Grammarly to Markdown
Convert your Grammarly texts to Markdown
Because writing is what matters
Every day, more is written and published on the internet. Many of these texts are written in English, by non-native speakers. Grammarly does a really good job helping writers to create bold and clear texts.
Markdown is a click away
Many might want to export documents in Markdown, but there is no official way to export texts to Markdown. This is where this extension will help you.
While in the document page, a simple click on the extension button (located on the browser toolbar) will do its magic. It will parse and copy the text to your clipboard, ready to be published in any markdown medium.
Want to contribute?
If you found a bug, want to suggest a feature or even implement some improvement, please have a look at this project on Github -- and perhaps leave a star 😉


I am a web developer with a passion for learning and a zeal for Torah. Yehovah gave me this zeal to remember, to learn and to teach truth. My goal however is not to be your teacher, but to make Yah your one and only source of truth, through His Torah.  Ask yourself one question.
If Paul was removed from the Bible - what would you believe?
One could remove one of any other book from the Bible and it would not affect what we fundamentally believe as Yah repeats himself to us. Paul's message is ONLY taught by Paul and by no one else. His saving message isn't even what he taught in Acts.
More on the web about me


Alerts tab
The Alerts tab show the Alerts
that have been raised in this session.
The alerts are displayed in a tree in risk order in the left hand pane, and each
node of the tree shows the total number of alerts underneath it.
Selecting an alert with one click will display it in the right hand pane.
Double clicking an alert will display the Add Alert
dialog which will allow you to change the alert details.
Filtering alerts
The filter buttons, shown immediately above the tree, allows you to restrict which alerts are displayed:
/
Show only URLs in Scope / Show all URLs
Allows to show only the alerts of URLs that are in scope.
/
Link / Unlink with Sites selection
Allows to show only the alerts of the Sites tab selected tree node.
Note: Selecting one filter will disable the other as they are mutually
exclusive.
Edit Alert
Will display the Add Alert dialog which will allow you
to change the alert details, for the selected alert.
Note: If multiple alerts are selected in the tree, the most recently selected
item is edited.
Delete All Alerts
Allows to delete all alerts.
Right click menu
Right clicking on a node will bring up a menu which will allow you to:
Open/Resend with Request Editor...
This will bring up the
Manual Request Editor dialog which allows you to
resend the request associated with the alert after making any changes to it that you want to.
See also


--- layout: post title: "My Week in Review" date: 2018-03-18 08:30:00 +0000 tags: image: https://s3.amazonaws.com/hackedu/2018-03-14-osu.jpg imagecredits: https://www.flickr.com/photos/vasenka/5115401450 ---
Icon credits: The Noun Project


Highlights include linear regression models, data visualization,
prediction mechanics, and other data fit models.
Social-Regional Analysis of COVID-19
- University of California Berkeley : 2020 :
Using the data provided from the Berkeley Yu Group,
this report analyzed the relationship between
demographic measures (income, political region, rural-urban etc.)
of regions with the impact of the recent Corona Virus and predicted
relationships using sklearn's Log Regression model. Validated by cross validation
and multi-dimensional data visualization and mapping.
Food Safety Investigation and Mapping - University of California Berkeley : 2020 :
Investigation of restaurant food safety scores for restaurants in San
Francisco. Using pandas and data visualization through matplotlib and seaborn,
this analysis looks at the geographic variances in food inspection scores.
Spam Email Feature Engineering and Linear Regression
- University of California Berkeley : 2020 :
Utilization of feature engineering and sklearn (Logistic Regression) to
process email data, fit models,
validate performance of model, minimize overfitting and
generate precision-recall curves.
Highlights include data analysis, machine
learning, simulations, and mapping in Python, Java, SQL, and R.
Nanotechnology Research with Jadoo Tech Inc. : 2019 :
Undergraduate student apprentice for Nanotechnology lab mainly focused on
electrical hardware and software design and
statistical data analysis. After various testing sequences,
we will be moving into electrochemical reactions and data analytics. (PYTHON)
Build Your Own World Project with University of California Berkeley : 2019 :
This is a large design project entailing an engine for generating
explorable worlds. Additionally, we created a game aspect to exploring
the worlds by incorporating interactivity and user interfaces. (JAVA)
NBody Simulation Project - University of California Berkeley : 2019 :
A program simulating the motion of N objects in a plane,
accounting for gravitational forces mutually affecting each
object as demonstrated by Newton. The project will draw an
animation of bodies floating around in space tugging on each
other with the power of gravity.(JAVA)
BearMaps Project - University of California Berkeley : 2019 :
A system inspired by Google Maps, the project programming
utilizes K-d trees, ExtrinsicPQs, and A* Algorithms to
emulate a working map. (JAVA)
Economic Scholar Citations Analysis - University of California Berkeley : 2019 :
An analysis of various aspects of the google scholar citations
data gathered from web scraping the respective scholar profile pages
of Abhijit Banerjee and Ester Duflo, two Nobel Laureate
Winners from the same decade. (R)
Financial Investment Projections Shiny Web App- University of California Berkeley :
2019 : This Shiny App takes in several variants to display a timeline
graph of the cumulative investment over time. Taking in various
outputs, the app displays a reactive timeline plot of different
modes of investment. (R)
Various projects regarding data analysis and object-oriented
programs and emulators.
Special Relativity Essay - International Baccaleaureate : 2018 :
A 4000-word in depth analysis of modern relativity theory
and experiments that test its limits, centered at the
question: "To what extent is Einstein's time dilation postulation valid?"
Data Analysis with Rutherford Gold Foil Simulation - KyungHee University : 2018 :
Analysis of Geant4 10000 simulated electron pathways to emulate the
Rutherford Gold Foil Experiment. ODE and python analysis. (PYTHON)
Yelp Map - University of California Berkeley : 2018 :
A visualization of restaurant ratings of Berkeley using
machine learning and the Yelp academic dataset. Berkeley
is segmented into regions, where each region is shaded by
the predicted rating of the closest restaurant (yellow
is 5 stars, blue is 1 star) in a Voronoi diagram. (PYTHON)
Ants - University of California Berkeley : 2018 :
This large-scale project revolves around the back-end
development of a tower defense game, Ants Vs. SomeBees.
This project combines functional and object-oriented
programming paradigms. (PYTHON)
Scheme - University of California Berkeley : 2018 :
An interpreter for a subset of the scheme
language, as well as several smaller programs in scheme.(PYTHON, SCHEME)
2017 projects consist of summer research at Yale University.
Orbital Optimization of Near-Earth Asteroid YP11 - Yale University : 2017 :
Month analysis and future modelling and projection of astronomical asteroid
pathways data calculated from telescope observations.
(PYTHON)


Technologist with a Passion for coaching & developing people. Creative and entrpreneurial leader with a passion for hiring, developing, coaching/mentoring top talent, fostering a culture of innovation, empowerment & trust leveraging diversity. Avid and fast learner keen to explore new areas of technology and human experience.
Skilled in defining and driving software strategy and vision in partnership with ecosystem within & external to the organization. Experience in product development for the healthcare & consumer electronics industries.
An engineer at heart & an opinionated technologist constantly learning and experimenting with new ideas and approaches.
Software Architecture · Modern C++ · Python · ML/AI · Middleware for self-driving Vehicles · Reprocessing and Simulation · Coaching and Mentoring · TDD and Lean/XP Practices · Design and Code reviews
Technical due-diligence, Proof-Of-Concet · Prototyping /MVP · ML/AI · SW Roadmapping & Strategy · PWA and Mobile App development · Ionic, Firebase ·
Agile Transformation · Organization Design · Global Functional Leadership · Engineering Excellence · SW Roadmapping & Strategy · Vendor Management · Cross-Functional Initiatives · Coaching & Mentoring
Functional Leadership · Engineering Excellence · People Management · Regulatory Compliance · Coaching & Mentoring
Program Management · Medical Imaging · Regulatory Compliance · Coaching & Mentoring · Risk Management
Digital TV · Software Project Management · People Management · Stakeholder Management · Coaching & Mentoring · Product Roadmapping & Development · Cross-Functional Leadership
Embedded Systems · Digital TV · Set-top Box · Semiconductors · SDLC · Software Design · Software Development · Digital Signal Processing
GPA: 7.83
Score: 93%
Other than reading (fiction, non-fiction and philosophy), I love learning new languages (human / computer). I am a trained classical percussionist and I'm teaching myself to play the Piano.
I like to stay informed technolgy advancements in the web & mobile development world. In my free time I create software and contribute to projects that capture my fancy.
A Rich and Featured Cross-Platform mobile/ web-app for managing contacts. Supports sign-in with Google, offline use and syncs data securely to the cloud.
See it in action!A Companion in learning multiplication tables. In terms of architecture, this is an attempt to unify Ionic and PWA to provide maximum flexibility in deployment.
See it in action!Obstacle detection on the road and avoidance algorithm for self-driving car. The trajectory planner choose a free lane and plans a path for the vehicle while ensuring ride comfort.
Read the report


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Sort by ID
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stateUser.name
User ID: stateUser.id
User age: stateUser.age
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|
QuantLib: a free/open-source library for quantitative finance
Fully annotated sources - version 1.30
|
Garman-Klass volatility model. More...
#include <ql/models/volatility/garmanklass.hpp>
Public Member Functions | |
| GarmanKlassAbstract (Real y) | |
| TimeSeries< Volatility > | calculate (const TimeSeries< IntervalPrice > "eSeries) override |
| Public Member Functions inherited from LocalVolatilityEstimator< IntervalPrice > | |
| virtual | ~LocalVolatilityEstimator ()=default |
| virtual TimeSeries< Volatility > | calculate (const TimeSeries< IntervalPrice > "eSeries)=0 |
Protected Member Functions | |
| virtual Real | calculatePoint (const IntervalPrice &p)=0 |
Protected Attributes | |
| Real | yearFraction_ |
Garman-Klass volatility model.
This class implements a concrete volatility model based on high low formulas using the method of Garman and Klass in their paper "On the Estimation of the Security Price from Historical Data" at http://www.fea.com/resources/pdf/a_estimation_of_security_price.pdf
Volatilities are assumed to be expressed on an annual basis.
Definition at line 41 of file garmanklass.hpp.
|
explicit |
Definition at line 47 of file garmanklass.hpp.
|
protectedpure virtual |
Implemented in GarmanKlassSimpleSigma, ParkinsonSigma, GarmanKlassSigma4, and GarmanKlassSigma5.
|
overridevirtual |
Implements LocalVolatilityEstimator< IntervalPrice >.
Reimplemented in GarmanKlassOpenClose< GarmanKlassSimpleSigma >, GarmanKlassOpenClose< ParkinsonSigma >, and GarmanKlassOpenClose< GarmanKlassSigma4 >.
Definition at line 49 of file garmanklass.hpp.
|
protected |
Definition at line 44 of file garmanklass.hpp.


