ECE 219 Large-Scale Data Mining: Models and Algorithms Winter 2025 Project 4: Regression Analysis and Define Your Own Task! Due on Mar 14, 2025, 11:59 pm 1 Introduction Regression analysis is a statistical procedure for estimating the relationship between a target variable and a set of features that jointly inform about the target. In this project, we explore specific-to-regression feature engineering methods and model selection that jointly improve the performance of regression. You will conduct different experiments and identify the relative significance of the different options. 2 Datasets You should take steps in section 3 on either one of the following datasets. 2.1 Dataset 1: Diamond Characteristics Valentine’s day might be over, but we are still interested in building a bot to predict the price and characteristics of diamonds. A synthetic diamonds dataset can be downloaded from this link. This dataset contains information about 150000 round-cut diamonds. There are 14 variables (features) and for each sample, these features specify the various properties of the sample. Below we describe some of these features: • carat: weight of the diamond • cut: quality of the cut • clarity: measured diamond clarity • length: measured length in mm • width: measured width in mm • depth: measured depth in mm • depth percent: diamond’s total height divided by it’stotal width • table percent: width of top of diamond relative to widest point • gridle min: refers to the thinnest part of the girdle • gridle max: refers to the thickest part of the girdle In addition to these features, there is the target variable: i.e what we would like to predict: • price: price in US dollars 2.2 Dataset 2: Wine Quality Dataset Perhaps your interests lean more towards the nuances of wine tasting rather than the than the fascination with diamonds. You can access the dataset through this link. The two datasets are related to red and white variants of the Portuguese ”Vinho Verde” wine. This dataset comprises 4, 898 instances for white wine and 1599 instances for red wine, with 13 features for each instance. 3 Required Steps In this section, we describe the setup you need to follow. Follow these steps to process either of the datasets in Section 2. 3.1 Before Training Before training an algorithm, it’s always essential to inspect the data. This provides intuition about the quality and quantity of the data and suggests ideas to extract features for downstream ML applications. In this following section we will address these steps. 3.1.1 Handling Categorical Features A categorical feature is a feature that can take on one of a limited number of possible values. If one dataset contains categorical features, a preprocessing step needs to be carried to convert categorical variables into numbers and thus prepared for training. One method for numerical encoding of categorical features is to assign a scalar. For instance, if we have a “Quality” feature with values {Poor, Fair, Typical, Good, Excellent} we might replace them with numbers 1 through 5. If there is no numerical meaning behind categorical features (e.g. {Cat, Dog}) one has to perform. “one-hot encoding” instead. 3.1.2 Data Inspection The first step for data analysis is to take a close look at the dataset. • Plot a heatmap of the Pearson correlation matrix of the dataset columns. Report which features have the highest absolute correlation with the target variable. In the context of either dataset, describe what the correlation patterns sug- gest.Question 1.1 • Plot the histogram of numerical features. What preprocessing can be done if the distribution of a feature has high skewness? Question 1.2 • Construct and inspect the box plot of categorical features vs target variable. What do you find? Question 1.3 • For the Diamonds dataset, plot the counts by color, cut and clarity. or • For the wine quality dataset, plot histogram for quality scores. Question 1.4 3.1.3 Standardization Standardization of datasets is a common requirement for many machine learning esti- mators; they might behave badly if the individual features do not more-or-less look like standard normally distributed data: Gaussian with zero mean and unit variance. If a feature has a variance that is orders of magnitude larger than others, it might dominate the objective function and make the estimator unable to learn from other features cor- rectly as expected. Standardize feature columns and prepare them for training. Question 2.1 3.1.4 Feature Selection • sklearn. feature selection. mutual info regression function returns estimated mutual information between each feature and the label. Mutual information (MI) between two random variables is a non-negative value which measures the depen- dency between the variables. It is equal to zero if and only if two random variables are independent, and higher values mean higher dependency. • sklearn. feature selection . f regression function provides F scores, which is a way of comparing the significance of the improvement of a model, with respect to the addition of new variables. You **may** use these functions to select features that yield better regression re- sults (especially in the classical models). Describe how this step qualitatively affects the performance of your models in terms of test RMSE. Is it true for all model types? Also list two features for either dataset that has the lowest MI w.r.t to the target. Question 2.2 From this point on, you are free to use any combination of features, as long as the performance on the regression model is on par (or slightly worse) than the Neural Network model. 