Cedric Warny cwarny

Goal is to create a tool to enable users to generate a structured knowledge from unstructured data through interaction with that data via a GUI. Here are the different features we would like to support:

Tagging content
- This in turn will update a predictive model that will suggest other documents to be tagged similarly (to be later confirmed or rejected by users)
Extracting entities by highlighting bits of text
Defining simple semantic relationships between entities by drag and drop
Defining ontologies (data models), i.e. rules and relationships that we expect among a set of entities
- This in turn will enable the system to automatically generate inferences, and therefore new insights

In this meeting, I want to focus on 2, 3 and 4.

	<!DOCTYPE html>
	<html>
	<head>
	<title>Cascade</title>
	<script src="http://cloud.github.com/downloads/processing-js/processing-js/processing-1.4.1.min.js"></script>
	<script src="http://code.jquery.com/jquery-2.0.3.min.js"></script>
	</head>
	<body>
	<script type="application/processing" data-processing-target="pjs">

	<!DOCTYPE html>
	<html>
	<head>
	<title>Social Graph</title>
	<script src="http://cloud.github.com/downloads/processing-js/processing-js/processing-1.4.1.min.js"></script>
	<script src="http://code.jquery.com/jquery-2.0.3.min.js"></script>
	</head>
	<body>
	<script type="application/processing" data-processing-target="pjs">

	var Friend = function(name, id, friends, x, y, theta) {
	this.d = 5;
	this.name = name;
	this.id = id;
	this.theta = theta;
	this.name = name;
	this.pos = new p5.Vector(x, y, 0);
	this.mutualFriends = friends;
	this.friends = [];
	};

	<html>
	<head>
	<meta http-equiv="Content-Type" content="text/html;charset=utf-8">
	<title>Twist</title>
	<script id="vertex-shader" type="x-shader/x-vertex">
	attribute vec4 vPosition;

	void main() {
	gl_Position = vPosition;
	}

	import Ember from 'ember';

	export default Ember.TextField.extend({
	keyPress(code) {
	console.log(code.key);
	}
	});

	class Acc:
	def __init__(self, ignore_index=1):
	self.ignore_index = ignore_index

	def __call__(self, pred, tgt):
	# both pred and tgt have shape (bs,seq_len)
	mask = tgt != self.ignore_index
	pred *= mask
	tgt *= mask
	correct = torch.eq(pred, tgt).all(1).sum()