This is Words and Buttons Online — a collection of interactive #tutorials, #demos, and #quizzes about #mathematics, #algorithms and #programming.
Check if your performance intuition still works with CUDA
An interactive quiz about microoptimizations in CUDA. 10 rounds, two pieces of code per each, you get to guess which is the faster.
#programming #quizzes
Challenge your performance intuition with C++ sine
One more interactive quiz. This time, it's all about the sine function. Which one is faster and when?
#programming #quizzes
Yet another alternative to floating-point numbers
This shows how computable intervals written in rational bounds may not only account for the input error but keep computational error under control as well.
#mathematics #programming #quizzes
Challenge your performance intuition with nanosecond sorting
And yet another interactive quiz where you get to estimate the performance difference between several variants of the same code.
#programming #quizzes
Using logical operators for logical operations is good
This is the follow-up for the “Challenge your intuition with C++ operators”. It shows that despite the occasional gains from a better compilation, using the proper operators is still beneficial in the long run.
#programming #quizzes
Challenge your performance intuition with C++ operators
Another interactive quiz where you get to estimate the performance difference between several variants of the same code.
#programming #quizzes
Can you tell an assembly language when you see one?
An interactive quiz featuring several obscure high-level languages and assembly variants.
#programming #quizzes
Challenge your performance intuition with C++ magic squares
An interactive quiz where you get to estimate the performance difference between several variants of the same code.
#programming #quizzes
It's a C test. If you think you know C, take this test. It only has 5 questions.
#programming #quizzes
A short quiz to reveal your hidden knowledge of Lisp.
#programming #quizzes
| All the source code for Words and Buttons is available on GitHub. | ||
| There is also RSS with all the news. | ||
|
I also wrote a book. It's called Geometry for Programmers and it is exactly what is says in the title. It's about splines, voxels, transformations, all in the context of efficient programming.
Here's why: why.html. |


Represents n-gram object.
Wraps filter functions that can be applied on NGram statistics.
Customizes collections partitioning so NGrams with the same prefix are shuffled to the same partitions.
Set of implicit conversions.
Predefined filters.
N-gram statistics ordering factories.
N-gram statistics collections functionality.
N-grams related functionality. For statistics (frequencies, probabilities) see ch.uzh.cl.slmlib.ngrams.statistics


Nuclear proliferation is one of the greatest risks to our national and global safety. Threats to safeguarding these nuclear materials include preventing the unintentional or accidental use of nuclear weapons. We also need to be aware of intentional use from terrorist acquisition or crisis escalation resulting in nuclear use with nations we are at odds with.
The nuclear weapons security sector is in need of an influx of new ideas, new perspectives, and inventive thinkers to solve a problem that is too often ignored.
The following is a Virtual Reality demonstration built for Google Cardboard to simulate the massive threat nuclear weapons proliferation poses.


Course enquiries
*Calling us on an 0300 number will cost you the same as a call to a standard landline number starting with 01 and 02.
If you have an inclusive call package for calling landline numbers on your mobile or landline telephone the call would simply be reduced from your available talk minutes. Otherwise your call will be charged at the same rate as calling a standard national rate number.
University reception
The University's reception handles general enquiries and visitors to the University. The address for the reception is:
University of Exeter Reception
Stocker Road
Exeter EX4 4PY
Telephone: +44 (0) 1392 661000
Access information for the main reception is available on the AccessAble website.
Postal address
General mail for the University should be addressed to:
The University of Exeter
Mail Room, The Old Library
Prince of Wales Road
Exeter, Devon UK
EX4 4SB
Website feedback
We welcome questions or comments on any matter regarding our website. These comments should be sent to the Web Editor.


Schedule your meals in 2 minutes
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Track your calories
Get insights of your daily and weekly calorie intake
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Keep money in your digital wallet and checkout faster
Subscribe to gerryeats boxes
Subscribe reusable boxes to save ₹20 of packaging cost on every meal and minimize the use of single use plastic.
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$sceDelegateProvider
ng
The $sceDelegateProvider
provider allows developers to configure the $sceDelegate
service. This allows one to get/set the whitelists and blacklists used to ensure
that the URLs used for sourcing Angular templates are safe. Refer $sceDelegateProvider.resourceUrlWhitelist
and
$sceDelegateProvider.resourceUrlBlacklist
For the general details about this service in Angular, read the main page for Strict Contextual Escaping (SCE)
.
Example: Consider the following case.
http://myapp.example.com/
http://srv01.assets.example.com/
, http://srv02.assets.example.com/
, etc.http://myapp.example.com/clickThru?...
.Here is what a secure configuration for this scenario might look like:
angular.module('myApp', []).config(function($sceDelegateProvider) { $sceDelegateProvider.resourceUrlWhitelist([ // Allow same origin resource loads. 'self', // Allow loading from our assets domain. Notice the difference between * and **. 'http://srv*.assets.example.com/**']); // The blacklist overrides the whitelist so the open redirect here is blocked. $sceDelegateProvider.resourceUrlBlacklist([ 'http://myapp.example.com/clickThru**']); });
Sets/Gets the blacklist of trusted resource URLs.
| Param | Type | Details |
|---|---|---|
| blacklist (optional) | Array | When provided, replaces the resourceUrlBlacklist with the value provided. This must be an array or null. A snapshot of this array is used so further changes to the array are ignored. Follow The typical usage for the blacklist is to block open redirects served by your domain as these would otherwise be trusted but actually return content from the redirected domain. Finally, the blacklist overrides the whitelist and has the final say. |
| Array | the currently set blacklist array. The default value when no whitelist has been explicitly set is the empty array (i.e. there is no blacklist.) |
Sets/Gets the whitelist of trusted resource URLs.
| Param | Type | Details |
|---|---|---|
| whitelist (optional) | Array | When provided, replaces the resourceUrlWhitelist with the value provided. This must be an array or null. A snapshot of this array is used so further changes to the array are ignored. Follow Note: an empty whitelist array will block all URLs! |
| Array | the currently set whitelist array. The default value when no whitelist has been explicitly set is |


Dosa
A dosa is a rice pancake, originating from South India, made from a fermented batter. It is somewhat similar to a crepe in appearance. Its main ingredients are rice and black gram, ground together in a fine, smooth batter with a dash of salt. Dosas are a common part of the diet in South India, but has become popular all over the Indian subcontinent. Traditionally, dosas are served hot along with sambar and chutney.
Idly
Idli or idly (About this soundpronunciation (help·info)) (/ɪdliː/) are a type of savoury rice cake, originating from the Indian subcontinent, popular as breakfast foods in Southern India especially Tamilnadu and among Tamils in Sri Lanka. The cakes are made by steaming a batter consisting of fermented black lentils (de-husked) and rice. The fermentation process breaks down the starches so that they are more readily metabolized by the body. Idli has several variations, including rava idli, which is made from semolina. Regional variants include sanna of Konkan.
Poori
Puri bhaji (sometimes spelled poori bhaji) is a dish, originating from the Indian subcontinent, of puri (deep-fried rounds of flour) and aloo (potato) bhaji (a spiced potato dish which may be dry or curried).[1] It is a traditional breakfast dish in North India. Fresh puris made at home Many Indian households prefer puri bhaji and other traditional dishes over cereals for breakfast.[3][4] Some serve it for lunch along with condiments such as dahi (yogurt) and salad.[5] In central India, puri bhaji is served as a street snack.[6] Puri bhaji is a vegetarian dish and is popular in India because it is relatively inexpensive and tasty.[citation needed] The dish is also served on railway platforms in India[7] and is served as a packed lunch on trains along with pickle.[8] Puri bhajican also be served with halva.


No person in the world can ever tell when a person would leave us, it just happens I guess, being in love makes time run fast, making the days pass like a split second but the moment they leave us the days seem to feel like years without them by our sides, funny how we are only able to aprreciate the things we onced had until they are gone
So be thankful and enjoy the time you have left with them, maximize and love with no regret and never let go until you still can hold on.


Whether it's Adblock Plus, uBlock Origin, or some other content blocker, having an ad blocker on is always a good idea. It increases the security on your computer by blocking potentially malicious ads and other undesirable content, and makes websites look and feel cleaner all while making them load faster, among other things. However, some websites don't like when you block their ads, and in return, they don't allow you to see their content. But what if you want to read those articles without compromising your security or convenience? This tutorial is here to help. I'll update it if I come across new methods to get around the problem.
If any method works for you, you don't really need to read the rest.
Some websites detect adblockers by running some javascript, so disabling it may fix your issue.
Each browser is different, but in my main browser, Safari, it is done by first enabling the Devloper menu. This is done by going to Safari Preferences (Safari menu bar button, then preferences), then to Advanced, then ticking the checkbox that says "Show Develop menu in menu bar". Close that window, then in the menu bar, open the Develop menu, and click on Disable Javascript. When you're done with the website, re-enable Javascript by going back to the Develop menu, and clicking Disable Javascript again.
You can follow this WikiHow article to learn how to disable javascript in some other browsers.
On Forbes.com and probably other websites, all you have to do is disable CSS and click the right link.
Each browser is different, but in my main browser, Safari, it is done by first enabling the Devloper menu. This is done by going to Safari Preferences (Safari menu bar button, then preferences), then to Advanced, then ticking the checkbox that says "Show Develop menu in menu bar". Close that window, then in the menu bar, open the Develop menu, and click on Disable Styles. Next, (on Forbes), wait for the "disable AdBlock" popup to appear, and click "continue to site". Then you can re-enable CSS by going back to the Develop menu, and clicking Disable Styles again.
Sometimes, all you have to do is delete the offending part of the website. Right click on the page, and click "Inspect Element". Every modern browser should have a similar function. Inspect Element will come up, and you have to select the "Select" tool. It usually looks like a crosshair or a mouse by a window. Hover over the overlay, and it should light up. Click it. Inside Inspect Element, some HTML should now be highlighted. Click the delete key on your keyboard. If it doesn't work, try deleting other things that may be part of the overlay. Make sure you don't delete the main content!
I'm not sure if other blockers have this, but AdBlock Plus has a Block Element feature that acts similarly to the Inspect Element method I just talked about. Click the "Block Element" button in the AdBlock Plus menu and then click on the overlay. A new tab should open up asking if you want to block that element. Say yes. You can undo it later by going to the "Options" section under the AdBlock Plus menu, then to "Add your own filters", and from there, removing the blocked elements from the list.
Hopefully my methods worked for you! I'll update the guide if I discover a new way to get around the AdBlock blockers. If you like these tutorials or you want to hear more from me, please follow me on Twitter @JustinAlexP.


