
file should be loaded - Parameters:
-
Fetches information on the URL of a blacklist and determines whether or not the blacklist can and should be downloaded. If the file can be downloaded,
should be downloaded
is determined by comparing the remote file’s etag to the etag in the metadata recorded when the current file was loaded.- Parameters:
- blacklistURL: The URL of the blacklist to be validated
Declaration
Swift
static func validateURL(blacklistURL:NSURL, completion:((updateStatus: ListUpdateStatus) -> ())?)
Parameters
blacklistURL
The URL of the blacklist to be validated
- Parameters:
-
Downloads a remote blacklist
Parameters:
- blacklistURL: The URL of the blacklist to be downloaded
Throws
ListUpdateStatus
- .ErrorDownloading: The download failed
- .ErrorSavingToLocalFilesystem: Saving the downloaded file failed
Returns
The locally-saved file
Declaration
Swift
static func downloadBlacklist(blacklistURL: NSURL) throws -> NSURL?
Parameters
blacklistURL
The URL of the blacklist to be downloaded
Return Value
The locally-saved file
-
If the downloaed blacklist is a JSON file, replaces the exiting
blockerList.json
file in the shared container with the downloaded file.If the downloaded blacklist is a hosts file, parses the downloaded file and uses the entries to create both a
blockerList.json
file and awildcardBlockerList.json
file in the shared container. The latter list adds a wildcard prefix to every rule to block subdomains.Parameters:
- blacklist: The URL of the downloaded blacklist
Returns
Returns:
- updateStatus:
- .UpdateSuccessful: The operation was successful
- .InvalidJSON: A JSON file was detected, but the file does not conform to expected Content Blocker syntax
- .ErrorSavingToLocalFilesystem: An error occurred moving the file to the shared container
- .ErrorParsingFile: A hosts file was detected, but the an error occurred while parsing it
- .TooManyEntries: There are more than 50,000 rule entries
- blacklistFileType:
JSON
orhosts,
depending upon which file type was detected - numberOfEntries: The number of entries detected in the blacklist
Declaration
Swift
static func createBlockerListJSON(blacklist: NSURL) -> (updateStatus: ListUpdateStatus, blacklistFileType: String?, numberOfEntries: Int?)
Parameters
blacklist
The URL of the downloaded blacklist
Return Value
-
Ignoring case, determines whether or not an array contains a particular string
Parameters:
- elements: An arrray of strings
- text: The string to search for
Returns
true if text occurs in elements, false otherwise
Declaration
Swift
static func contains(elements: Array<String>, text: String) -> Bool
Parameters
elements
An arrray of strings
text
The string to search for
Return Value
if occurs in , otherwise


Welcome to fdasrsf’s documentation!
A python package for functional data analysis using the square root slope framework and curves using the square root velocity framework which performs pair-wise and group-wise alignment as well as modeling using functional component analysis and regression.
- User Guide
- Functional Alignment
- Functional Principal Component Analysis
- Elastic Functional Boxplots
- Functional Principal Least Squares
- Elastic Regression
- Elastic Principal Component Regression
- Elastic GLM Regression
- Elastic Functional Tolerance Bounds
- Elastic Functional Clustering
- Elastic Image Warping
- Curve Registration
- SRVF Geodesic Computation
- Utility Functions
- Curve Functions
- UMAP EFDA Metrics
References
Tucker, J. D. 2014, Functional Component Analysis and Regression using Elastic Methods. Ph.D. Thesis, Florida State University.
Robinson, D. T. 2012, Function Data Analysis and Partial Shape Matching in the Square Root Velocity Framework. Ph.D. Thesis, Florida State University.
Huang, W. 2014, Optimization Algorithms on Riemannian Manifolds with Applications. Ph.D. Thesis, Florida State University.
Srivastava, A., Wu, W., Kurtek, S., Klassen, E. and Marron, J. S. (2011). Registration of Functional Data Using Fisher-Rao Metric. arXiv:1103.3817v2 [math.ST].
Tucker, J. D., Wu, W. and Srivastava, A. (2013). Generative models for functional data using phase and amplitude separation. Computational Statistics and Data Analysis 61, 50-66.
J. D. Tucker, W. Wu, and A. Srivastava, “Phase-Amplitude Separation of Proteomics Data Using Extended Fisher-Rao Metric,” Electronic Journal of Statistics, Vol 8, no. 2. pp 1724-1733, 2014.
J. D. Tucker, W. Wu, and A. Srivastava, “Analysis of signals under compositional noise With applications to SONAR data,” IEEE Journal of Oceanic Engineering, Vol 29, no. 2. pp 318-330, Apr 2014.
Srivastava, A., Klassen, E., Joshi, S., Jermyn, I., (2011). Shape analysis of elastic curves in euclidean spaces. Pattern Analysis and Machine Intelligence, IEEE Transactions on 33 (7), 1415-1428.
S. Kurtek, A. Srivastava, and W. Wu. Signal estimation under random time-warpings and nonlinear signal alignment. In Proceedings of Neural Information Processing Systems (NIPS), 2011.
Wen Huang, Kyle A. Gallivan, Anuj Srivastava, Pierre-Antoine Absil. “Riemannian Optimization for Elastic Shape Analysis”, Short version, The 21st International Symposium on Mathematical Theory of Networks and Systems (MTNS 2014).
Cheng, W., Dryden, I. L., and Huang, X. (2016). Bayesian registration of functions and curves. Bayesian Analysis, 11(2), 447-475.
W. Xie, S. Kurtek, K. Bharath, and Y. Sun, A geometric approach to visualization of variability in functional data, Journal of American Statistical Association 112 (2017), pp. 979-993.
Lu, Y., R. Herbei, and S. Kurtek, 2017: Bayesian registration of functions with a Gaussian process prior. Journal of Computational and Graphical Statistics, 26, no. 4, 894–904.
Lee, S. and S. Jung, 2017: Combined analysis of amplitude and phase variations in functional data. arXiv:1603.01775 [stat.ME], 1–21.
J. D. Tucker, J. R. Lewis, and A. Srivastava, “Elastic Functional Principal Component Regression,” Statistical Analysis and Data Mining, vol. 12, no. 2, pp. 101-115, 2019.
J. D. Tucker, J. R. Lewis, C. King, and S. Kurtek, “A Geometric Approach for Computing Tolerance Bounds for Elastic Functional Data,” Journal of Applied Statistics, 10.1080/02664763.2019.1645818, 2019.
T. Harris, J. D. Tucker, B. Li, and L. Shand, “Elastic depths for detecting shape anomalies in functional data,” Technometrics, 10.1080/00401706.2020.1811156, 2020.
M. K. Ahn, J. D. Tucker, W. Wu, and A. Srivastava. “Regression Models Using Shapes of Functions as Predictors” Computational Statistics and Data Analysis, 10.1016/j.csda.2020.107017, 2020.
J. D. Tucker, L. Shand, and K. Chowdhary. “Multimodal Bayesian Registration of Noisy Functions using Hamiltonian Monte Carlo”, Computational Statistics and Data Analysis, accepted, 2021.
X. Zhang, S. Kurtek, O. Chkrebtii, and J. D. Tucker, “Elastic k-means clustering of functional data for posterior exploration, with an application to inference on acute respiratory infection dynamics”, arXiv:2011.12397 [stat.ME], 2020.
Xie, S. Kurtek, E. Klassen, G. E. Christensen and A. Srivastava. Metric-based pairwise and multiple image registration. IEEE European Conference on Computer Vision (ECCV), September, 2014


