This document contains a lot of information. This section describes the tasks for Lab 2 while section 2 “Resources for Lab 2” provides additional information for completing this lab. I STRONGLY recommend that you read it all the way through. In this lab, you will create two applications, called Reader and Writer, starting from the code at this repository: https://github.com/kgrolinger/se3313-2020-lab2

This subject starts with an overview of the architecture and management of database systems, and a discussion of different existing database models. The main focus includes relational database analysis, design, and implementation. The students learn: relational algebra as the formal foundation of relational databases; relational conceptual design using an entity-relationship diagram; relational logical database design; security and integrity; and SQL implementation of relational database queries. Students will also learn advanced normalization theory and the techniques to remove data anomalies and redundancies. In this subject, students are required to design a database application that meets the needs of a system requirement specification, and to implement the system using a commercial standard database system such as ORACLE or POSTGRESQL. In addition, a selection of advanced topics in databases will be introduced and discussed.

In this project, graduate students are assigned tasks in addition to tasks expected from the undergraduate students. Here is a useful link on BLAS from Intel and NVIDIA to start with. https://software.intel.com/en-us/node/520725 http://docs.nvidia.com/cuda/cublas/#axzz4cdzdjSPJ

Submission is through WebCMS/give and should be a single pdf file, maximum size 2Mb. Prose should be typed, not handwritten. Use of LATEX is encouraged, but not required. Discussion of assignment material with others is permitted, but the work submitted must be your own in line with the University’s plagiarism policy.

Answer all the questions and submit your exercise in PDF. Make sure to include your R scripts or computer codes as an appendix to your submission. Please comment on each procedure so I would know what you are doing (or intend to do). Submission is via E-BART.

For this assignment you will be writing a Pytorch program that learns to read business reviews in text format and predict a rating (positive or negative) associated with each review, as well as a business category (0=Restaurants, 1=Shopping, 2=Home Services, 3=Health & Medical, 4=Automotive).

This assignment is worth 20% of the marks for your final assessment in this unit. Heavy penalties will apply for late submission. This is an individual assignment and must be entirely your own work. You must attribute the source of any part of your code which you have not written yourself. Your program will be checked with a code similarity detector. Please note the section on plagiarism in this document. The assignment must be done using the BlueJ environment. All user input to the system, and system output to the user, must be done at the BlueJ terminal window. No other graphical interfaces are to be used (nor assessed) in your program. The Java source code for this assignment must be implemented according to the FIT9131 Java Coding Standards. Any points needing clarification should be discussed with your tutor in the tutorial session. You should not make any assumptions about the program without consulting with your tutor.

The picture below represents the methodology to forecast equity returns used by Invesco. As you can see their expected return is the sum of three components: Total Yield (a slightly modified version of the dividend yield), Earnings Growth and Valuation Change. Explain in layman terms, without using any formula, what is the Valuation Change component. Try to capture the economic intuition behind this concept. Under which economic circumstances this quantity could be negative?

Due: October 30th, 2020 at the beginning of class. Submission: Upload your solutions of Exercises 3.1–3.5 to Crowdmark as one PDF file. (Do not hand in any solution to the Supplementary Exercises.) Exercise 3.1. Let P be the transition probability matrix of an irreducible discretetime Markov chain where the state space is E and every state is positive recurrent. Let A be a nonempty, proper subset of E and π be the stationary distribution of P.

The objectives of this assignment are: • To demonstrate the ability to implement algorithms using basic data structures and operations on them. • To gain experience in designing an algorithm for a given problem description and implementing that algorithm in Python. • To explain the computational problems and the challenges they pose as well as your chosen strategies and programming techniques to address them.