sagrave.t_cards
Global Attributes
None
Classes
t_cards
Functions
None
t_cards
Derived from
None
Class Attributes
tbName
Class Methods
None
Methods
t_cards
editCards
loadCards
Static Methods
None
t_cards (Constructor)
t_cards
(
theWin, theConn, theApp
)
t_cards.editCards
editCards
(
)
t_cards.loadCards
loadCards
(
)
Up


--- layout: post title: "ASP.NET Routing, DataBinding and WebForms" postId: 34 ---
Update 13/09/08 I've posted a new version of the sample which is compatible with .NET 3.5 SP1. It can be found here.
One of the great things about ASP.NET MVC is the automatic databinding support for routing. For example, if you define a route like this:
routes.Add(new Route("{controller}/{action}/{id}", new MvcRouteHandler()) { Defaults = new RouteValueDictionary(new { action = "Index", id = (string)null }) });
...and you have a controller like this:
public class HomeController : Controller { public ActionResult Show(int id) { // do stuff return View(); } }
...then visiting a url like mysite.com/Home/Show/5 will automatically popuate the id parameter of the Show action with the value 5.
I thought it would be fun to see if I could get something similar working using WebForms. It actually turned out to be quite straightforward.
First, you'll need to set up ASP.NET Routing to work with WebForms. I'm using Phil Haack's WebFormRouting library which can be found here.
I've added an extension method to RouteCollection that allows me to declare routes like this:
RouteTable.Routes.MapWithAutoBinding("Customer/{CustomerId}").To("~/Customer.aspx");
In the code behind file for my customer.aspx page I can now declare a property like this:
public partial class Customer : Page { protected void Page_Load(object sender, EventArgs e) { customerNameLabel.Text = CustomerId; } [DataBind] public string CustomerId { get; set; } }
So when I visit MySite.com/Customer/Jeremy the "CustomerId" property on the Customer.aspx page will automatically be set to "Jeremy".
Calling RouteTable.MapWithAutoBinding will create a route using the AutoBoundWebFormRouteHandler which inherits from Phil Haack's WebFormRouteHandler. After GetHttpHandler is called, reflection is used to find any properties on the page that have a DataBindAttribute.
The RequestContext is then passed to the PerformBinding method on the DataBindAttribute, which will find the appropriate value from the RouteData and invoke the property's setter.
Just for fun, I thought I'd see if it was possible to automatically fetch an entity from the database based on the value in the RouteData in a similar way to Monorail's ARDataBind attribute. I thought I'd try and use Linq to SQL as I haven't used this in a project before.
The first stage was to inherit from the DataBindAttribute class and override the PerformBinding method. Now, instead of just passing the value from the RouteData to the property, we have to run a linq query using the value from the route data as the primary key. This is very easy to do thanks to the Dynamic Linq provider that Microsoft ship in the Visual Studio 2008 samples folder.
So, now the property on the Customer.aspx page can look like this:
[LinqBind("CustomerId")] public Customer Cust { get; set; }
The "CustomerId" being passed into the LinqBind attribute tells the binder the name of the RouteData value it should use as the primary key for the Customer entity. Currently, this only works with single primary keys, although it should be possible to get it work with composite keys too.
Visiting mysite.com/customer/ALFKI will now run a linq query to load a customer from the Customers table using the customer id of "ALFKI" and then set the "Cust" property to be a reference to this entity.
It is also necessary to tell the LinqBindAttribute which DataContext should be used to do the fetching. This can be set in the Global.asax's Application_Start method using a delegate:
LinqBindAttribute.DataContextFactory = () => new MyDataContext();
I've uploaded the code here if anyone wants to play with it.
Disclaimer: The zip file contains Phil Haack's WebFormRouting.dll and Microsoft's Dynamic Linq provider from the Visual Studio 2008 samples.


Research
Here is a non-exhaustive list of my research projects.
Unsupervised learning for medical image denoising without high-quality images
Currently most neural network-based medical image denoising methods require matched or unmatched high-quality images as reference during training, which are inaccessible under certain circumstances such as dynamic imaging. Based on the recently proposed Noise2noise method, we proposed to train denoising network from noisy measurement only by splitting the measurement into two sets and mapping the reconstructed images from one set to the other. Theoretical analysis was done which indicated the feasible conditions of the Noise2noise scheme. We further proposed a novel consensus loss function based on the theory to better utilize both noisy measurements.
4x undersampled MR image reconstruction. The proposed method achieved best performance among all the unsupervised methods which do not require fully sampled images for training or testing. The Noise2clean required fully sampled images during training.
Wu, D., Gong, K., Kim, K. and Li, Q., 2019. Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples. arXiv preprint arXiv:1906.03639.
Iterative Low-dose CT reconstruction with unsupervised manifold learning via k-sparse autoencoder
A k-sparse autoencoder was built to learn the manifold of normal-dose CT images in an unsupervised manner. Then objective function was built concerning both data fidelity and distance to the manifold. The objective function was solved via alternative optimization of the data fidelity part and the manifold part, which was a monotonic algorithm given appropriate step size. K-sparse autoencoder was built on patches and trained greedily by keeping only the k largest encodes at each training iteration. Furthermore, the k-sparse autoencoder demonstrated superior edge preservation performance compared to normal autoencoders and L1-sparse autoencoder.
Left: concept of manifold for normal-dose CT images; Right: The structure of patch-based k-sparse autoencoder.
Wu, D., Kim, K., El Fakhri, G. and Li, Q., 2017. Iterative low-dose CT reconstruction with priors trained by artificial neural network. IEEE transactions on medical imaging, 36(12), pp.2479-2486.
Undersampled image reconstruction with computational efficient neural network
One way to do image reconstruction with deep neural networks is unrolling iterative algorithms to finite steps and parameterize prior-related terms with trainable neural networks. By taking the measurement into consideration, the unroll approach achieved promising performance in undersampled problems such as few-view, limited-angle and low-dose. However, it requires significant amount of memory and training time for 3D volume reconstruction which is not practical for most current hardware. We proposed a computationally efficient training approach which broke the learning into sequential image-domain trainings and significantly reduced the computational cost. The estimated memory cost was reduced from 417 GB to 2 GB for a CT reconstruction problem with ordinal-size image (640*640*128). It was also demonstrated the proposed method could achieve similar performance to unrolled network.
(a) Unrolled method trained all the parameters based on the final image, which required the full image as input for training, leading to excessive computational cost; (b) Our network trained the parameter at each unroll sequentially in the image domain utilizing patch-based training.
Wu, D., Kim, K. and Li, Q., 2019. Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction. Medical physics.
Low-dose dual energy CT reconstruction via deep neural networks
Dual energy CT (DECT) is generally not feasible for very large patients due to the heavily attenuated photon flux under 80kVp. This project investigated the deep neural network-based image reconstruction for low-flux scenarios of DECT. Normal-dose and half-dose DECT scans were acquired for each of 40 patients under IRB approval. Noise was inserted to the normal-dose raw data to simulate low-dose scans and a learned primal-dual network was trained with L2 norm on 30 patients. The network was tested on the rest 10 patients’ real half-dose scans and achieved significantly reduced noise without bias.
Reconstructed images. Learned prima-dual significantly reduced noise while maintaining the texture. The half-dose scans were done right after the normal-dose scan for each patient, so the contrast had different distribution in the liver.
Wu, D., Kim, K., Kalra, M.K., De Man, B. and Li, Q., 2019, May. Learned primal-dual reconstruction for dual energy computed tomography with reduced dose. In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Vol. 11072, p. 1107206). International Society for Optics and Photonics.
Task-based image reconstruction for lung nodule detection
Current image reconstruction is optimized for visual quality of the images to radiologist. However, computer aided diagnosis system could rely on very different features for automatic lesion detection than human observers. We built a lung nodule detection network whose input was the sinogram rather than the images. The deep neural network consisted of a learned primal-dual reconstruction network and 3D CNN detection network. Improved FROC performance was achieved with the task-based image reconstruction on simulated sparse-view sinograms compared to reconstruction optimized for L2 norm loss on images.
FROCs of different methods under various noise level. The reference was nodule detection on fully sampled images. The task-based reconstruction (end-to-end) achieved substantially better FROC compared to FBP and reconstruction optimized for L2 loss on images (two-step).
Wu, D., Kim, K., Dong, B., El Fakhri, G. and Li, Q., 2018, September. End-to-end lung nodule detection in computed tomography. In International Workshop on Machine Learning in Medical Imaging (pp. 37-45). Springer, Cham.