Colophon
All models are wrong, but some are useful
Designers and artists make their portfolios to showcase their work. Pretty obvious. Websites are the digital form of showcasing the work. Also pretty obvious. However, recruiters see the web-portfolio as a process model of a designer. What is a designer’s ethos, what are the layers which she operates in, and what type of work does she likes to produce. While recruiters would like to know the first two things, most portfolio websites are just about the ends and not about the means /0. The idea of means is roughly translated to a designer's process, I mean a much deeper translation of means
It feels that most design portfolios look the same, talk in the same way, try to solve similar problems. They essentially create some kind of noise by closely resembling with each other. Being a designer also means to design your own portfolio, a portfolio that communicates at a different level than the others.
Although design output is usually visceral, experiential, to be able to display and view the ethos of designers, I think design articulation is a must. Development of ethos is a continuous process (which is longer and different) than design process. To represent that, blogs, tweets from designers are important to be able to know a designer completely.
In this representational sense,
All portfolios are rubbish, some are communicative.
Through blogs, tweets, small gimmicky artefacts, and notion links, I aim to make this website a bit more communicative, a bit more transparent, a bit less self-promotional and a bit more representative of me, my own digital garden which you can visit. I would like, if you liked it, but if you have some suggestions, I would love to hear them out.
Go-Hu-go
The website is build using Hugo and Tuftesque. Hugo is a static site generator written in Go. I have used Grav (2017-2019) and WordPress (2013-2017) before switching over to Hugo. Grav being a flat-file CMS, had tremendous build times as I used goDaddy servers to host. The compilation itself took around 30s to 1m. But the major drawbacks were not related to effeciency of compilation, but were functional. One, the pipeline to git was pretty complicated. The plugins were limited in functionality /1 1/ One needed to install plugins to embed YT videos. . The next overhaul of my web presence demanded an easy development and customisation with better loading time than Grav.
The build time of Hugo sites is around 20ms on my 4GB single core development machine running Ubuntu 18.04. Hugo allows me to have an easy offline development experience with more HTML/JS based plugins. Themes available for JAMSTACK are more in number than that of flat-file/CMS/Php based websites. As for hosting, I still have a goDaddy space, but I prefer to use ghpages. I do not mind the ‘github.io’ at least for the less serious and unstructured work. I can always point it to my domain later. Also, I do not use images much, so, gh pages should do the job.
The load time as measured through Pingdom for hugo site using github pages is 455ms from European servers. This is decent as compared to a Grav website on a goDaddy hosting with load time of 10s from European servers. Also, the number of requests is reduced from 56 to a mere 14 at the moment. As for writing experience, I, anyday prefer writing on Sublime text than on the WYSIWYG editors. Additionally, I use Notion these days which can export Pandocs markdown. So, I write whereever I want on Notion, and then, just copy-pasta for the Hugo site.
Tuftesque
I infatuated with side notes and pull-away margin notes while writing my term paper. gwern.net
alleviated the infatuation. TufteCSS felt the right answer as the moment. I do not use much images on my pages anyway (except when I want to show prototypes), although it has a pretty good way of lay-ing out images for the web. Thankfully, there were several implementations of Tufte CSS for hugo out there. Notably by ShawnOhare, Alanorth and NStrayer. I used NStrayer one as I found it through a blog and has pretty good explanation apart from documentation.
I intend to use margin notes for better structuring of my thoughts, with more intelligent and network content than a normal medium-like blog. While web is not the print /2, I borrow from Tufte’s tips on qualitative display of information 2/ From the Leipmann's description of the Tufte Project
Content
The logo.png
of this website is Ingi, a lazy-yet-cool cat and a friend of a friend-Paulanthony [their twitter, instagram]. Thanks to Paul for allowing me to use his supercool sketch of Ingi for this website.
Ingi /iŋdʒi/ the great Sketch by Paul
And the bird-app cliche: Opinions and ideas are my own. Projects are definitely my own.


Scroll Up and Down this page to see the parallax scrolling effect. This div is here to represent your web site content
.feature-list {
background-color: #3F51B5;
height: 100rem;
}

Scroll Up and Down this page to see the parallax scrolling effect. This div is here to represent your web site content


