Shading & Lighting Models Lecture: 9 Fall 2016 Computer Graphics (CS3388) Department of Computer Science University of Western Ontario Shading & Lighting Models Outline Basics of shading & lighting Light sources Reflections Lambertian shading Gouraud shading Phong Shading materials from Foley & Van Dam, Nancy Pollard (CMU), T. M ̈oller (SFU), T. Komura (Edinburgh), E. […]
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Sequence Labelling 2: Hidden Markov Models The PoS Tagging Problem This time: The PoS Tagging Problem Modelling the problem Hidden Markov Models Hidden Markov Model tagging Computing sequence probabilities Finding the most likely path Efficient computation: The Viterbi algorithm Training HMMs Data Science Group (Informatics) NLE/ANLP The PoS Tagging Problem Autumn 2015 1 / 29
Program Analysis Term 1, 2015 Problem Sheet 6 1. TheUnion-FinddatastructureisusedinKruskal’sminimumspanningtreealgo- rithm (reproduced below). In general, Union-Find is used when we are working with partitions of a collection of elements. In this case all of the sets (partitions) are disjoint: i.e. they do not share any elements. This data structure is associated with the following two
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Syntax and Parsing 4: Statistical Parsing Variation and Ambiguity This time: The problem of ambiguity Lexical ambiguity Structural ambiguity Local vs. global ambiguity Probabilistic Context-Free Grammar (PCFG) Parse probability and string probability Disambiguation Training Two challenges for NLP are variation and ambiguity: Variation: Practically unlimited number of ways of saying the same thing Need huge
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Algorithms and Data, Summer 1 Problem Set 2 Due May 25, 9PM Write up the answers to part I and an explanation of your strategy for part II in a file called PS2.pdf. Zip this with Dijkstra.java and any other supplemental code for part II in a file called 4800_PS2_XX.zip, where XX are your initials,
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Clipping Algorithms The Graphics Pipeline attributed geometry – Δ Model Transform Perspective Projection Scan Conversion Viewing Transform 3D Clipping Lighting, Shading, Texturing image -pixels 2 Clipping • 3Dclippingtoviewingfrustum • 2D clipping to windows and viewports • Today:Lineclipping,polygonclipping 3 Images courtesy of MIT 3D Clipping • Why REALLY Clip? 4 • Why REALLY Clip? – Save
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Syntax and Parsing 3: Parsing with CFG This time: Basic recognition/parsing strategies top-down strategy bottom up strategy Problems with simple strategies left recursion empty productions redundant reparsing Earley’s Algorithm: Chart Parsing edges and the chart the fundamental rule Data Science Group (Informatics) NLE/ANLP Autumn 2015 1 / 28 Parsing with CFG Consider again the following
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Algorithms and Data 19: Working Around NP-Completeness Professor Kevin Gold Two Approaches to Dealing With NP-Complete Problems 1. Special cases. Maybe you never really see the toughest cases and your worst case need not be exponential. 2. Approximation algorithms. If a close solution is good enough — as in, within a constant
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Compilers and computer architecture: Semantic analysis Martin Berger Alex Jeffery October 2015 Recall the function of compilers Recall the structure of compilers Source program Lexical analysis Intermediate code generation Optimisation Syntax analysis Semantic analysis, e.g. type checking Code generation Translated program Semantic analysis One of the jobs of the compiler front-end is to reject ill-formed
midterm-checkpoint In [ ]: %matplotlib inline %precision 16 import numpy import matplotlib.pyplot as plt Before you turn this problem in, make sure everything runs as expected. First, restart the kernel (in the menubar, select Kernel $\rightarrow$ Restart) and then run all cells (in the menubar, select Cell $\rightarrow$ Run All). Make sure you fill in any place
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