3.2 Training Once the data is prepared, we would like to train multiple algorithms and compare their performance using average RMSE from 10-fold cross-validation (please refer to part 3.3). 3.3 Evaluation Perform 10-fold cross-validation and measure average RMSE errors for training and val- idation sets. For random forest model, measure “Out-of-Bag Error” (OOB) as well. 3.3.1 Linear Regression What is the objective function? Train three models: (a) ordinary least squares (linear regression without regularization), (b) Lasso and (c) Ridge regression, and answer the following questions. • Explain how each regularization scheme affects the learned parameter set. Ques- tion 4.1 • Report your choice of the best regularization scheme along with the optimal penalty parameter and explain how you computed it. Question 4.2 • Does feature standardization play a role in improving the model performance (in the cases with ridge regularization)? Justify your answer. Question 4.3 • Some linear regression packages return p-values for different features. What is the meaning of these p-values and how can you infer the most significant features? A qualitative reasoning is sufficient. Question 4.4 3.3.2 Polynomial Regression Perform polynomial regression by crafting products of features you selected in part 3.1.4 up to a certain degree (max degree 6) and applying ridge regression on the compound features. You can use scikit-learn library to build such features. Avoid overfitting by proper regularization. Answer the following: • What are the most salient features? Why? Question 5.1 • What degree of polynomial is best? How did you find the optimal degree? What does a very high-order polynomial imply about the fit on the training data? What about its performance on testing data? Question 5.2 3.3.3 Neural Network You will train a multi-layer perceptron (fully connected neural network). You can simply use the sklearn implementation: • Adjust your network size (number of hidden neurons and depth), and weight decay as regularization. Find a good hyper-parameter set systematically (no more than 20 experiments in total). Question 6.1 • How does the performance generally compare with linear regression? Why? Ques- tion 6.2 • What activation function did you use for the output and why? You may use none. Question 6.3 • What is the risk of increasing the depth of the network too far? Question 6.4 3.3.4 Random Forest We will train a random forest regression model on datasets, and answer the following: • Random forests have the following hyper-parameters: – Maximum number of features; – Number of trees; – Depth of each tree; Explain how these hyper-parameters affect the overall performance. Describe if and how each hyper-parameter results in a regularization effect during training. Question 7.1 • How do random forests create a highly non-linear decision boundary despite the fact that all we do at each layer is apply a threshold on a feature? Question 7.2 • Randomly pick a tree in your random forest model (with maximum depth of 4) and plot its structure. Which feature is selected for branching at the root node? What can you infer about the importance of this feature as opposed to others? Do the important features correspond to what you got in part 3.3.1? Question 7.3 • Measure “Out-of-Bag Error” (OOB). Explain what OOB error and R2 score means. Question 7.4 3.3.5 LightGBM, CatBoost and Bayesian Optimization Boosted tree methods have shown advantages when dealing with tabular data, and recent advances make these algorithms scalable to large scale data and enable natural treatment of (high-cardinality) categorical features. Two of the most successful examples are Light- GBM and CatBoost. Both algorithms have many hyperparameters that influence their performance. This results in large search space of hyperparameters, making the tuning of the hyperparame- ters hard with naive random search and grid search. Therefore, one may want to utilize “smarter” hyperparameter search schemes. We specifically explore one of them: Bayesian optimization. In this part, pick either one of the datasets and apply LightGBM OR CatBoost. If you do both, we will only look at the first one. • Read the documentation of LightGBM OR CatBoost and determine the important hyperparameters along with a search space for the tuning of these parameters (keep the search space small). Question 8.1 • Apply Bayesian optimization using skopt.BayesSearchCVfrom scikit-optmizeto find the ideal hyperparameter combination in your search space. Keep your search space small enough to finish running on a single Google Colab instance within 60 minutes. Report the best hyperparameter set found and the corresponding RMSE. Question 8.2 • Qualitatively interpret the effect of the hyperparameters using the Bayesian opti- mization results: Which of them helps with performance? Which helps with reg- ularization (shrinks the generalization gap)? Which affects the fitting efficiency? Question 8.3