One of the key challenges in the world of human-computer interactions is training a computer to discern a user's intent from natural-language commands. Suppose that a user types "find news about Microsoft" into a news reader or browser. A person understands this command easily. But implementing the logic for a computer to understand this simple phrase is an exceedingly difficult task, especially when you factor in language and culture and the influence that those have on how we communicate.
Microsoft's Language Understanding Intelligent Service, or LUIS for short, is designed to fill this need, offering a fast and effective way to add language understanding to applications. LUIS lets you create models that take sentences or utterances and interpret them by discerning intents such as "find news" and extracting entities such as "Microsoft". LUIS utilizes interactive machine-learning techniques and benefits from extensive research on language understanding performed by researchers at Microsoft Research and other institutions. And once a LUIS model is built and trained, it is easily deployed as a Web service so bots and other applications can call it to convert commands entered by users into actions.
In this lab, you will create a LUIS application, configure a language-understanding model using both pre-built and custom entities, and define intents. Then you will build a bot that uses the model to respond to natural-language commands such as "find news about Microsoft" or "get the latest soccer news."
In this hands-on lab, you will learn how to:
The following are required to complete this hands-on lab:
This hands-on lab includes the following exercises:
Estimated time to complete this lab: 60 minutes.
The first step in creating an intelligent language model with LUIS is to provision an application in the LUIS portal. In order to use the portal, you must run it in Internet Explorer or Google Chrome. If you do not have one of these browsers available, stop now and install Chrome.
Open the LUIS portal in Internet Explorer or Chrome. If you aren't already signed in, click Sign in or create an account and sign in with your Microsoft account. If you are prompted to "let this app access your info," review the permissions requested and click Yes. Additionally, if you are prompted to provide additional details such as the country you live in and the company you work for, fill in the information and click Continue.
Signing in to LUIS
Click + New App, and then select New Application.
Creating a LUIS application
In the ensuing dialog, enter "Newsy" (without quotation marks) as the application name and select Bot as the usage scenario. Check the News & Magazines box and click the Add App button.
Entering information about the application
Your LUIS application is now provisioned and you are ready to start configuring it by adding intents and entities.
In the language of LUIS, intents represent actions such as "search" or "find," while entities describe target for intents. In this exercise, you will create a simple intent for searching the news and an entity to specify the type of news to search for, such as "soccer" or "Microsoft Surface."
Click + to the right of "Intents" to add an intent.
Adding an intent
Enter "SearchNews" (without quotation marks) for the intent name, and type "Find soccer news" (again, without quotation marks) into box below. The latter is an example of an utterance, which is a command that triggers an intent. Then click the Save button.
Defining an intent
Click + to the right of "Entities" to add an entity.
Adding an entity
Enter "NewsCategory" as the entity name. Then click the Save button.
Defining an entity
To connect the NewsCategory entity to the SearchNews intent, click the word "soccer" and select NewsCategory from the popup menu. Then click the Submit button.
Connecting an entity to an intent
The phrase "Find soccer news" is now associated with the SearchNews intent, and LUIS will understand the word “soccer” as the target of your action. In order for LUIS to train the model successfully, it needs a few more sample utterances.
Type "Get soccer news" into the box in the "New utterances" panel and click the arrow.
Adding an utterance
Select SearchNews from the drop-down list of intents.
Selecting an intent
Click the word "soccer" and select NewsCategory from the popup menu. Then click the Submit button.
Configuring an utterance
Repeat steps 6 through 8, making certain you select SearchNews from the intent drop-down, with the following sample phrases (without quotation marks) to populate your LUIS application with information to properly train the model:
Review the utterances you have defined by clicking Review labels and selecting Show all labeled utterances from the drop-down list. Confirm that all entries are assigned to the SearchNews intent and have a yellow-highlighted entity.
Reviewing labeled utterances
Click Train in the lower-left corner of the page. After a few seconds, the message "Last train completed" will appear.
Training a LUIS model
Your LUIS application is now configured to understand phrases such as "Get soccer news" and to discern that the user wants to search news for the term "soccer." LUIS is smart enough now to know that a phrase such as "Find hockey news" also represents an intent to search news, but for news regarding hockey even though you haven't explicitly trained the model with that term. Your LUIS model is now ready to be configured to support more advanced scenarios, such as identifying terms from a specific list of news categories.
Now that LUIS understands some basic intents and phrases, it's time to make the model even more intelligent by leveraging pre-built entities and phrase lists. Pre-built entities allow a model to know, for example, that words such as "tomorrow" and "September 30" that appear in an entity represent dates and times, or that "20 years old" represents an age. A complete list of the pre-built entities that LUIS supports can be found at https://www.microsoft.com/cognitive-services/en-us/LUIS-api/documentation/Pre-builtEntities. In this exercise, you will add the pre-built encyclopedia entity to your intents, and add a phrase list for refining news results by category.
Click + to the right of "Pre-built Entities" to display a list of pre-built entities.
Showing pre-built entities
Scroll down and select encyclopedia. Then click the OK button. A new pre-built entity named "encyclopedia" will be added to the list.
Adding the encyclopedia entity
Click + to the right of "Phrase List Features" to add a new phrase list.
Adding a phrase list
Type "NewsCategory" (without quotation marks) for the feature name. Enter the comma-delimited list below, and then click the Save button.
Business,Entertainment,Health,Politics,ScienceAndTechnology,Sports,US,World
Saving the phrase list
Now that you have added an encyclopedia entity and phrase list, it is time to create a couple more intents to help LUIS understand whether a user wants to search news by using an encyclopedia entity such as "Super Bowl" or "World War," or using a more general category such as "Health" or "Politics." Begin by clicking SearchNews in the list of intents.
Opening the SearchNews intent
Click + Add Action. Then click + Add Parameter. Enter "Encyclopedia" as the parameter name and select encyclopedia in the drop-down list labeled "Type." Then click the Save button.
Adding an action
Click + to the right of "Intents" to add an intent.
Adding another intent
Enter "SearchNewsByCategory" for the intent name and "Find Super Bowl news" as an example of a command that triggers the intent. Then click + Add Action, followed by + Add Parameter. Under "Action Parameters," check the Required box, enter "NewsCategory" as the parameter name, select NewsCategory for both Type and Value, and enter "Please specify a category" for the prompt. Then click the Save button.
Saving the intent
Notice that the phrase "find super bowl news" is now displayed, with the term "super bowl" highlighted and "SearchNewsByCategory" selected as the intent. LUIS was smart enough to understand the term "super bowl" as an encyclopedia entry. Click the Submit button to add this entry to your model.
Saving the utterance
Type "Get Health news" into the new utterances box and click the arrow.
Adding an utterance
Select SearchNewsByCategory from the drop-down list of intents. Click the word "health" and select NewsCategory to connect the two. Then click the Submit button.
Saving the utterance
Click Train in the lower-left corner of the page to train the model.
Your LUIS model has now been enhanced with a pre-built entity and a phrase list, and can understand most typical phrases relating to finding news based on various terms. Let's see how LUIS interprets this information and publish the model to make it available for use.
In this exercise, you will publish the model to an HTTP endpoint so it can be accessed by applications that wish to incorporate language understanding. Before proceeding, be certain that you trained the model in the last step in the previous exercise. If you did not train it, train it now.
Click Publish.
Publishing the model
Click the Publish web service button.
Publishing to an HTTP endpoint
After a short delay, the dialog will expand and show an interface for entering queries, calling the HTTP endpoint, and viewing query results. Type "Find Super Bowl news" into the "Query" box and then click the link below to test the query.
Testing the query
This calls the HTTP endpoint and displays the JSON data that is returned. Observe that LUIS identified "super bowl" as the entity and determined that "SearchNewsByCategory" was the most likely intent:
{
"query": "Find Super Bowl news",
"topScoringIntent": {
"intent": "SearchNewsByCategory",
"score": 0.414106578,
"actions": [
{
"triggered": false,
"name": "SearchNewsByCategory",
"parameters": [
{
"name": "NewsCategory",
"type": "NewsCategory",
"required": true,
"value": null
}
]
}
]
},
"intents": [
{
"intent": "SearchNewsByCategory",
"score": 0.414106578,
"actions": [
{
"triggered": false,
"name": "SearchNewsByCategory",
"parameters": [
{
"name": "NewsCategory",
"type": "NewsCategory",
"required": true,
"value": null
}
]
}
]
},
{
"intent": "SearchNews",
"score": 0.131110832,
"actions": [
{
"triggered": true,
"name": "SearchNews",
"parameters": [
{
"name": "Encyclopedia",
"type": "encyclopedia",
"required": false,
"value": null
}
]
}
]
},
{
"intent": "None",
"score": 0.04309769
}
],
"entities": [
{
"entity": "super bowl",
"type": "builtin.encyclopedia.time.event",
"startIndex": 5,
"endIndex": 14,
"score": 0.9727775
}
],
"dialog": {
"prompt": "Please specify a category",
"parameterName": "NewsCategory",
"parameterType": "NewsCategory",
"contextId": "dc4c0b11-95b1-463f-8251-2b7e59253ee9",
"status": "Question"
}
}
Click the X in the upper-right corner of the dialog to close the dialog.
Now that the model is available over HTTP, you can call it from an application and incorporate the model's intelligence in the application.
In this exercise, you will use the Microsoft Bot Framework to build a bot and connect it to the LUIS model you built and deployed so the bot can respond to commands such as "Find health news." You will use Visual Studio Code to write the bot. If you haven't installed Visual Studio Code, please do so before continuing with this exercise. It's free, and it runs on Windows, Linux, and macOS.
Start Visual Studio Code and select Integrated Terminal from the View menu. A TERMINAL window will appear at the bottom of the workspace.
Opening an Integrated Terminal
In the TERMINAL window, execute the following commands to create a folder named "Newsy" and make it the current directory:
md Newsy
cd Newsy
Creating a "Newsy" folder
Execute the following command in the TERMINAL window to launch a new instance of Visual Studio Code from the Newsy directory. You can then close the old instance of Visual Studio Code.
code .
In the new instance of Visual Studio Code, click the New File icon to create a new file in the "Newsy" folder, and name the file app.js.
Adding a file to the project
Enter the following line of JavaScript code into app.js. Then save the file.
console.log("Welcome to Newsy!");
Select Integrated Terminal from the View menu again to open an integrated terminal.
In the TERMINAL window, execute the following command to install the Microsoft Bot Builder SDK:
npm install --save botbuilder
Now execute this command to install the packages needed to leverage REST-based messaging routes in your code:
npm install --save restify
Execute the following command to run the application. Confirm that "Welcome to Newsy!" appears in the TERMINAL window:
node app.js
Click the Debug button in the ribbon on the left. Then click the green arrow at top and select Node.js from the drop-down list. This adds a new configuration file named launch.json to your project. When a message appears asking you to set up the launch configuration file for this project, click the Close button.
Adding a launch file to the project
Add the following line to launch.json, inserting it between the "program" property and the "cwd" property as shown below. Then save the file.
"console": "integratedTerminal",
Configuring the integrated terminal as the debugging console
Return to app.js and replace the contents of the file with the following code:
var builder = require('botbuilder');
var https = require('https');
var querystring = require('querystring');
var connector = new builder.ConsoleConnector().listen();
var bot = new builder.UniversalBot(connector);
var luisAppId = "LUIS_APP_ID";
var luisSubscriptionKey = "LUIS_SUBSCRIPTION_ID";
bot.dialog('/', [
function (session) {
if (session.userData.intent == null) {
builder.Prompts.text(session, "Welcome to Newsy! Try typing something like 'Find health news'.");
}
else {
builder.Prompts.text(session, "Try typing something like 'Find health news'.");
}
},
function (session, results) {
session.userData.searchQuery = results.response;
var searchQuery = querystring.escape(session.userData.searchQuery);
var searchQueryString = "";
processLuisResults(session, searchQuery, searchQueryString);
}
]);
var processLuisResults = (function (session, searchQuery, searchQueryString) {
//CALL LUIS APPLICATION
var optionsLuisGet = {
host: 'api.projectoxford.ai',
port: 443,
path: '/luis/v2.0/apps/' + luisAppId + '?subscription-key=' + luisSubscriptionKey + '&q=' + searchQuery + '&verbose=true',
method: 'GET'
};
var reqGet = https.request(optionsLuisGet, function (res) {
res.on('data', function (luisResults) {
var luisResult = JSON.parse(luisResults.toString());
session.userData.intent = luisResult.intents[0].intent.toString();
if (luisResult.topScoringIntent.intent == "None") {
session.send("I'm not quite sure what you mean by '" + session.userData.searchQuery.toUpperCase() + "'");
}
else {
if (luisResult.topScoringIntent.actions[0].parameters[0].value != null) {
session.userData.entity=luisResult.topScoringIntent.actions[0].parameters[0].value[0].entity;
}
else {
session.userData.entity = luisResult.entities[0].entity.toString()
}
switch (session.userData.intent) {
case "FindNews":
searchQueryString = 'searchQuery=' + querystring.escape(session.userData.entity);
break;
case "FindNewsByCategory":
searchQueryString = 'category=' + querystring.escape(session.userData.entity);
break;
default:
searchQueryString = 'searchQuery=' + querystring.escape(session.userData.entity);
break;
}
session.send("Here's some news about " + session.userData.entity.toUpperCase() + " for you:");
//SEND DERIVED LUIS INTENT OVER TO BING SEARCH
showNewsResults(session, searchQueryString);
}
});
});
reqGet.end();
reqGet.on('error', function (e) {
session.send(e.toString());
});
//END CALL LUIS APPLICATION
});
var showNewsResults = (function (session, searchQueryString) {
//CALL BING NEWS SEARCH
var optionsSearch = {
host: 'traininglabservices.azurewebsites.net',
port: 443,
path: '/api/News?' + searchQueryString,
method: 'GET'
};
var reqGet = https.request(optionsSearch, function (res) {
res.on('data', function (newsResults) {
var newsResult = JSON.parse(newsResults.toString());
for (var result in newsResult) {
val = newsResult[result];
var title = val.Title;
var description = val.Description;
session.send(title + "\r\n" + description);
}
});
});
reqGet.end();
reqGet.on('error', function (e) {
session.send(e.toString());
});
//END BING NEWS SEARCH
});
Take a moment to review the code and observe how it uses the Microsoft Bot Framework Bot Builder to prompt the user, and how it places calls to the HTTP endpoint created in Exercise 4 to access your LUIS model.
Before you can test the bot, you need to update your code with the app ID and subcription ID of your LUIS model. These values can be retrieved from LUIS portal. Return to the portal in your browser and open the Newsy application you created in Exercise 1 if it isn't already open.
Click App Settings.
Opening App Settings
Copy the app ID to clipboard. Then click the X in the upper-right corner to close the dialog.
Copying the app ID
Return to app.js in Visual Studio Code and replace LUIS_APP_ID on line 8 with the app ID that is on the clipboard.
Return to the LUIS portal and click your name to the right of the gear icon.
Accessing LUIS account settings
Click Subscription Keys. Then copy the programmatic API Key to the clipboard. This is your LUIS subscription ID.
Copying the subscription Id
Return to app.js in Visual Studio Code and replace LUIS_SUBSCRIPTION_ID on line 9 with the app ID that is on the clipboard. Then save the file.
That's it! Your bot is now built, connected to LUIS, and ready to test.
The motivation for creating a bot is to have it carry on conversations with users. LUIS makes those conversations feel much more natural. In this exercise, you will run the bot you implemented in the previous exercise and see Language Understanding Intelligent Service at work.
In Visual Studio Code's TERMINAL window, execute the following command to start the bot:
node app.js
Type "Hello" to "wake up" the bot. Then press Enter and wait for the bot to respond.
Waking up the bot
Type "find soccer news" and press Enter. Wait for the bot to respond. After a brief pause while the bot calls out to your LUIS model, up to five relevant news articles will appear.
Communicating with the bot
Type "Hello" again and press Enter. Then type "find some great news about Microsoft" and press Enter again. What does the bot return this time?
Feel free to try other commands such as "get the latest political news" and continue to converse with the bot. It understands a relatively narrow range of commands, but for those commands that it does understand, it almost seems to know what you're saying. Of course, you could expand the bot's vocabulary by adding to the LUIS model that you built. The only limit is how much time you're willing to spend refining and enhancing the model.
In this hands-on lab you learned how to:
There is much more that you can do to leverage the power of the Microsoft Language Understanding Intelligent Services in your code. For starters, try experimenting with other LUIS features such as intent dialogs and adding more pre-built entities to your model. For additional background on LUIS and examples of how to use it, refer to https://docs.botframework.com/en-us/node/builder/guides/understanding-natural-language/.
Copyright 2016 Microsoft Corporation. All rights reserved. Except where otherwise noted, these materials are licensed under the terms of the MIT License. You may use them according to the license as is most appropriate for your project. The terms of this license can be found at https://opensource.org/licenses/MIT.