Reproducible & Collaborative Data Science: An Introduction
Carl Boettiger
So, what is data science anyway?
Statistics on a Mac
What is Data Science?
“The Two Cultures?” Statistics or Machine Learning
Donoho’s “Greater Data Science”
- Data Exploration and Preparation*
- Data Representation and Transformation*
- Computing with Data
- Data Modeling
- Data Visualization and Presentation
- Science about Data Science*
Computational Science
- HPC-centric but not software-centric
- Ed Lazowksa: “will future of academic data science be like computer science or computational science?”
Big Data
- Big Data = stuff breaks (3 Vs)
- Volume: Breaks your RAM, your harddrive
- Velocity: Breaks your network cable
- Variety: Breaks everything else
- Machine learning
Reproducible research
More to say later, but likewise a movement both distinct from and largely pre-dating “Data Science” but now very much part of ‘greater data science’ vision.
Open Science
- Open Access
- Open Data
- Open Code
Colloborative & Reproducible
- Collaboration & Reproducibility are core elements of “Greater” (sensu Donoho) Data Science.
Collaborative & Reproducible
Collaborative
You will:
- Learn by doing
- Work in pairs on all assignments
- Critique, edit & extend work of other pairs
- Learn from everyone here and beyond
Collaborative
So what’s to be learned? Don’t we already know how to collaborate?
- Yourself, \(n\) months from now
- Colleagues in the same room
- Remote & asynchronous collaborators
- Anonymous collaboration: scientific publishing, open source software, forums, etc
Involves:
- Technical skills
- Communication skills: code as prose
Reproducible
Fundamental to science…
Reproducible
… But not primarily for that reason.
Reproducible
- Replication is fundamental to understanding & extending existing work
- Most analyses today have lots of free parameters
- Making efficient to explore the road(s) not taken
Course Pedagogy
Some things in this course are in keeping with good / leading pedagogy
- We will start with graphs first
- active learning, flipped classroom model
And some things aren’t
- Starting on the deep end
- We will start with untidy data
- unstructured training
Course Content: Data Science as Process
Data cleaning
Analysis & Modeling
- Traditionally the 80%
- Computational implementations have made this much easier
- And led to an explosion of new methods & tools
Intentional Proccess = Intentional Programming
Course motto
Write code for people. Write data for computers
– Vince Buffalo, UC Davis c 2015
- a key difference in perspective between computational science and data science.
- central to our themes of reproducibility and collaboration.
Final Project
- Will also be collaborative (pairs).
- You can do one or two separate analyses, but both partners must have input on everything
- Hopefully relevant to your thesis research
- Start thinking about projects early
Language: Why R?
- Not about the technical attributes
- Pick your language based on what people in your area speak/code
- Python vs R: closely analogous feature set; lots of interesting differences to discuss over beer
Why tidyverse?
- tidyverse manifesto
- Consistent design, parallels tools in other languages (Python/pandas, SQL)
- Significant adoption in the data science community
- Pick your language based on what people in your area speak/code
Coding Style
Write code for people
Modules
- Global Climate Change (Data exploration & visualization)
- Popluation Dynamics (Programming: functions, loops)
- Fisheries Collapse (databases)
- Extinctions (APIs, regex)
- Final project
Module flow
- Week 1: Introduction, live code examples, getting started
- Week 2: Pair work on assignment; mini-tutorials
- Week 3: Present / compare / discuss solutions
Climate Change
- Data parsing
- Data visualization
- Basic data wrangling
- Workflow
Population Dynamics
- programming: loops, conditionals, functions
- ecological simulation
- dynamical systems modeling, ODE & SDE simulations, bifurcation analysis, chaos, bet hedging
Fisheries Collapse
- Working with multiple tables/tibbles
- SQL databases, relational data
Extinctions
- Online data, REST APIs
- JSON & extensible data formats
- Regular Expressions (RegEx)
RMarkdown
Some history:
- Literate programming, Knuth’s
noweb
, weave/tangle
- Sweave, S & R
- knitr
- markdown, RMarkdown, RStudio
Getting Help
- Instructor, GSI, colleagues
- Course Discussion list: https://github.com/ds421/discussion/issues
- Googling error messages
- StackOverflow
- Interacting with tool developers: GitHub, gitter, etc
- Debugging strategies (more later)
Workflow & naming things
- The two hard problems in computer science
GitHub/RStudio test drive
- Confirm you have a respository on course GitHub, e.g. https://github.com/ds421/firstname.lastname
- If repo does not have a README, click to add a default README file.
- Open an issue on your own repository for “test drive”
- Launch RStudio
- From RStudio menu, open a new project from this existing git repository.
- Create a new RMarkdown document (use GitHub template)
- Knit your RMarkdown file
- Commit changes mentioning
closes issue #1
, and push back to repository


Main Page
Related Pages
Modules
Classes
Class List
Class Index
Class Members
JRFlashMediaObject Member List
This is the complete list of members for
JRFlashMediaObject
, including all inherited members.
expanded_height
JRFlashMediaObject
expanded_width
JRFlashMediaObject
height
JRFlashMediaObject
imgsrc
JRFlashMediaObject
-
initWithSwfsrc:andImgsrc:
JRFlashMediaObject
swfsrc
JRFlashMediaObject
width
JRFlashMediaObject


######################## BEGIN LICENSE BLOCK ######################## # The Original Code is Mozilla Communicator client code. # # The Initial Developer of the Original Code is # Netscape Communications Corporation. # Portions created by the Initial Developer are Copyright (C) 1998 # the Initial Developer. All Rights Reserved. # # Contributor(s): # Mark Pilgrim - port to Python # # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public # License as published by the Free Software Foundation; either # version 2.1 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this library; if not, write to the Free Software # Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA # 02110-1301 USA ######################### END LICENSE BLOCK ######################### from .enums import ProbingState from .charsetprober import CharSetProber class CharSetGroupProber(CharSetProber): def __init__(self, lang_filter=None): super(CharSetGroupProber, self).__init__(lang_filter=lang_filter) self._active_num = 0 self.probers = [] self._best_guess_prober = None def reset(self): super(CharSetGroupProber, self).reset() self._active_num = 0 for prober in self.probers: if prober: prober.reset() prober.active = True self._active_num += 1 self._best_guess_prober = None @property def charset_name(self): if not self._best_guess_prober: self.get_confidence() if not self._best_guess_prober: return None return self._best_guess_prober.charset_name @property def language(self): if not self._best_guess_prober: self.get_confidence() if not self._best_guess_prober: return None return self._best_guess_prober.language def feed(self, byte_str): for prober in self.probers: if not prober: continue if not prober.active: continue state = prober.feed(byte_str) if not state: continue if state == ProbingState.FOUND_IT: self._best_guess_prober = prober return self.state elif state == ProbingState.NOT_ME: prober.active = False self._active_num -= 1 if self._active_num <= 0: self._state = ProbingState.NOT_ME return self.state return self.state def get_confidence(self): state = self.state if state == ProbingState.FOUND_IT: return 0.99 elif state == ProbingState.NOT_ME: return 0.01 best_conf = 0.0 self._best_guess_prober = None for prober in self.probers: if not prober: continue if not prober.active: self.logger.debug('%s not active', prober.charset_name) continue conf = prober.get_confidence() self.logger.debug('%s %s confidence = %s', prober.charset_name, prober.language, conf) if best_conf < conf: best_conf = conf self._best_guess_prober = prober if not self._best_guess_prober: return 0.0 return best_conf


Serena is a Professor of Astronomy at Leiden Observatory at Leiden University and leads the MOPPEX project. She also holds a position at University College London.
Her research interests lie within the field of astrochemistry and galactic and extragalactic star formation studies, both by observations and modelling, with emphasis on the use of chemical and dynamical models to study the clumpy nature of the ISM, and star forming regions. See her publication list for more details.
Jon is a PDRA at Leiden Observatory at Leiden University. His research generally includes the use and development of models and stastical tools to understand chemistry in extragalactic and star-forming environments.
Jon also works part time as a data scientist for a major UK hospital, borrowing data analysis techniques from his research to provide operational support and clinical alerts.
Monica is a Taiwanese citizen and an astronomer. She currently works as a postdoctoral researcher at Leiden University. Her main focus is to investigate the chemical & physical properties in nearby galaxies with interferometric observations from ALMA, along with modeling analysis.
She used to work with spectral-line polarization data from ALMA, VLA, and CARMA to study the magnetic field morphology around star-forming regions and the circumstellar envelope of AGB stars.
Photo credit: Nina Chanlin
He is a PhD student at Leiden Observatory, working with Serena. His work involves using Molecular Line Ratios to investigate the ISM in multi-component galaxies, currently NGC 1068. He is accomplishing this by comparing Archival line intensity detections for many different molecules and transitions of varying spatial resolutions.
He completed his Master’s at Lancaster University, investigating the co-evolution of Supermassive Black Holes and Galaxies in both Bulgeless and Classical Bulge Galaxies via Bayesian Inference of CLOUDY models on X-ray Data..
Damien is a final year PhD student and member of the University College London Centre for Doctoral Training in data intensive science. His research focuses on applying data-driven techniques to astronomy problems. His most recent line of work involves developing techniques for extracting the chemical content of stellar spectra. Outside of astronomy, he has also worked on machine learning explainability.
Johannes is a second-year PhD student based in the Centre for Doctoral Training in Data-Intensive Science at UCL. His research focusses on developing accelerated Bayesian inference techniques to determine the rate parameters of grain-surface reactions for large chemical networks.
Johannes also has an interest in applying data science techniques to other areas, having worked with the Office of National Statistics to determine what insights about what insights can be gathered from Twitter data and will be working with the NHS as part of the Getting It Right First Time (GIRFT) programme.
Tom is a final year PhD student in the astrophysics group at University College London. His research spans both computational and observational astrophysics applied to molecular gas in the Milky Way. In particular, Tom is interested in the star forming capabilities of gas in the central few pcs of the Milky Way as a probe of the gas conditions and statistics as proxies of star forming efficiencies of gas in similar regions of external galaxies.
Prior to his PhD, Tom was an undergraduate at Cardiff University and a software developer in industry. He is also an active outreach scientist and regularly disseminates his work to school students through the ORBYTS scheme, the public through local talks, and prisoners as part of their rehabilitation via the BounceBack Project.
Marcus is a second year PhD student at University College London. He focuses on creating statistical tools and machine learning algorithms to be used in modelling astrochemical objects and comparing them to observations. His work is part of an ITN called “Astrochemical Origins” (ACO), a large-scale project aiming to unveil the early history of the solar system, while training a new generation of researchers.
Marcus’s interests in machine learning span beyond just their applications, and goes into the engineering of them, and how to improve the performance through the use of GPU as well. To further develop in those areas, he will be working with Mellanox as part of a secondment.
Louise is a 1st year PhD candidate at Leiden Observatory. She completed her MSci in Physics and Astronomy at the University of Glasgow in 2020.
Her main interests include the physics surrounding AGNs, galactic outflows and jets, and her thesis project currently focuses on using ALMA CO observations to study the kinematics of the nearby Seyfert galaxy NGC 1068.
Ross is a second year PhD student at University College London. His research focuses on the early stages of low mass star formation, understanding the processes at work and their chemical significance.
His PhD is part of an ITN called "Astrochemical Origins" (ACO), a large-scale project aiming to unveil the early history of the solar system and train a new generation of researchers. His role within ACO is the development of astrochemical models and using these to determine the physical characteristics of a developing system.
Matt is a third year PhD student, working with Serena Viti and Amélie Saintonge at University College London. His research interests include the use of molecular line emission to investigate the ISM, having previously worked with high resolution CS maps to compare the physical conditions in different sub-regions of the Seyfert galaxy NGC 1068.
As a part of the DESI collaboration, Matt is currently using machine learning techniques to extract information about the ISM of galaxies in the survey.