EE6221-ROBOTICS AND INTELLIGENT SENSORS SEMESTER 2 EXAMINATION 2023-2024 1. A robotic manipulator with six joints is shown in Figure 1. Figure 1 (a) Obtain the link coordinate diagram by using the Denavit-Hartenberg (D-H) algorithm. (12 Marks) (b) Derive the kinematic parameters of the robot based on the coordinate diagramobtained in part (a). (8 Marks) 2. A Cartesian robot with two degrees of freedom is in contact with a workpiece, which is shown in Figure 2. Figure 2 The dynamic equations of the robot, when it is not in contact with the workpiece, aredgiven as follows: where ux, u, are the control inputs and dy,dy are the unknown constant disturbances.The stiffness of the workpiece in any direction is given as 50 N/m and the static positionof the workpiece in the v axis is 0.25 m. The system possesses unmodelled resonances at7 rad/s and 15 rad/s. (a) Assume that dy=0 and dy=0. Design a hybrid position and force controller for the robot. The motion control subspace should be critically damped, and the force control subspace should be overdamped with a damping ratio of 1.25. The control gains should be chosen to be as high as possible and the system should not excite all the unmodelled resonances. (13 Marks) (b) Assume that dy, dy are not negligible. Design an appropriate hybrid position and force controller and show that the steady state error can be eliminated. In case that the stiffness of the workpiece is wrongly estimated as 49 N/m, discuss its effects on the controller. (7 Marks) 3. (a) A mobile robot platform. with a shape of an equilateral triangle (i.e., all three sideshave the same length) attached to a square is shown in Figure 3. The robot has onesteered standard wheel, two standard wheels and one castor wheel. A local referenceframe. (x yr) is assigned at the mid-point between the steered standard wheel andthe castor wheel. The radius of each standard wheel is 6 cm and the radius of thecastor wheel is 3 cm. Let the rotational velocities of the steered standard wheel, thetwo standard wheels and the castor wheel be denoted by фss, ds1, s2, and фc,respectively. Derive the rolling and sliding constraints of the mobile robot. Figure 3 (10 Marks) (b) A robot manipulator with three ioint variables g, g2. ga are mounted on a mobile robot. The link-coordinate homogeneous transformation matrix from the basecoordinate to the tool coordinate of the rohot maninulator is given as follows where S1 = sin (q1), S2 = sin (q2), S23 = sin(q2 + q3), C1 = cos (q1), C2 =cos(q2), C23 = cos(q2+q3). Solve the inverse kinematic problem using the analytic method toexpress (q1, q2, 93)T in terms of the position of the end effector (x, y, z)T(Note: the orientation is not required). (10 Marks)
CRIM-1615H-W: Introduction to Criminology 2025WI - Online Description: In this course, students will be introduced to Criminology as a field of study. Key topics include fear and moral panics, deterrence, ‘the science of morality,’ the roles of strain, social reactions, and power / risk in constructing crime, and modern approaches to addressing crime, such as restorative justice. Learning Outcomes: The course has been developed to address several learning outcomes. Upon completion, a successful student should be able to: critically examine social constructs of crime, criminality and justice; knowledgeably examine and discuss relationships between uneven power dynamics in society, social inequality, and criminalization; knowledgeably and critically evaluate different criminological theories in terms of their genesis, strengths, limitations, and applications; critically explore present-day criminal justice, restorative justice and social justice discourses and issues in light of criminological theory. Additionally, this course emphasizes critical thinking, communication, and research skills. Texts: Students are not required to purchase a text for this course. Instead, all course readings are available in e-formats and links are provided on Blackboard. Readings: Course readings are an integral part of the Weekly Learning Modules. All course readings are in e-formats and links are provided in each module as well as the “List of Readings and Videos” section on the left-hand navigation menu.