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Updated:
Survey-Rails Project
I planned to write this post yesterday, but getting my wisdom teeth out hurt a lot more than I expected.
Anyways, I have been working on this project for the past couple of weeks and I think I have gotten it to a point that I am happy with.
Check out the github and survey-rails test site.
This was a good project to work on because it involved many models working togethere and also included heavy ajax which was something I needed to learn. In this website, a user can log in or create an account. From there, they can create surveys, take surveys, and see the stats of the results of their surveys.
What I learned:
- One of the most important bits of information I learned was that by adding to my forms, it would automatically submit via ajax. This saved me time from having to write the ajax for each submit.
- After making my own flash messages on the last project, I experimented with toastr messages which are a lot cleaner and look nicer than the ones I was making.
- By using 6 models in the website, it forced me to think about how each model was interacting and how I could connect them.
- I also experimented with foundation icons (which don't seem to work all the time on the test site, but do locally). They added a nice touch to the site and make it easier to navigate.
- I added a bar graph and pie graph in the stats section for each question on the survey. I used google charts which worked very well. This also was good practice for displaying data, which my next project will be on.
- On my route to understanding how jquery and ajax work, I had to learn more about js.erb files and how rendering works. before doing this project, I had no idea what the render block was for but after researching it, I found the purpose. It finds the type of request being sent and matches the type to the correct response. This was useful in this project because it allowed me to give error and success messages along with updating the page.
- One major thing I learned during the project was performance testing. I used both a website called loader.io where I did load testing on getting pages, along side with benchmarking to test post routes. I wrote this test page which along with fixtures to test how fast the server could respond to mulitple requests. It also created a file to view the records in which I could use to visualize the results of the test. From the loader.io test, which tested the process of getting the survey: This result was longer than the test I ran with benchmark but it was on heroku which is slower than the local environment.
What I want to learn:
- Even though the ajax worked well most of the time, there were still on rare occasion times when it wouldn't do what I was expecting it to. I want to become better at ajax and jquery so I can make my projects as easy to use as possible.
- I also want to learn more about the asset pipeline because I have a feeling that is the reason why the icons aren't working in production.
Feedback from testing:
I had few people test the site and got their feedback. For tech savy people like my friend and my brother, there was no issues at all really with the site. But for less inclined people like my mother, there was a bit more of an issue. For the next project, I will have to find a balance bewteen making the site fool proof and not overdoing it with instructions. Most of the issue for the non tech savvy people came with creating the survey form. I though about changing the form to make it easier to use but after looking at the edit form at SurveyMonkey, I decided mine was actually better. Although theirs has more options, mine is simpler to use.
The survey monkey edit page:


- radians(double) - Static method in class JProcessing.Processing
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- RADIUS - Static variable in class JProcessing.Processing
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Shape mode -- use the first two parameters of a rectangle, ellipse, or PImage
as the location of
the center of their shape, and use the last two parameters as half of their width and height.
- random() - Static method in class JProcessing.Processing
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- random(value) - Static method in class JProcessing.Processing
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- random(value, value) - Static method in class JProcessing.Processing
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- random2D() - Static method in class JProcessing.Processing.PVector
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- random3D() - Static method in class JProcessing.Processing.PVector
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- randomGaussian() - Static method in class JProcessing.Processing
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- randomSeed(long) - Static method in class JProcessing.Processing
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- rect(double, double, double, double) - Method in class JProcessing.Processing
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- rect(float, float, float, float) - Method in class JProcessing.Processing
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- rectMode(byte) - Method in class JProcessing.Processing
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- red(int) - Method in class JProcessing.Processing
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- red(Color) - Method in class JProcessing.Processing
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- redraw() - Method in class JProcessing.Processing.PGraphics
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Deprecated.
- redraw() - Method in class JProcessing.Processing
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Processing redraw()
function, execute the code within draw()
one time and then manually update a frame.
- repaint() - Method in class JProcessing.Processing
-
Deprecated.
- repaint(long) - Method in class JProcessing.Processing
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Deprecated.
- repaint(int, int, int, int) - Method in class JProcessing.Processing
-
Deprecated.
- repaint(long, int, int, int, int) - Method in class JProcessing.Processing
-
Deprecated.
- requestImage(String) - Static method in class JProcessing.Processing
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- resetMatrix() - Method in class JProcessing.Processing
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- resize(int, int) - Method in class JProcessing.Processing.PImage
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Resize the PImage
.
- RETURN - Static variable in class JProcessing.Processing
-
The return character
- RGB - Static variable in class JProcessing.Processing
-
Color mode -- specify input color in RGB color mode
Image format -- represents an image with 8-bit RGB color components packed into integer pixels.
- RIGHT - Static variable in class JProcessing.Processing
-
The keycode for right (=39 or KeyEvent.VK_RIGHT
)
Text alignment -- align text to the right
- root(double, double) - Static method in class JProcessing.Processing
-
- rotate(angle) - Method in class JProcessing.Processing.PVector
-
- rotate(Processing.PVector, angle) - Static method in class JProcessing.Processing.PVector
-
- rotate(double) - Method in class JProcessing.Processing
-
- ROUND - Static variable in class JProcessing.Processing
-
Stroke cap mode -- ends unclosed paths and dash segments with a round decoration that has a radius
equal to half of the line width.
- round(float) - Static method in class JProcessing.Processing
-
- round(double) - Static method in class JProcessing.Processing
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- roundRect(double, double, double, double, double, double) - Method in class JProcessing.Processing
-
- roundRect(float, float, float, float, float, float) - Method in class JProcessing.Processing
-