{% extends "layout.html" %} {% block content %}
Algebra tiles are great tools for teaching students how
to look for patterns and make predictions.
For instance, a great task would be to ask students to draw
the missing figures and write a general equation describing
how the pattern changes.
| Fig. {{ x }} | {% endfor %}|
|---|---|
| {% else %} | {% endif %} {% endfor %} |
This app has been designed to make the creation of such patterns
easy so you don't have to manually draw the tiles.
With a simple text-based input to represent algebra tiles, you can
quickly roll out worksheets for students to work with.
The only restriction is that unit side lengths cannot be matched
with variable side lengths. (i.e.
| Fig. {{ x }} | {% endfor %}
|---|
| {% endfor %} |


Elizabeth (1998)
A Film Review by Mark O'Hara
Visit Online Film Critics Society at http://www.ofcs.org
What provides 'Elizabeth' with a good deal of tension? As a viewer, it is not being sure that the woman who lends her name to the period will survive.
We know she flourished, of course, but the early days of her reign were assailed by divers types of intrigues: social, political, even sexual. And it is always fascinating to follow a figure when failure is not yet an impossibility.
We are quickly introduced to the religious persecution that went on in Great Britain in the 1550's. Three Protestants are brutally shaved and tied to a mammoth stake, the square filled with onlookers. A bishop of the Roman Church presides at the spectacle, and his very words, directed to the victims of the impending auto-de-fe, show the terrible results of politics mixing with religion.
Princess Elizabeth appears first outdoors, dancing with her ladies in waiting. But she soon suffers the wrath of her half sister, Mary Tudor, the ailing Queen. Imprisoned and then released, Elizabeth is warned not to consort with anyone perceived as dangerous to the Crown. Various factions form, a Catholic one led by the Duke of Norfolk (Christopher Eccleston), a Protestant one led by Sir William Cecil (Sir Richard Attenborough) and Sir Francis Walsingham (Geoffrey Rush). There are other players in the political intrigue, such as Mary of Guise (Fanny Ardant), and various ambassadors and underlings. After Elizabeth ascends the throne, she continues her affair of the heart with Sir Robert Dudley (Joseph Fiennes), but the reality of ruling sobers her attitude considerably. The realm's coffers are depleted, its army and navy ineffectual. France and Spain are enemies waiting for a chance to consume the entire island. Does she have it in her to transform her callow and impish demeanor? She is the daughter of Henry VIII, after all.
Already the recipient of a Best Actress Award from the Online Film Critics Society (OFCS), Cate Blanchett is riveting in the role of the Virgin Queen (the film's slightly sarcastic subtitle). Blanchett's Elizabeth is vibrant and principled, spirited and sharp, beautiful and bitter. In a very demanding role, Blanchett is able to assume an air of originality, a quiet determination to find or create the inspiration she will need to preserve the traditions and power of the monarchy. Look for Blanchett's face on the big screen for years to come, and her name on many nomination lists.
Geoffrey Rush is just as strong. As Francis Walsingham, a man returning to court after a period of exile, Rush depicts consummate dedication to Queen Elizabeth I. Walsingham is charged with protecting the Queen, and he immerses himself in the arduous task of security. There's a slight resemblance between Walsingham and Inspector Javert, whom Rush played in the recent remake of 'Les Miserables.' Javert is nearly psychotic in his pursuit of the quasi-saint, Jean Valjean; Walsingham's hunting of those disloyal to the Crown is almost as obsessive. Much is implied by the script, perhaps a weak point: perhaps we could have seen more of this early Renaissance knight commanding his forces. We see him only once giving orders to a spy. Still, Rush assists in a steady building of plot, until the film resonates with the overtones of power and ambition and love of country.
As Sir Robert Dudley, Joseph Fiennes gives a sturdy performance. His part demands him to be first a sincere lover and later a treasonous outcast. Fiennes plays the range well, except that at times he seems too arch, his chin beard and cocked brow the harbingers of cliché. He is good here, but better as the Bard of Stratford in 'Shakespeare in Love', a fortuitous companion piece whose humor compliments the historical tone of 'Elizabeth'.
'Sumptuous' is an apt word for the costumes and cinematography. They function in tandem to create a visual banquet of images. The few peasants we see are appropriately scruffy. More noticeably, the noblemen who are the focus of 'Elizabeth' are outfitted expertly. The retinues of Queens Mary and Elizabeth are stunningly coordinated, and all the accoutrements worn by Blanchett are painstaking in their detail. My only criticism is the black leather look of the traitors, particularly the garb of the main villain, Norfolk. But the look of the film is memorable, to say the least. Like the momentum of the plot, the beauty of the photography causes the viewer a degree of awe; toward the end there is a retrospective montage, almost a collage of greatest visual hits, that summarizes the rise and, more importantly, the growth of Elizabeth into a mature head of state.
Shekhar Kapur has accomplished a remarkable feat for a director with only a few films behind him. The scenes are well shot, especially ones that exploit the mood of such a period piece - bishops held in a dingy cell, Walsingham looking down on them from a low-angle vantage. And at the end, as Elizabeth assumes the aspect of born-again virgin, she becomes an icon of power and pallor, her servants applying the lead and arsenic to her skin. As the undisputed ruler she crosses a roomful of admiring subjects, and for an instant she gets almost too close to the camera. For an instant we are too close to this monarch. Then we cut away to see the young but stern Elizabeth more distantly, and we glimpse the bright future of the British Empire.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{{Infobox prepared food | name = Cheonsachae | image = | caption = | alternate_name = | country = Korea | region = | creator = | course = | type = Korean noodles | served = | main_ingredient = Kombu extract | variations = | calories = | other = }} {{Infobox Korean name |hangul=천사채 |hanja= |rr=cheonsachae |mr=ch'onsach'ae }}
Cheonsachae are Korean half-transparent noodles made from the jelly-like extract left after steaming kombu, without the addition of grain flour or starch. The taste of these noodles is bland, so they are generally eaten as a light salad after seasoned or served as a garnish beneath saengseon hoe (sliced raw fish). Cheonsachae has a chewy texture and is low in calories.<ref name="Cheonsachae">{{cite news|url=http://www.encyber.com/search_w/ctdetail.php?masterno=798768&contentno=798768 |script-title=ko:천사채 |publisher=Doosan Encyclopedia |language=Korean}}</ref>
{{Reflist}} {{Korea-cuisine-stub}} {{Noodle}}


(upcoming)
Hi, I'm Maxime Jacques a software engineer, full stack dev and no-nosense programmer.
Read my articles on (social) hacking booking.com (upcoming) and others in my
blog
Check out my latest projects on
github
Download my
resume
Based in Montreal, Canada. Contact me at: maxyme[at]outlook[dot]com


| MATLAB File Help: prtPreProcPca |
prtPreProcPca Principle Component Analysis PCA = prtPreProcPca creates a Principle Component Analysis object. PCA = prtPreProcPca('nComponents',N) constructs a prtPreProcPCP object PCA with nComponents set to the value N. A prtPreProcPca object has the following properites: nComponents - The number of principle componenets A prtPreProcPca object also inherits all properties and functions from the prtAction class Example: dataSet = prtDataGenFeatureSelection; % Load a data set pca = prtPreProcPca; % Create a prtPreProcPca object pca = pca.train(dataSet); % Train the prtPreProcPca object dataSetNew = pca.run(dataSet); % Run % Plot plot(dataSetNew); title('PCA Projected Data');
| Superclasses | prtPreProc |
| Sealed | false |
| Construct on load | false |
| prtPreProcPca | Principle Component Analysis |
| dataSet | The training prtDataSet, only stored if verboseStorage is true. |
| dataSetSummary | Structure that summarizes prtDataSet. |
| isCrossValidateValid | True |
| isSupervised | False |
| isTrained | Indicates if prtAction object has been trained. |
| means | A vector of the means |
| nComponents | The number of principle components |
| name | Principal Component Analysis |
| nameAbbreviation | PCA |
| pcaVectors | The PCA vectors. |
| showProgressBar | |
| totalPercentVarianceCumulative | reduced dimension data as a |
| totalVariance | training data |
| totalVarianceCumulative | dimension data. |
| trainingTotalVariance | The total variance contained in the |
| userData | User specified data |
| verboseStorage | Specifies whether or not to store the training prtDataset. |
| crossValidate | Cross validate prtAction using prtDataSet and cross validation keys. | |
| get | get the object properties | |
| kfolds | Perform K-folds cross validation of prtAction | |
| optimize | Optimize action parameter by exhaustive function maximization. | |
| run | Run a prtAction object on a prtDataSet object. | |
| set | set the object properties | |
| train | Train a prtAction object using training a prtDataSet object. |


{% extends 'base.html' %} {% block header %}
Please specify the number of additional epochs for which to train. This represents the number of times to iterate over the data set when training. Higher values mean training will take longer, but could yield better results. If left blank, a default value of {{ default_num_epochs }} will be used.


Enhancing effect of 17 beta-estradiol on human NK cell activity.
Abstract
The in vitro effect of 17 beta-estradiol on NK activity was studied. The proliferation and NK activity of YT-N17 (a human NK-like cell line) were enhanced by 17 beta-estradiol (E2), and the enhancement was blocked by tamoxifen (Tx), an antagonist of E2. On the contrary, other steroid hormones such as Tx, progesterone, and testosterone had no effect. YT-N17 contained 11.8 fmol/mg protein of estrogen receptor (mean of two independent assays), a value which was 5-10-fold higher than that of other hematopoietic cell lines. An enhancement of NK activity by E2 was also seen in large granular lymphocytes obtained from normal subjects, and it was again suppressed by Tx. These data suggest that E2 is one of the activating factors for NK/LGL cells.