Midterm 1 Review Lab Principles of Functional Programming Spring 2025 1 Preamble 1.1 Midterm 1 Topics The topics you can expect to be tested on the first midterm are: • Types • Evaluation – Declarations – Bindings – Function calls • Equivalence (including totality) • Recursion (including tail-recursion) • Induction – Weak/standard – Strong – Structural • Lists • Datatypes • Trees • Work/Span Here is a collection of advice regarding how to prepare for the upcoming midterm: 1. List out the topics that you require the most review on and complete review lab problems that test those concepts. • Note: Problems have been labeled by the most relevant topics that they test. 2. Review your homeworks with a focus on the mistakes you made or the problems you missed. Make sure to understand why your solution was incorrect and please ask a TA if you need help understanding why! • Note: Exam questions will likely be easier than homework questions. However, a deeper conceptual understanding will be beneficial regardless. 3. Make a cheat sheet! The professors will usually allow a cheat sheet on the exam. Some helpful things to put on your cheat sheet are: • definitions (e.g. totality, valuability, extensional equivalence) • interesting function implementations (i.e. functions that you didn’t know how to implement upon first glance) • common mistakes you made on previous assignments and how to avoid them • SML syntax, in case you are still rusty in certain areas • words of encouragement :) 4. Revisit past labs and lecture notes for further practice/review. 2 Conceptual T/F For each of the following tasks, indicate whether the statement is true or false. 2.1 Basics Task 2.1. Expressions that are not well-typed will not be evaluated. Task 2.2. If an expression is well-typed, then it is valuable. Task 2.3. If e1 =→ e2, then e1 e2 . Task 2.4. If e1 e1 =→ e2 . Task 2.5. SML evaluates the arguments to a function before stepping through the body of the function. Task 2.6. If a function f : t1 -> t2 is valuable, then it is total. Task 2.7. case (3 + 1) of (2 + 2) => " four " | _ => " not four " reduces to " four " . Task 2.8. fn (x : int) : int => x + 1 + 1 reduces to fn (x : int) : int => x + 2 . Task 2.9. The following declares a recursive function foo : int - > int : val foo = ( fn (0 : int) => 0 | n => 1 + foo (n - 1)) 2.2 Induction/Recursion Task 2.10. Consider the following declaration for foo : int - > int. If we want to prove a theorem about foo n for all positive n : int, we can use simple/weak induction to do so. | foo (n : int) : int = foo (n - 1) + foo (n - 2) Task 2.11. For which values of (A) and (B) is f 150 valuable? | f (n : int) : int = f (n - 1) + f (n - 2) (a) (A) is 0 , (B) is 1 . (b) (A) is 0 , (B) is 2 . (c) (A) is 1 , (B) is 2 . (d) (A) is 1 , (B) is 50 . Task 2.12. Any statement that can be proven by weak induction can also be proven by strong induction. For Tasks 2.13 and 2.14, suppose we are given the implent ions of two functions f : int -> t and g : int -> t. We want to show that f x = g x for all values x ≥ 0 (where x : int). (If a task is false, provide a (small) correction to the IH that could potentially make it work.) Task 2.13. The following could be a valid inductive hypothesis in an induction proof: IH: Assume that for all values x : int, we have . Task 2.14. In a strong induction proof, our inductive hypothesis does not have to cover the values of any of our base cases. For example, the quantification for y in the inductive hypothesis below is correct. BC 1: x = 0 . (proof omitted) BC 2: x = 1 . (proof omitted) IS: x > 1 . IH: Assume that for all values y : int satisfying 2 ≤ y int option REQUIRES: true ENSURES: max L =⇒ ( SOME NONE v if if v L is the max element of is empty. L fun max ’ (L : int list , acc : int option) : int option = raise Fail " your implementation here " fun max (L : int list) : int option = max ’ (L, NONE) 2.3 Lists/Datatypes Task 2.16. 