title: ‘MapAgora: R Package for Collecting U.S. Nonprofit Organization Data’ tags:
As Tocqueville stressed in Democracy in America (1835), voluntarism is a defining characteristic of American democracy. From this perspective, nonprofit organizations are vital to understanding civic activities and their economic, political, and social consequences in the US. Despite their importance, it is difficult to analyze them systematically at scale because information about these organizations is scattered across various sources, such as their tax reports, websites, and social media posts. The data in these sources is not only stored in diverse, unstructured formats (e.g., XML, HTML, JSON) but also created and organized in complex and largely inconsistent ways.
MapAgora addresses all of the problems identified above. It helps researchers and practitioners systematically collect information on nonprofit organizations in the US based on these organizations’ wide range of administrative and digital trace data with little demand for technical understanding of the underlying unstructured data formats and their documentations. On the administrative data side, the package parses the tax reports these organizations filed with the US Internal Revenue Service (i.e., IRS 990 filings). On the digital trace data side, the package extracts how these organizations describe themselves based on their websites and social media posts.
The potential applications of this unprecedented big data on the study of the US nonprofit landscape are numerous. For instance, analysts can easily link the physical locations of these organizations with other administrative data, such as the Census or commercial/political surveys. They might also be interested in leveraging the detailed descriptions of the organizations’ missions and activities to automatically classify these organizations based on some latent variables (e.g., racial justice organizations).
The package is available on GitHub and can be installed in the following way:
# Install devtools
if(!require(devtools)) install.packages("devtools")
# Install MapAgora
devtools::install_github("snfagora/MapAgora")
MapAgora is an R package that helps researchers and practitioners collect the most comprehensive data on nonprofit organizations in the US. As summarized in Table 1, there are many proprietary and even open-source databases and tools that explore IRS data. However, if researchers and practitioners are interested in linking these data with nonprofit organizations’ digital traces, such as their websites and social media posts, they must confront many additional technical challenges. MapAgora
reduces these steps and offers a flexible tool that can fit into the various needs of researchers and practitioners.
| Name | Developer | Data sources | Paywalled? | Bulk Download? |
|---|---|---|---|---|
| Encyclopedia of Associations | GALE | IRS | Yes | No |
| Guidestar | Candid | IRS | Yes | No |
| National Center for Charitable Statistics | Urban Institute | IRS | No | Yes |
| Nonprofit Data Search | OpenSecrets | IRS | No | Yes |
| Nonprofit Explorer | ProPublica | IRS | No | Yes |
| open990 | Applied Nonprofit Research | IRS | No | Yes |
| Open Data for Nonprofit Research | Jesse Lecy (Arizona State University) and Nathan Grasse (Carleton University) | IRS | No | Yes |
| MapAgora | Milan de Vries and Jae Yeon Kim (SNF Agora Institute, Johns Hopkins University) | IRS, websites, social media posts | No | Yes |
Table 1. Comparison with existing databases and data tools for collecting US nonprofit organizations.
MapAgora is designed for researchers and practitioners interested in systematically examining nonprofit organizations in the US. It can be used to create nonprofit datasets that can be used directly for analysis and linked with other administrative datasets, such as the Census or commercial/political surveys. It can also be used to classify nonprofit organizations based on the text data collected from tax reports, websites, and social media posts using natural language processing and machine learning [@ma2020automated].
These use cases explain the best workflow for using the functions in the package to extract information from a particular data source (IRS/websites/social media posts).
IRS data provide comprehensive demographic and financial information on civic organizations.
To begin, load the IRS index for a selected year.
# load 2018 index file from the IRS Amazon Web Server
idx_2018 <- import_idx(2018)
Digitized 990 data are available as XML files in an Amazon Web Server (AWS) repository. The yearly index file organizes these files. The year, in this case, refers to the year the IRS processed the file. For example, all the files in the 2018 index were processed in 2018. However, the tax filing itself can be from any previous date. This discrepancy makes it challenging to locate a specific filing for a given organization and year.
To remedy this, we rebuilt a comprehensive index file for this package. This index, included in the package, contains the additional fields TaxPeriodBeginDt,
TaxPeriodEndDt,
and (importantly) Tax_Year.
Tax_Year
refers to the tax year of the 990 form the organization submitted, but IRS_Year
refers to the year the IRS converted the submission into digitized form. Note that organizations can choose to adhere to either a calendar or a fiscal year. TaxPeriodBeginDt
and TaxPeriodEndDt
refer to the beginning and end dates of that filing’s year.
The location of a given digitized filing can be found with the get_aws_url
function. This function returns the location of the XML file in the AWS repository.
# this organization's 2018 990 filing can be found here
aws_url <- get_aws_url("061553389", 2018)
To parse fields from filings, first load an XML file of interest. Note that because this function calls get_aws_url()
, there is no need to find the URL location. The first step for most uses of this package is get_990()
.
## load an XML for this organization's 2018 filing.
xml_root <- get_990("221405099", year = 2018)
## see name of this organization
organization_name <- get_organization_name_990("221405099")
Nonprofit organizations can file different versions of the 990 form depending on their specific status. Knowledge of the specific form type is not required to extract values with this package, but the type of a given form can be seen with the following:
## see form type of a filing
filing_type <- get_filing_type_990(xml_root)
Available parsed fields can be seen in the irs_fields
table. The package_variable
column lists the variable names to be used in functions. Other columns show both the XML path and the physical form location for that variable. These variables are grouped by category and subcategory.
## see available variables
names(irs_fields)
## get total revenue for this org
revenue_total <- get_single_value_990(xml_root, "revenue_total")
Organizations report descriptive information about their primary mission and main activities in 990 filings. The concatenated responses to these fields can be extracted.
# mission statement
mission_desc <- get_value_990(xml_root, "mission_desc")
# program description
program_desc <- get_value_990(xml_root, "program_desc")
To help track the information on nonprofits not captured by their tax filings, we developed code to extract the text that organizations use to describe themselves on their websites.
The most common place to find informative text about an organization is on its website, particularly its About Page. The About Page often contains information about the group’s mission, notable activities, and history.
There is no single standard way to find an About Page, but many common patterns predominate. Our strategy is to begin by extracting all the links from an organization’s home page. Then, we look for links containing the string “about” or “who.” If we fail to find these patterns, we try to identify other less direct common patterns.
Websites can have multiple About Pages, often nested under a common heading. We first need to identify all About Pages for a selected website.
# find this site's about pages
about_pages <- extract_about_links("http://www.ibew567.com")
Websites typically contain a great deal of code that is not directly displayed in the use case. In addition, displayed text often includes menus, headers, footers, or other small text snippets. Using natural language processing (NLP) can help clean up the text, but it is tedious and often produces low-quality output.
Our approach is to filter out text that is not in sentences with at least a certain threshold to extract salient text. Also, when extracting text from multiple pages, we look for and remove shared header and footer language to avoid duplicating it in the final corpus.
# find the combined text of about pages and home page
org_web_text <- get_all_texts("http://www.ibew567.com")
MapAgora has been designed with several key principles in mind.
Principle 1. User-First. Most data about nonprofit organizations exists in specialized, technical structures. IRS filings, for instance, can be made on three different versions of the same form (i.e., Form 990, 990-EZ, and 990-N; for more information, see this IRS guideline), depending on the organization’s gross income and total assets. We develop the package by focusing on the kind of variables that a user would want to extract from the data sources. In other words, the functions in the package do not assume that users understand the underlying data structures or their documentation. If users would like to know about an organization’s revenue, knowing the target field is sufficient.
Principle 2. Linkage. Part of the motivation to create this package is that nonprofit data stems from multiple sources. The package aims to make both extracting and linking data from these different sources easy and fast. As such, the package is suitable for building a relational database based on these diverse data sources.
Principle 3. Modularity. We acknowledge that researchers and practitioners have various needs to use these data sources to solve their problems. We assume that building the most comprehensive dataset based on these data sources is not a desirable data collection method for most of these users, given the technical and administrative costs involved. With these time and other resource constraints in mind, we built functions that allow users to extract the specific subset of the data they need.
Social media handles
Social media posts help to investigate the public presence of organizations. However, there is no standard way of identifying the social media handles belonging to a given organization.
Here, we approach this problem by extracting social media handles from an organization’s website. As a secondary strategy, we provide the functionality to search for social media handles using the Bing search application programming interface (API).
Twitter handle
Twitter handles are identified on a page by extracting all links on the page and then filtering for possible Twitter handles. If a handle can be found, some link cleanup is attempted before returning it.
Facebook page
The
find_facebook_page_from_org_page()
function works similarly but looks for a Facebook page instead of a handle.


