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1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

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import javafx.application.Application;
import javafx.event.EventHandler;
import javafx.geometry.Point2D;
import javafx.scene.Group;
import javafx.scene.Scene;
import javafx.scene.canvas.Canvas;
import javafx.scene.canvas.GraphicsContext;
import javafx.scene.input.MouseEvent;
import javafx.stage.Stage;
import static javafx.scene.input.MouseEvent.*;

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Exercise: Define foldl in terms of foldr

Recall the definition of folding a list from the right

    foldr :: (a -> b -> b) -> b -> [a] -> b
    foldr f b []     = b
    foldr f b (a:as) = f a (foldr f b as)

for example foldr f b [1,2,3] = f 1 (f 2 (f 3 b)).

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A playful introduction to Rx

Learning Rx does not have to be boring or painful working your way through theoretical sermons about esoteric concepts like category theory and duality. Life is too short for that kind of abstract nonsense.

So what is a better way to spend a hot summer day with an ice-cold drink, or dually, a cold winter night with a piping hot drink, than to learn Rx by writing an awesome platform game. In this talk, I will walk you through many of the features of Rx by way of scripting a friendly bug to run across a lavish grass meadow and jump for the stars. In just a few tens of lines of code, we will leverage operators like map, flatMap, filter, scan, and many more to react to the keyboard, animate jumps, and perform collision detection. We will even use a test scheduler to make the game run at a speed we determine so we can easily debug and test our game.

We will use RxScala and IntelliJ to develop the game, but since Rx is language and platform agnos

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object InferMyTypePlease {
    def uncurried[A, B](src: List[A], f: A => B): List[B] = src.map(f)
    def   curried[A, B](src: List[A])(f: A => B): List[B] = uncurried(src, f)
}

object Scala {
  import InferMyTypePlease._

  def main(args: Array[String]): Unit = {
    val xs = List(1,2,3)

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The introduction to Reactive Programming you've been missing

(by @andrestaltz)

So you're curious in learning this new thing called (Functional) Reactive Programming (FRP).

Learning it is hard, even harder by the lack of good material. When I started, I tried looking for tutorials. I found only a handful of practical guides, but they just scratched the surface and never tackled the challenge of building the whole architecture around it. Library documentations often don't help when you're trying to understand some function. I mean, honestly, look at this:

Rx.Observable.prototype.flatMapLatest(selector, [thisArg])

Projects each element of an observable sequence into a new sequence of observable sequences by incorporating the element's index and then transforms an observable sequence of observable sequences into an observable sequence producing values only from the most recent observable sequence.

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1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

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public class Pager<I, O> {

    private static final Observable FINISH_SEQUENCE = Observable.never();

    private PublishSubject<Observable<I>> pages;
    private Observable<I> nextPage = finish();
    private Subscription subscription = Subscriptions.empty();

    private final PagingFunction<I> pagingFunction;
    private final Func1<I, O> pageTransformer;

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In this gist I would like to describe an idea for GraphQL subscriptions. It was inspired by conversations about subscriptions in the GraphQL slack channel and different GH issues, like #89 and #411.

Conceptual Model

At the moment GraphQL allows 2 types of queries:

• query
• mutation

Reference implementation also adds the third type: subscription. It does not have any semantics yet, so here I would like to propose one possible semantics interpretation and the reasoning behind it.

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Direct links to free Springer maths books (pdf versions)

duplicates = multiple editions

A Classical Introduction to Modern Number Theory,Kenneth IrelandMichael Rosen

A Classical Introduction to Modern Number Theory,Kenneth IrelandMichael Rosen

A Course in Arithmetic,Jean-Pierre Serre