1::2::3::[] is a value. Task 2.17. The type and datatype keywords are interchangeable (i.e. type and datatype declara- tions are the same). Task 2.18. Node has type tree, where the datatype declaration for tree is as follows: datatype tree = Empty | Node of tree * int * tree Task 2.19. Given the following datatype declaration for varTree , Two (One x, _ ) is a valid pat- tern. datatype varTree = Zero | One of varTree | Two of varTree * varTree Task 2.20. With structural induction on trees, we induct directly on the number of nodes in the tree. 2.4 Work/Span Task 2.21. The following implementation of rev : int list - > int list has O(n) work, where n is the length of the input list. fun rev ([] : int list) : int list = [] | rev (x::xs) = (rev xs) @ [x] Task 2.22. The best-case work for calling inord is when the input tree is balanced. fun inord (Empty : tree) : int list = [] | inord (Node (L, x, R)) = (inord L) @ (x :: (inord R)) Task 2.23. When calculating span, we assume that expressions in tuples are evaluated in parallel. Task 2.24. When calculating span, we assume that val declarations in let ... in ... end expressions are executed in parallel. Task 2.25. Suppose the recursive function foo has input type tree. In the tree method, the work tree for foo always has 2i nodes at level i when the input tree is balanced. Task 2.26. If the work of a function is in O(f(n)), then its span is also in O(f(n)). 3 Types and Evaluation Assume the following is in scope: datatype tree = Empty | Node of tree * int * tree For each of the following declarations: • If it typechecks, give the type of v; otherwise state “not well typed.” and explain why it has no type. • If v is valuable, give its value. Otherwise, give “no value.” and explain why it has no value. Task 3.1. (Recommended) val v = 3 / 2 Task 3.2. val v = 3 + 2 . 0 Task 3.3. (Recommended) val v = 1 div 0 Task 3.4. (Recommended) val v = fn x : int => x + 1 Task 3.5. val v = fn (x, y) => x andalso y = 3 Task 3.6. (Recommended) val v = fn (x : int) => Node (Empty , x, Empty) Task 3.7. val v = () Task 3.8. (Recommended) val v = [[1]] @ [] Task 3.9. val v = [150] :: [] Task 3.10. val v = fn (x : int) => x :: [1] Task 3.11. (Recommended) val v = ( fn (x : int list) => x :: []) [1] Task 3.12. fun v (x : int) : int = x = 3 Task 3.13. (Recommended) val v = if 5 > 0 then " polly " else 150 Task 3.14. fun v (Empty) = [] | v (Node (l, x, r)) = v l @ x @ v r Task 3.15. (Recommended) fun v (a, []) = ([a], a + 1) | v ( _ , x :: xs) = v ( not x, xs) Task 3.16. (Recommended) val v = let fun g [] = g ([ " 150 " ]) | g (x :: xs) = x ^ g xs in (g, g [], g [ " 3 " ]) end
FCOM6013: FINAL PROJECT TERM 2 2025 OVERVIEW At the core of an illustrator’s portfolio, alongside commissioned work, is personally inspired or self-written project work. This unit provides the opportunity to examine a subject or theme in depth through a self-determined and challenging self-initiated project, which will demonstrate the maturity of your visual communication skills. This project may take any form. you wish. It could explore a personal theme or interest, or you may choose to collaborate with an external partner working on a community-based project or creative enterprise for example. The value of this type of work in a professional portfolio is manifold: it demonstrates blue-sky and independent thinking to potential commissioners of illustration, brings new ventures to the market, explores collaborative agendas, seeks to support you in developing an individual visual voice, and allows a creative freedom not so readily evident in commissioned work. You need to make sure your project theme allows you to take conceptual and technical risks with your ideas and making and in so doing add to the current body of knowledge around the topic. It also requires you to manage and oversee your own workflow and creative process - a vital aspect of final year study. This project will most likely form. an important part of your digital portfolio to show to clients after graduation. Work you make for this unit will also be exhibited in the end of year Degree Show and be seen by potential clients and wide-ranging audiences. Alongside this, as part of the unit, you will be asked to engage with understanding the nuances and practicalities inherent in contemporary work situations and align this with your future vocational ambitions. We will call this part of the unit Professional Practice. This professional practice element of the unit will involve gathering and analysing research, visits and practitioner talks from Terms 2 and 1 and before. This will culminate in the development and creation of printed and digital forms of self-promotion that will be available to audiences at your degree show. UNIT LEARNING OBJECTIVES LO1 Experimentation & Innovation Take visual risks, speculating with a broad range of experimental approaches, and solve problems using