Anna Koenig
Frank Engelmann
Betty Leipold
Anna Stagl
Rudy Stagl
For Sharing their Voices
Aaron Engelhart
Madeline VonStein
Ryan Zarecki
Caitlin Spicer
Michael Rodescheim
Noah Frato-Sweeney
Diccon Ong
Steve Smith
Christopher P. Myers
Henry Ong
For his Beautiful Music
Fred Leipold
For Sharing Images
Erna Schuster Becker
Frank Engelmann
David Hall
Michelle Smith
Anna and Rudy Stagl
Lori Wolf-Heffner
and
The Donauschwäbisches Zentralmuseum Ulm
and the Muzeul Stefan Jäger
For Sharing Videos
David Hall
Steve Smith
With Additional Thanks To:
Michelle Smith
Steve Smith
Diccon Ong
Russell Morrison
Sarah Horgan
Andrew Huang
Holly Bunt
Lori Wolf-Heffner
David Hall
And to everyone who contributed thoughts, ideas, and support: You made Up and Down the Years possible.


Use the convenience of Google Drive to safely manage accounts and passwords.
Sign in with your Google account.


libsvm package implements Spark SQL data source API for loading LIBSVM data as DataFrame.
libsvm package implements Spark SQL data source API for loading LIBSVM data as DataFrame.
The loaded DataFrame has two columns: label containing labels stored as doubles and
features containing feature vectors stored as Vectors.
To use LIBSVM data source, you need to set "libsvm" as the format in DataFrameReader and
optionally specify options, for example:
LIBSVM data source supports the following options:
"numFeatures": number of features.
If unspecified or nonpositive, the number of features will be determined automatically at the
cost of one additional pass.
This is also useful when the dataset is already split into multiple files and you want to load
them separately, because some features may not present in certain files, which leads to
inconsistent feature dimensions.
"vectorType": feature vector type, "sparse" (default) or "dense".
Note
This class is public for documentation purpose. Please don't use this class directly.
Rather, use the data source API as illustrated above.
libsvm
package implements Spark SQL data source API for loading LIBSVM data asDataFrame
. The loadedDataFrame
has two columns:label
containing labels stored as doubles andfeatures
containing feature vectors stored asVector
s.To use LIBSVM data source, you need to set "libsvm" as the format in
DataFrameReader
and optionally specify options, for example:LIBSVM data source supports the following options:
This class is public for documentation purpose. Please don't use this class directly. Rather, use the data source API as illustrated above.
LIBSVM datasets


JavaScript was initially created to “make web pages alive”. The programs in this language are called scripts. They can be written right in a web page’s HTML and run automatically as the page loads. Scripts are provided and executed as plain text. They don’t need special preparation or compilation to run. In this aspect, JavaScript is very different from another language called Java.
Today, JavaScript can execute not only in the browser, but also on the server, or actually on any device that has a special program called the JavaScript engine. The browser has an embedded engine sometimes called a “JavaScript virtual machine”. Different engines have different “codenames”. For example: V8 – in Chrome and Opera. SpiderMonkey – in Firefox. …There are other codenames like “Trident” and “Chakra” for different versions of IE, “ChakraCore” for Microsoft Edge, “Nitro” and “SquirrelFish” for Safari, etc. The terms above are good to remember because they are used in developer articles on the internet. We’ll use them too. For instance, if “a feature X is supported by V8”, then it probably works in Chrome and Opera.
Modern JavaScript is a “safe” programming language. It does not provide low-level access to memory or CPU, because it was initially created for browsers which do not require it. JavaScript’s capabilities greatly depend on the environment it’s running in. For instance, Node.js supports functions that allow JavaScript to read/write arbitrary files, perform network requests, etc. In-browser JavaScript can do everything related to webpage manipulation, interaction with the user, and the webserver. For instance, in-browser JavaScript is able to: Add new HTML to the page, change the existing content, modify styles. React to user actions, run on mouse clicks, pointer movements, key presses. Send requests over the network to remote servers, download and upload files (so-called AJAX and COMET technologies). Get and set cookies, ask questions to the visitor, show messages. Remember the data on the client-side (“local storage”).
The syntax of JavaScript does not suit everyone’s needs. Different people want different features. That’s to be expected, because projects and requirements are different for everyone. So recently a plethora of new languages appeared, which are transpiled (converted) to JavaScript before they run in the browser. Modern tools make the transpilation very fast and transparent, actually allowing developers to code in another language and auto-converting it “under the hood”.
function greetMe(yourName){
alert("Hello " + yourName);
}
greetMe("World!")
- Variables
- Functions
- Control Flow
- Promises
- Unsual Areas
Most of the time, a JavaScript application needs to work with information. Here are two examples: An online shop – the information might include goods being sold and a shopping cart. A chat application – the information might include users, messages, and much more. Variables are used to store this information. A variable A variable is a “named storage” for data. We can use variables to store goodies, visitors, and other data. To create a variable in JavaScript, use the let keyword. The statement below creates (in other words: declares) a variable with the name “message”:
Talking about variables, there’s one more extremely important thing. A variable name should have a clean, obvious meaning, describing the data that it stores. Variable naming is one of the most important and complex skills in programming. A quick glance at variable names can reveal which code was written by a beginner versus an experienced developer. In a real project, most of the time is spent modifying and extending an existing code base rather than writing something completely separate from scratch. When we return to some code after doing something else for a while, it’s much easier to find information that is well-labeled. Or, in other words, when the variables have good names. Please spend time thinking about the right name for a variable before declaring it. Doing so will repay you handsomely.
If an expression has more than one operator, the execution order is defined by their precedence, or, in other words, the default priority order of operators. From school, we all know that the multiplication in the expression 1 + 2 * 2 should be calculated before the addition. That’s exactly the precedence thing. The multiplication is said to have a higher precedence than the addition. Parentheses override any precedence, so if we’re not satisfied with the default order, we can use them to change it. For example, write (1 + 2) * 2. There are many operators in JavaScript. Every operator has a corresponding precedence number. The one with the larger number executes first. If the precedence is the same, the execution order is from left to right.
There are three logical operators in JavaScript: || (OR), && (AND), ! (NOT). Although they are called “logical”, they can be applied to values of any type, not only boolean. Their result can also be of any type. Let’s see the details.
if (year == 2015) {
alert( "That's correct!" );
alert( "You're so smart!" );
}
uite often we need to perform a similar action in many places of the script. For example, we need to show a nice-looking message when a visitor logs in, logs out and maybe somewhere else. Functions are the main “building blocks” of the program. They allow the code to be called many times without repetition. We’ve already seen examples of built-in functions, like alert(message), prompt(message, default) and confirm(question). But we can create functions of our own as well.
Functions are actions. So their name is usually a verb. It should be brief, as accurate as possible and describe what the function does, so that someone reading the code gets an indication of what the function does. It is a widespread practice to start a function with a verbal prefix which vaguely describes the action. There must be an agreement within the team on the meaning of the prefixes. For instance, functions that start with "show" usually show something.
let userName = 'John';
function showMessage() {
let message = 'Hello, ' + userName;
alert(message);
}
showMessage(); // Hello, John
The
function showMessage(from, text) { // arguments: from, text
alert(from + ': ' + text);
}
showMessage('Ann', 'Hello!'); // Ann: Hello! (*)
showMessage('Ann', "What's up?"); // Ann: What's up? (**)
let year = prompt('In which year was the ECMAScript-2015 specification published?', '');
if (year == 2015) {
alert( 'You guessed it right!' );
} else {
alert( 'How can you be so wrong?' ); // any value except 2015
}
let func = function(arg1, arg2, ...argN) {
return expression;
};


Choose organisation
Choose the organisation that employs the apprentices you’d like to add – this should be the organisation named on the contract for service, if relevant:
Choose the organisation that employs the apprentices you’d like to add – this should be the organisation named on the contract for service, if relevant:


AlpsNMR-package.Rd
AlpsNMR allows you to import NMR spectra into R and provides automated and efficient signal processing for untargeted NMR metabolomics.
The following functions can be combined with the pipe. They create or modify the nmr_dataset object.
There are also functions to extract the metadata and submit the samples to irods, see the example below.
The nmr_dataset object is essentially a list, so it is easy to access its components for further analysis.
Maintainer: Luis Fernandez<EMAIL_ADDRESS>(ORCID)
Authors:
Ivan Montoliu Roura<EMAIL_ADDRESS>Sergio Oller Moreno<EMAIL_ADDRESS>(ORCID)
Francisco Madrid Gambin<EMAIL_ADDRESS>(ORCID)
Héctor Gracia Cabrera<EMAIL_ADDRESS>Other contributors:
Nestlé Institute of Health Sciences [copyright holder]
Institute for Bioengineering of Catalonia [copyright holder]
dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR") dataset <- nmr_read_samples_dir(dir_to_demo_dataset)#> Warning: `future_options()` was deprecated in furrr 0.2.0. #> Please use `furrr_options()` instead.#> #>#>#> #>my_nmr_dataset <- dataset %>% nmr_interpolate_1D(axis = c(0.4, 10)) %>% nmr_exclude_region(exclude = list(water = c(4.6, 5))) %>% nmr_normalize(method = "pqn") %>% plot#> Warning: The Probabalistic Quotient Normalization requires several samples to compute the median spectra. Your number of samples is low