You are given the array a consisting of n elements and the integer k≤n.
You want to obtain at least k equal elements in the array a . In one move, you can make one of the following two operations: Take one of the minimum elements of the array and increase its value by one (more formally, if the minimum value of a is mn then you choose such index i that ai=mn and set ai:=ai+1); take one of the maximum elements of the array and decrease its value by one (more formally, if the maximum value of a is mx then you choose such index i that ai=mx and set ai:=ai−1). Your task is to calculate the minimum number of moves required to obtain at least k equal elements in the array.The first line of the input contains two integers n and k (1≤k≤n≤2⋅105) — the number of elements in a and the required number of equal elements. The second line of the input contains n integers a1,a2,…,an (1≤ai≤109), where ai is the i-th element of a.
Print one integer — the minimum number of moves required to obtain at least k equal elements in the array.
6 5
1 2 2 1 2
5
6 5 1 2 2 1 2
5
Consider the queries. The first query is [3,8,9,10] . The answer is "YES" as you can choose the path from the root 1 to the vertex u=10. Then vertices [3,9,10] belong to the path from 1 to 10 and the vertex 8 has distance 1 to the vertex 7 which also belongs to this path. The second query is [2,4,6] . The answer is "YES" as you can choose the path to the vertex u=2. Then the vertex 4 has distance 1 to the vertex 1 which belongs to this path and the vertex 6 has distance 1 to the vertex 2 which belongs to this path. The third query is [2,1,5] . The answer is "YES" as you can choose the path to the vertex u=5 and all vertices of the query belong to this path. The fourth query is [4,8,2] . The answer is "YES" as you can choose the path to the vertex u=9 so vertices 2 and 4 both have distance 1 to the vertex 1 which belongs to this path and the vertex 8 has distance 1 to the vertex 7 which belongs to this path. The fifth and the sixth queries both have answer "NO" because you cannot choose suitable vertex u .