alternative systems and processes. LO2 Research & Analysis Compile, interpret and extensively analyse a diverse range of source material, justifying methods as to support innovative design solutions. LO3 Engaging with Practice Build a clear dialogue with contemporary work settings, underpinned by appropriate links to both current design insights and future vocational ambitions LO4 Realisation & Communication Explore a wide variety of technical processes and work readiness skills, demonstrating relevance to multiple audiences and users. LO5 Personal & Professional Connectivity Demonstrate awareness of how design ideas can apply to a diverse set of cultures and communities, showing how communication is adapted by context and setting.
FBE 555: Investments Spring 2025 Problem Set 1 To carry out this problem set, you will need a computer running Microsoft Excel. You can use data from https://www.nasdaq.com/market-activity/quotes/historical and compute returns for Question 5 based on Close/Last prices ignoring dividends. You can also consult the guidelines described in the attached file Getting_Data.pdf. This problem set is due by 1:59pm PT on Wednesday, February 12, 2025. Your solutions should be in the form of a single Excel file with each question answered in a separate sheet. You can make use of the template provided separately. Make sure to include the names of all people in your team somewhere obvious inside the file. Submit the file on Brightspace. It is all team members’ responsibility that the problem set is submitted on time. It is sufficient if one of the team members makes a submission. Question 1 You deposited $100,000 in a margin account and proceeded to buy $150,000 of stockin GRPN. There is a 30% maintenance margin required by your broker. If the price of GRPN falls by 60%, how much cash will your broker require you to send if you are to keep him from selling your positions? What if GRPN falls by 30%? By what percent does GRPN have to fall before you will receive a margin call? If GRPN falls by 60% and you are unable to send any additional cash, what is the minimum fraction of your holdings in GRPN that your broker will liquidate? Question 2 Suppose you have $50,000 in cash in your brokerage account. You then short sell 20 sharesofTSLA, currently priced at $1500 per share. What is the dollar gain or loss on your portfolio, over the next year,if TSLA falls to $1250 per share? What would have been the gain or loss on your portfolio had you bought (using margin) 50 shares of TSLA instead? For both parts of the question, assume that the interest rate for borrowing and lending is 1% per year. Question 3 Without relying on published NAV figures, but using data from the ETF’s own website, compute the net asset value (on aper share basis) of the VanEck Vectors Semiconductor ETF (ticker symbolSMH). Specify clearly the date on which this calculation was made. Compare this value with the contemporaneous market price per share of SMH. Question 4 Assume that the market price of SMH was $1 higher than the value that you actually observed. If you were the manager of a large hedge fund, would you see this as a profitable trading opportunity? What would you expect as a rate of return on that trade? What would you do if the price of SMH was $1 lower? Question 5 Download the last 10 years of daily returns on three assets: 1. One S&P 500 Index ETF 2. One stock that is in the Dow Jones Industrial Average 3. One stock with a market capitalization of $100 million or less. Compute the mean and standard deviation of each. Assess whether any of the assets displays return behavior. that is inconsistent with the normal distribution. Question 6 Assume that the number of shares outstanding in the two stocks from question 5 has not changed over the last ten years. Compute the returns on equal weighted and value weighted portfolios of the two stocks (not the ETF). The return on an equal weighted portfolio is just the 50/50 average of the returns of the two stocks. The return on the value weighted portfolio is w1 R1 + w2 R2, where Ri is the return on stock i and wi is the market capitalization of stock i divided by the sum of the market capitalizations of both stocks. What are the correlations between each of the stocks and the value weighted portfolio? What are the correlations with the equal weighted portfolio? Why do you think that some correlations are higher than others?