{% include schedule/intro.html date='October 3rd' %}
{% include schedule/header.html tracks='1' %}
{% include schedule/event.html time='9.15-9.45' title='Check-in' fill='true' %}
{% include schedule/event.html time='9.45-10.00' title='GrUSP presentation' fill='true' %}
{% include schedule/talk.html title='Node.js: A Decade On' time='10.00-10.30' fill='true' %}
{% include schedule/talk.html title="What's new in Node.js testing" time='10.35-11.05' fill='true' %}
{% include schedule/pause.html time='11.05-11.30' title='Coffee break' icon="coffee" fill='true' %}
{% include schedule/talk.html title='Designing Node.js applications for scalability, observability, maintainability and K8S deployability' time='11.30-12.15' fill='true' %}
{% include schedule/talk.html title='I want to publish my first Open Source library' time='12.20-13.05' fill='true' %}
{% include schedule/pause.html time='13.05-14.30' title='Lunch & Lightning Talks' icon="food" fill='true' %}
{% include schedule/talk.html title='A world of Rest APIs...from Restify to a faster Fastify' time='14.30-15.00' fill='true' %}
{% include schedule/talk.html title='Why GraphQL vs. Why not GraphQL' time='15.05-15.35' fill='true' %}
{% include schedule/talk.html title='Getting Started with gRPC and Node.js' time='15.40-16.10' fill='true' %}
{% include schedule/pause.html time='16.10-16.40' title='Coffee break' icon="coffee" fill='true' %}
{% include schedule/talk.html title='Web Authentication - Imagine a World Without Passwords' time='16.40-17.10' fill='true' %}
{% include schedule/talk.html title='Allegro.pl in flames' time='17.15-17.45' fill='true' %}
{% include schedule/pause.html time='17.45-17.50' title='Bye bye from GrUSP' fill='true' %}
{% include schedule/pause.html time='17.50-19.00' title='Happy Hour' icon="bar" fill='true' %}
{% include schedule/header.html track='1' %}
{% include schedule/event.html time='9.15-9.45' title='Check-in' fill='true' %}
{% include schedule/event.html time='9.45-10.00' title='GrUSP presentation' fill='true' %}
{% include schedule/talk.html title='Node.js: A Decade On' time='10.00-10.30' fill='true' %}
{% include schedule/talk.html title="What's new in Node.js testing" time='10.35-11.05' fill='true' %}
{% include schedule/pause.html time='11.05-11.30' title='Coffee break' icon="coffee" fill='true' %}
{% include schedule/talk.html title='Designing Node.js applications for scalability, observability, maintainability and K8S deployability' time='11.30-12.15' fill='true' %}
{% include schedule/talk.html title='I want to publish my first Open Source library' time='12.20-13.05' fill='true' %}
{% include schedule/pause.html time='13.05-14.30' title='Lunch & Lightning Talks' icon="food" fill='true' %}
{% include schedule/talk.html title='A world of Rest APIs...from Restify to a faster Fastify' time='14.30-15.00' fill='true' %}
{% include schedule/talk.html title='Why GraphQL vs. Why not GraphQL' time='15.05-15.35' fill='true' %}
{% include schedule/talk.html title='Getting Started with gRPC and Node.js' time='15.40-16.10' fill='true' %}
{% include schedule/pause.html time='16.10-16.40' title='Coffee break' icon="coffee" fill='true' %}
{% include schedule/talk.html title='Web Authentication - Imagine a World Without Passwords' time='16.40-17.10' fill='true' %}
{% include schedule/talk.html title='Allegro.pl in flames' time='17.15-17.45' fill='true' %}
{% include schedule/pause.html time='17.45-17.50' title='Bye bye from GrUSP' fill='true' %}
{% include schedule/pause.html time='17.50-19.00' title='Happy Hour' icon="bar" fill='true' %}
{% include index/newsletter.html %}
{% include index/sponsors.html %}


Notebooks
Story: The crying of the wind is an evil omen
The crying of the wind is an evil omen
Title
The crying of the wind is an evil omen
Collection
Summary
The crying of the Wind believed to forebode evil. In the Katkop dialect, by Díä!kwãin (L V.-23. 5841-5845).
Comments
1) p.5840v: Dia!kwain was told this by his mother (≠kamme-an); the wind's crying sounds like the howling of a dog, 2) pp.5842v-5845v: see |xannay |xannay and the wind, 3) see also |xannan |xannan and The crying of the wind tells beasts of prey where to find people, 4) This story is found in Book V-23
Contributors
Date
January 1876
Categories
Custom and daily life, Portention
Keywords
wind (its crying is an evil omen) , wind (foretells evil) , wind (the noises it makes) , wind (foretells death and misfortune) , wind (brings bad news) , wind (howls) , wind (sounds like a dog) , wind (imitates the crying of people) , wind (feels that people will cry) , wind (and sorrow) , wind (accounting for the noise it makes) , death (crying of the wind is an evil omen of) , death (the crying wind foretells) , death (omens of) , presentiment (the crying wind and death) , presentiment (the wind and forebodings of evil) , omen (crying of the wind is an evil) , omen (of death)
Story Pages
5841-5845


This algorithm has been ported to modern C++ and can be found at https://github.com/ned14/quickcpplib/blob/master/include/quickcpplib/algorithm/bitwise_trie.hpp. This project has been ARCHIVED and will no longer be maintained. Thanks for all the user support throughout the years.
Web site: http://www.nedprod.com/programs/portable/nedtries/
API Reference: https://ned14.github.io/nedtries/nedtrie_8h.html
master branch CI status: Linux: Windows:
Enclosed is nedtries, an in-place bitwise binary Fredkin trie algorithm which allows for near constant time insertions, deletions, finds, closest fit finds and iteration. On modern hardware it is approximately 50-100% faster than red-black binary trees, it handily beats even the venerable O(1) hash table for less than 3000 objects and it is barely slower than the hash table for 10000 objects. Past 10000 objects you probably ought to use a hash table though, and if you need nearest fit rather than close fit then red-black trees are still optimal.
It is licensed under the Boost Software License which basically means you can do anything you like with it. Commercial support is available from ned Productions Limited.
Its advantages over other algorithms are sizeable:
Its two primary disadvantages are that (i) it can only key upon a size_t (i.e. the size of a void *), so it cannot make use of an arbitrarily large key like a hash table can (though of course one could hash the large key into a size_t sized key) and (ii) it is lousy at guaranteed nearest fit finds. It also runs fastest when the key is as unique from other keys as possible, so if you wish to replace a red-black tree which has a complex left-right comparison function which cannot be converted into a stable size_t value then you will need to stick with red-black trees. In other words, it is ideal when you are keying on pointer sized keys where each item has a definitive non-changing key.
Have a look at the scaling graphs below to decide if your software could benefit. Note for non-random key distributions you may get significantly better or worse performance than shown. If you're interested, read on for how to add them to your software.
In v1.02 a breaking change in what NEDTRIE_NFIND means and does was introduced, and the version was bumped to v1.02 to warn users of the change. The problem in v1.01 and earlier was that I had incorrectly documented what NEDTRIE_NFIND does: I said that it found the nearest item to the search term when this was patently untrue (thanks to smilingthax for reporting this). In fact, NEDTRIE_NFIND used to return a matching item, if there was one, but if there was no matching item, it returned any larger item rather than the next largest item. I do apologise to the users of nedtries for this documentation error, and for the lost productivity it surely must have caused some of you.
The good news is that NEDTRIE_NFIND now guarantees to return the next largest item, and it therefore now matches BSD's red-black Nfind. The bad news is that bitwise tries are really not ideal for guaranteed nearest matching, and performance is terrible as you can see via the purple line in the graphs above. If you can put up with non-guaranteed nearest matching, NEDTRIE_CFIND offers much better performance. NEDTRIE_CFIND takes a rounds parameter which indicates how hard the routine should try to return a close item: rounds=0 means to return the first item encountered which is equal or larger to search key, rounds=1 means try one level down, rounds=2 means try two levels down and so on. rounds=INT_MAX means try hardest, and guarantees that any item with a matching key will be found and that if not matching, any item returned will have a very close key (those not necessarily the closest).
For a summary of the differences between Nfind and Cfind, see this useful table. Note that if you just want any item with a key larger or equal to the search key, NEDTRIE_CFIND(rounds=0|1|2) is extremely swift and has O(1) complexity as shown in the graphs above.
The source makes use of C macros on C and C++ templates on C++ - therefore, unlike typical C-macro-based algorithms it is easy to debug and in fact, the improved metadata specified by the templates lets a modern C++ compiler produce 5-15% faster code through PGO guided selective inlining. The code is 100% standard C and C++, so it should run on any platform or architecture though you may need to implement your own nedtriebitscanr() function if you're not using GCC nor MSVC and want to keep performance high. If you are building debug, NEDTRIEDEBUG is by default turned on: this causes a complete state validation check to be performed after each and every change to the trie which tends to be very good at catching bugs early, but can make debug builds a little slow.
So what is "an in-place bitwise binary Fredkin trie algorithm" then? Well you ought to start by reading and fully digesting the Wikipedia page on Fredkin tries as what comes won't make much sense otherwise. The Wikipedia page describes a non-inplace trie which uses dynamic memory to store each consecutive non-differing section of a string, and indeed this is how tries are normally described in algorithm theory and classes. nedtries obviously enough selects on individual bits rather than substrings, and it uses an inplace instead of dynamically allocated implementation.
Here is how nedtries performs an indexation: firstly, the most significant set bit X is found using nedtriebitscanr() which is no more than one to three CPU cycles on modern processors. This is used to index an array of bins. Each bin X contains a binary tree of items whose keys are (1<<X) <= key < (1<<(X+1)), so what one does is to follow the tree downwards selecting left or right based on whether the next bit downwards is 0 or 1. If an item has children, its key is only guaranteed to be constrained to that of its bin, whereas if an item does not have children then its key is guaranteed to match as closely as possible its position in the tree.
If you insert an item, nedtries indexes as far as it can down the existing tree where the new item ought to be and inserts it there. If you remove an item, if that item has no children it is simply removed. If it has a child then a nobble function is called to select the bias for how to select the childless item to be nobbled and used as the replacement i.e. one either traverses down preferentially 1 or preferentially 0 until you find a childless item, then you delink it from there and link it in to replace the item being removed.
If you think about this hard enough, you realise that you will get a "nearly sorted" binary tree i.e. one whose node keys are very nearly in order. In fact, the more randomised the key, the more in order the tree becomes. The tree is usually sufficiently ordered that one can assume it to be so for most operations, but if you need to guarantee order then you can bubble sort per MSB bin as bubble sort performs very well on nearly sorted data (as does smooth sort if you have a very large set of data) .
The enclosed benchmark.cpp will run a series of scalability tests comparing the bitwise binary trie implementation from nedtries with others outputting its results in CSV format:
You will also find enclosed a set of precomputed Microsoft Excel spreadsheets which were generated on a 2.67Ghz Intel Core 2 Quad Windows 7 x64 machine. They should be representative of performance on modern hardware - though note that the Intel Atom has a 17 cycle nedtriebitscanr() which is the only modern CPU to be so slow. See http://gmplib.org/~tege/x86-timing.pdf for x86 and x64 instruction timings.
Usage via C macros follows the FreeBSD rbtree.h format. See the enclosed nedtries.chm for detailed API documentation. Here is some sample code which can be compiled cleanly using gcc -Wall -pedantic -std=c99 test.c (or as C++ via g++ -Wall -pedantic test.c):
#include <stdio.h> #include <string.h> #include "nedtrie.h" typedef struct foo_s foo_t; struct foo_s { NEDTRIE_ENTRY(foo_s) link; size_t key; }; typedef struct foo_tree_s foo_tree_t; NEDTRIE_HEAD(foo_tree_s, foo_s); static foo_tree_t footree; size_t fookeyfunct(const foo_t *r) { return r->key; } NEDTRIE_GENERATE(static, foo_tree_s, foo_s, link, fookeyfunct, NEDTRIE_NOBBLEZEROS(foo_tree_s)) int main(void) { foo_t a, b, c, *r; NEDTRIE_INIT(&footree); a.key=2; NEDTRIE_INSERT(foo_tree_s, &footree, &a); b.key=6; NEDTRIE_INSERT(foo_tree_s, &footree, &b); r=NEDTRIE_FIND(foo_tree_s, &footree, &b); assert(r==&b); c.key=5; r=NEDTRIE_NFIND(foo_tree_s, &footree, &c); assert(r==&b); /* NFIND finds next largest. Invert the key function (i.e. 1-key) to find next smallest. */ NEDTRIE_REMOVE(foo_tree_s, &footree, &a); NEDTRIE_FOREACH(r, foo_tree_s, &footree) { printf("%p, %u\n", (void *) r, (unsigned) r->key); } assert(!NEDTRIE_PREV(foo_tree_s, &footree, &b)); assert(!NEDTRIE_NEXT(foo_tree_s, &footree, &b)); return 0; }
There isn't really much more to it - if you want to throw away the trie, simply NEDTRIE_INIT() its head. As no dynamic memory is involved, nothing is lost.
I should mention what the nobble function is for: you have three default choices, NEDTRIE_NOBBLEZEROS, NEDTRIE_NOBBLEONES and NEDTRIE_NOBBLEEQUALLY though you can of course also define your own. The nobble function contributes to tree balance by working against bit bias in your keys, so if your keys contain an excess of non-leading zeros then you should preferentially nobble zeros. Equally if your keys contain an excess of ones, then you should preferentially nobble ones and, as you might have guessed, if your bits after the first set bit are completely random (which is rare) then you should nobble equally.
Sounds complicated? In fact it's very easy if you simply use trial & error. Start with nobble zeroes which tends to be right in most situations, and then use benchmarking your code to determine the correct setting.
Where the BSD red-black tree implementation has RB_NFIND() for finding items which are nearest to the search term, nedtries provides NEDTRIE_CFIND() and NEDTRIE_NFIND(). What's the difference? Here's a quick table:
| Nfind | Cfind(rounds=INT_MAX) | Cfind(rounds=0|1) |
|---|---|---|
|
|
|
NOTE: Enable the C++ STL containers using the NEDTRIE_ENABLE_STL_CONTAINERS macro. You are suggested to avoid the C++ STL containers where possible, they were hacked together.
C++ usage is even easier than the C macro usage thanks to nedtries::trie_map<> and nedtries::trie_multimap<> which is API compatible with the std::map<>, std::multimap<> and std::unordered_map<> STL associative containers. trie_map<> and trie_multimap<> makes full use of rvalue construction if either you are running on C++0x according to the value of __cplusplus, or have defined HAVE_CPP0X. In the general case, simply drop trie_map<> or trie_multimap<> in where your STL associative container used to be and enjoy the speed benefits!
Note that insertion and deletion speed in any STL container is heavily bound by the speed of your memory allocator. You may wish to consider employing nedmalloc which can deliver some unholy speed benefits if you run it as root, otherwise it will need some small source changes to employ its advanced v2 malloc API.
In case you are not familiar with STL associative containers, they are very simple e.g.:
nedtries::trie_map<size_t, Foo> foomap; foomap[5]=Foo(); foomap.erase(foomap.find(5));
You can of course iterate through them and do all the normal things you can do with any STL container.
trie_map<> and trie_multimap<> are actually a STL container wrappers rather than a proper STL container in its own right i.e. it subclasses an existing STL container passing through most of its API, but selectively overrides certain members. Its default parameters point at std::list<> which is its most likely usage model for most people.
The advantages are mainly that it is quick to implement and can be theoretically applied to any arbitrary STL container, thus taking advantage of that STL container's optimisations and customisations. The big disadvantage is that it is hacky, dirty and prone to getting bugs into it, and if you look at the source you'll see what I mean. There is after all a number of places where I am doing a number of very illegal things in C++ which just happen to usually work.
The chances are that this implementation will be good enough for most people. If however you might like to sponsor the development of a full bitwise trie STL associative container for submission to the Boost C++ peer reviewed libraries (and thereafter into the standard C++ language itself), I would be very pleased to oblige. Please contact ned Productions Consulting Ltd. for further details.


