Midsummer Examinations 2013 CO2017 No. of Pages: 9 No. of Questions: 4 Subject COMPUTER SCIENCE Title of paper CO2017 — OPERATING SYSTEMS, NETWORKS, AND DIS- TRIBUTED SYSTEMS Time allowed Three hours (3 hrs) Instructions to candidates This paper contains two sections, Section A and Section B. Attempt all of Section A. There are three questions […]
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CS124 Lecture 10 Spring 2016 10.1 The Birthday Paradox How many people do there need to be in a room before with probability greater than 1/2 some two of them have the same birthday? (Assume birthdays are distributed uniformly at random.) Surprisingly, only 23. This is easily determined as follows: the probability the first two
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Question 1: Module: CSE102 Assignment 1 Assessment The tasks contribute 10% to the overall assessment of CSE102 Submission Please complete the assessment tasks using Microsoft Word and submit via ICE. You are also asked to print out a copy of your answers and submit it in the class or my office by 10-April- 2017, Monday.
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Microsoft Word – AE1PGP-EX1-CW-Issue.doc School of Computer Science – Coursework Issue Sheet (required for each Saturn component) Session 2016-17 Semester 1 Module Name Programming Paradigms Code AE1PGP Module Convenor(s) (CW Convenor in Bold) Paul Dempster Coursework Name Coursework 1 – Stock Exchange (Java) Weight 15% Deliverable (a brief description of what is to
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ECE 657A: Classification – Lecture 7: k-NN, SVM and Kernal Methods ECE 657A: Classification Lecture 7: k-NN, SVM and Kernal Methods Mark Crowley February 15, 2017 Mark Crowley ECE 657A : Lecture 7 February 15, 2017 1 / 22 Class Admin Announcements Today’s Class Announcements K-nearest neighbor classification Kernel Methods Support Vector Machines Mark Crowley
COMP3421 Computer Graphics COMP3421/9415 Computer Graphics Introduction Angela Finlayson Email: angf@cse.unsw.edu.au mailto:angf@cse.unsw.edu.au Course Admin http://www.cse.unsw.edu.au/~cs3421 Same website used for ~cs9415 Not moodle Course Outline Angela: lectures week1 – week 7 Robert lectures week 8 – week 12 . http://www.cse.unsw.edu.au/~cs3421 http://www.cse.unsw.edu.au/~cs3421/outline.html Tuts and Labs Tutorials start week 2. No marks for tutorial attendance Optional lab tonight:
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handoutB.dvi ECS130 Scientific Computation Handout B January 13, 2017 1. Gaussian elimination = LU factorization A = LU. where L is a unit lower triangular matrix and U a upper triangular matrix. 2. Not all matrices have the LU factorization. For example, A = 0 1 0 0 2 1 2 3
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DEPARTMENT OF INFORMATICS CO2017 Operating Systems, Networks & Distributed Systems Slides 2016/2017 Dr. G. Laycock CO2017 — Operating Systems, Networks and Distributed Systems Week1 L1 — Introduction Dr Gilbert Laycock (gtl1) 2016–01–24 gtl1–R557 W1L1 — Introduction 2016–01–24 1 / 22 Module Organisation Teaching staff Teaching staff Convenor: Dr Gilbert Laycock email: gtl1@le.ac.uk office: G15 Teaching
ANLY550–Spring, 2017 Homework 4 Out: April 7, 2017 Due: April 25, 2017 1. Recall that a hash family H of hash functions mapping [n]→ [r] is pairwise independent if for every 2 distinct values x1,x2 ∈ [n] = {1, . . . ,n}, and any y1,y2 ∈ [r], Pr h←H [h(x1) = y1 and h(x2)
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Deep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com Abstract Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as