Conflict3/8/2015I feel like coming from a traditional Mexican family it's really easy for parents to see their child going to a college or a university as their answer for you to make a living. Sometimes they can act like that's the ONLY answer. I know I've had my share of conflicts with my parents on this subject. When I finally realized I was only going to college because of my parents and not because it was something I truly wanted to do, I dropped out. The news hit my parents really hard as if I had given up on my future. The day I told my parents I wasn't going back to school they felt so let down. They said everything they've done at this point in their life was to set me and my siblings up to go to school, especially considering the fact that my oldest sister graduated from a university and it worked out really well for her. It upsets me because they're so close minded on what is really going on now a days but I can't blame them for their lack of knowledge seeing how they're barely learning how to text. I felt as if the only way I could redeem myself with them was if I hit the goal they wanted for me without a degree. I made them realize that it wasn't the degree that they wanted me to get, it was the a career or some sort of long term financial stability. So I showed them I wasn't dropping out to just to be at home but to put my attention towards other things that can get me there a lot faster. I made a game plan and I showed them how I was going to put it to action. Their minds definitely changed after they saw how excited and motivated I was. Only thing I'd change from this experience is not dropping out sooner. Or even starting school at all. I see it was a waste of time for me because I was doing something I didnt want to do because other people wanted me to do. I learned that if I focus too much on just making other people happy no matter who it is, it won't necessarily mean I'll be happy. |
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Other Blog entries |


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def | Clear |
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def | OutputUnchecked |
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def | OutputPartial |
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def | TryMerge |
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def | __str__ |
| |
|
|
| task_name_ |
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| has_queue_name_ |
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| queue_name_ |
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| has_app_id_ |
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| app_id_ |
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int | has_queue_name_ = 0 |
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string | queue_name_ = "" |
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int | has_app_id_ = 0 |
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string | app_id_ = "" |
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int | kqueue_name = 1 |
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int | ktask_name = 2 |
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int | kapp_id = 3 |
| |
The documentation for this class was generated from the following file:
- code/googleappengine-read-only/python/google/appengine/api/taskqueue/taskqueue_service_pb.py


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Meet Our Alumni
Profiles of some of our prominent alumni.
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Year : {{member.year}}
Went to : {{member.wentTo}}
mail: {{member.mail}}
phone : {{member.phone}}


2019年是美国第一条横贯大陆铁路建成150周年，为了纪念第一批华人来美建设这条铁路所做出的巨大贡献， 美国华人博物馆(MOCA)将其2019 TCS NYC Marathon的活动命名为 MOCA Spike 150 – Running Forward With Our Stories！ 鼓励大家通过马拉松公益跑，全国线上接力，以及一天一个故事的方式讲述150个华人的历史故事，敬请大家持续关注， 讲出华人自己的故事，回顾过去，立足现在，展望未来，共同书写美国历史，使之更加充实完整。
关注MOCA Spike 150活动网站
支持MOCA的事业，请给MOCA Spike 150团队捐款
2019 is the 150th anniversary of the completion of the Transcontinental Railroad. To commemorate the contribution of the Chinese laborer to this monumental railroad, Museum of Chinese in America (MOCA) has designated its 2019 TCS NYC Marathon event as MOCA Spike 150 – Running Forward With Our Stories！ We encourage everyone to participate through 1) our national relay and 2) a story a day which consists of 150 Chinese American personal stories. We hope you will continue to follow and support us. Go tell your story. Reflect on the past; root in the present and embrace the future. Let us add to the American history, making it fuller and richer.
MOCA Spike 150 website
Support MOCA，Donate to MOCA Spike 150 Team


{% extends "base.html" %}
{% block products %}
This fenotype is empty. A quirk of the Genetic Programming algorithm is the fact that it sometimes proposes empty sets. Because empty shops will never be succesful, expect this one to disappear quickly from the population. Click here to start a new session and view another shop.
{% endblock %}


Technical Skills
Computer Software: ArcMap, QGIS, RStudio, ERDAS Imagine
Programming Languages: Python, HTML, JavaScript, ModelBuilder
Laboratory Techniques: Abrasive Sandblaster, Rainfall Simulator, Freeze Dryer, Thin Section Machine, Rock Cutter, Soil Moisture Probe, Orbital Shaker, Soil Lysimeter, Liquid Isotope Analyzer, Particle Size Analyzer, pH/Conductivity Meters, Syringe/Gravitational Filters


Pingpong will count up to your number from 1 with these exceptions:
- Numbers divisible by 3 become "ping"
- Numbers divisible by 5 become "pong"
- Numbers divisible by 3 and 5 become "pingpong"
Pingpong will count up to your number from 1 with these exceptions:
Here's your result:


bool proc_terminate ( resource $process [, int $signal = 15 ] )
Signals a process (created using proc_open()) that it should terminate. proc_terminate() returns immediately and does not wait for the process to terminate. proc_terminate() allows you terminate the process and continue with other tasks. You may poll the process (to see if it has stopped yet) by using the proc_get_status() function.
Returns the termination status of the process that was run.


NOTE (March 2013): The calendar has been broken for a while, ever since my syncing between laptop, Apple's MobileMe and Google was broken (mostly due to Apple). I'll figure something out eventually.
Please bear the following points in mind (March 2012):
- Note the timezone (I'm in the UK so it's either BST or GMT)
- Pinkish coloured items are usually unconfirmed placeholders
- Mustard-ish coloured items can sometimes be ignored
- To subscribe to an amalgamated feed of free/busy times, Right click here and copy the link into your calendar (courtesy of MashiCal)
- NB the iCal feed is several hours delayed


Brief History of Cytoscape Automation
When to Automate
Why run a script when I could just use Cytoscape directly?
- For things you want to do multiple times, e.g., loops
- For things you want to repeat in the future
- For things you want to share with colleagues or publish
- For things you are already working on in R or Python, etc
To prepare data for collaborators
Automation Examples
Check out the expanding collection of R and Python scripts:
Automation with RCy3
Automation with Apps
Check out this growing collection of F1000Research articles on Cytoscape apps that support automation:
f1000research.com/gateways/cytoscapeapps/automation


{%extends "base/page.html"%} {%block title%}Feature Description{%endblock%} {%block contents%}
{{feature.featuredescription}}


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oa. Publications obtained for 2. further full text evaluation < (n=496)
Studies included in meta- analysis(n=20)
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Publication excluded Duplicate date(n=103)
Studies excleded (n=161); Case report or review articles or editorials
Exclusion for not meeting involved criteria or not obtaining revelant data
(n=476):