Before Change
return gs.array([[False]] * n_points)
point_transpose = gs.transpose(point, axes=(0, 2, 1))
diff = gs.matmul(point_transpose, point) - gs.eye(p)
diff_norm = gs.linalg.norm(diff, axis=(1, 2))
belongs = gs.less_equal(diff_norm, tolerance)
After Change
return gs.array([[False]] * n_points)
point_transpose = gs.transpose(point, axes=(0, 2, 1))
identity = gs.to_ndarray(gs.eye(p), to_ndim=3)
identity = gs.tile(identity, (n_points, 1, 1))
diff = gs.einsum("nij,njk->nik", point_transpose, point) - identity
diff_norm = gs.linalg.norm(diff, axis=(1, 2))
belongs = gs.less_equal(diff_norm, tolerance)


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/dsimp.3B.20simp.html ---
I think I set a new personal record:
by dsimp [is_limit.equiv, adjunction.cone_equivalence]; simp; dsimp; simp
Can we make a dsimp
and simp
all in one tactic.
Yay, and I'm happy seeing is_limit.equiv
being used moments after I wrote it (again). :-)
@Scott Morrison I think we should expand the category theory library
instead of saying that Lean is not ready for it
Absolutely, I agree it should be expanded massively.
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


Copyright (c) 2005-2008 Flamingo, Kirill Grouchnikov.
All Rights Reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.


So Karl will PV outperform Xen in terms of handling more models, processing more events, etc on-screen without hiccups? You mentioned EJ had some real faulty coding on his part and was using old modules and principles of programming, will you be able to beef gameplay to play more smooth and seamless?
Walking is a coder-hack right now, but it's a lot smoother that it was (you'd never want to have seen that)
Kuba -- PV will definitely outperform Xenimus in most regards once I clean up and optimize the code. For now (and the forseeable future) it isn't going to be blazingly fast. That said, it plays at a solid 60 fps on my laptop.
I think it's because I'm recording at half-quality, and the non-hand cursor (that cursor appears only when over a button) is only 2 pixels wide--it's the crosshair, because I don't have time to make a better one. :-p
been very unactive lately due to r/l issues. looking in and seeing this is sweet. gonna be way more active now I think. Have you posted this on xentales? would prob make alot more interested when they actually see that its playable
Team F-G-T We are fugitives of the law. Idiocy is our only option


2. Choose from the manuals below.
Online developer documentation for OpenStack Compute (Nova)
Online developer documentation for OpenStack Object Storage (Swift)
Online developer documentation for OpenStack Image Service (Glance)
The OpenStack developer wiki


The mighty Great White shark(Carcharodon carcharias) is often mistakenly thought of as the most voracious predator of the seas, and even has a reputation as a ferocious man-eater, something that sadly has been hugely exaggerated by the media. Their powerful body is supported by a cartilaginous skeleton (as opposed to the bone skeleton of most other vertebrates), is streamlined for efficient movement through the water, and has a pointed snout, two large, sickle-shaped pectoral fins and a large triangular first dorsal fin. The mouth is armed with an array of sharply pointed, serrated teeth; indeed the generic name is derived from the Greek word carcharos for ragged and odon for tooth. These sharks are grey or bronze on the upper surface of the body and are white underneath (5). They have an acute sense of smell and are able to sense electric fields through sensors in the snout.


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{% extends 'base.html' %} {% block header %}
Please specify the epoch for which you would like to generate a snapshot histogram. The maximum epoch on record is {{ max_epoch }}. Please select an integer value between 0 and {{ max_epoch }}.
Please specify whether you are interested in generating histograms for the Generator or Discriminator network.


I’m currently working my way through the PTPv4 course offered by eLearn Security. One of the labs involves extending our reach to otherwise inaccessible networks by pivoting through a Victim that we already have an active Meterpreter session on.
I’m going to skip the network discovery part and jump straight into the interesting bits! Therefore assume the network map looks like this:
As seen below, we have an existing Meterpreter session active for Victim 1. We will be using this session to pivot via Victim 1 to access Victim 2.
Socks Module Setup
We will need to use the Socks4a Metasploit module to setup a proxy from Meterpreter to our system. The Socks proxy is required because Meterpreter uses its own separate routing table vs what the host uses. Meaning only Metasploit can access the routes it sets up unless we use a proxy.
Adding Default Routes
Next up we will use the Autoroute module with the CMD set to default and the SESSION set to 14 (see Figure 1). The SUBNET setting is not required when using default. This tells Metasploit to add a default route to its routing table, routing all traffic Metasploit see’s through VICTIM 1. When combined with the Socks proxy setup previously, it allows access to any system that VICTIM 1 can access via our machine.
Foxyproxy
The final step is to use our proxy outside of Metasploit. In this case, I’m using FoxyProxy in Firefox but another common route is to use proxychains (which comes installed by default in Kali).
Testing
All thats left to do is to test we can accesss VICTIM 2 via Firefox, and as seen in Figure 5 it works!
Takeaway’s
- Metasploits routing table != system routing table
- Pivoting is a great way to gain access to networks that aren’t connected to the internet!
Sources:
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