Assessment Brief 2024/2025 Assignment Information Course Code ACCFIN5230 Course Title Artificial Intelligence in Finance 1. QUESTION/ DESCRIPTION OF ACTIVITY Individual written assignment The topic of the assignment is: “Critically assess the utility of Neural Networks in a modern financial environment.” Student guidelines: You will need to plan your answer carefully to provide a focused and succinct essay whatever your approach and reasoning ability within the word-limit. You should also demonstrate your ability to explain and apply the concepts, theories or models and to justify any conclusion you may reach based on evidence provided by all relevant course materials or any other properly referenced source you may choose to use. The assignment answers should be written in an academic and logical manner, not a journalistic style. The assignment refers to the related lectures on Neural Networks, their design and applications. Evidence of wider reading and critical thinking are strongly encouraged. The word limit is 2000 words. You should not exceed it.
Hydrosystems Engineering (EACEE 3250 / 4250) Spring 2025 Homework #1 (Due Monday, February 24th, 11:59 pm) Homework Guidelines: Your solutions to homework assignments will be submitted and graded through Gradescope (see the Gradescope tab on your Courseworks dashboard). You will have two options for submitting your work in Gradescope, either: 1) upload individual scanned images of your handwritten pages (e.g., using your phone), one or more per question; or 2) upload a single PDF that you create which contains the whole submission (e.g., merge files on your computer or phone with a software of your choice). Please use the naming convention Lastname_HWxx.pdf when submitting your homework assignment. You may choose to type up your calculations, in which case show all your steps and highlight your solution. Note: During the upload, Gradescope will ask you to mark which page/s each problem is on (see examplehere). It is important that you follow that step for grading purposes. It is acceptable to discuss problems with your colleagues, and questions are encouraged during office hours, but all work must be done independently. Make sure to clearly show all work on each problem and that your solutions are presented in an orderly fashion. It is your responsibility to make your solutions easy to grade. Topics/Chapters covered: • Chapter 1: Important properties of water; Control volume and water balance; Watershed definition and delineation • Chapter 2: Atmospheric moisture and temperature; Thermodynamics of air • Chapter 3: Basics of radiation, shortwave/longwave radiation, net radiation Problem #1 (20 pts) The relatively unique properties of water are among the reasons why it plays such a key role in the Earth's climate. Chief among these properties are the ones that couple energy and water processes. This problem is designed to provide some experience with these properties in the context of simple examples. Provide all answers in standard SI units. a) Suppose you have a 2 cm x 2 cm x 2 cm ice cube that is frozen at a temperature of 0°C. How much energy (in Joules) input would be required to melt it? b) Suppose you are making yourself 1.50 liters of tea but its temperature is 75°C, which is too hot for you to drink. In order to cool its temperature, you immerse five ice cubes of the same dimension as the one in part (a) in your tea. If the energy used to melt the ice came solely from the internal energy (i.e., temperature) of the tea, what would the reduced temperature of your tea be after all five ice cubes have melted (where you may assume tea is made up entirely of water)? For simplicity, in this class you may generally use a value of 1000 kg/m3 for the density of water. c) The tea will also cool as a result of evaporation. Suppose the evaporation rate from the tea vessel is 5 × 10-4 kg/m2/s and occurs over the tea surface area of 80 cm2. How much more will the temperature of the tea change as a result of evaporative cooling over 10 minutes? Note that you can assume the mass change in tea due to evaporation is negligible here. Problem #2 (20 pts) The reservoir behind a small Earth dam in the Midwest can be reasonably represented as a cylinder with a radius of 150 [m]. The water in it stands as it had a uniform depth of 1.0 [m] in May 2014. In May 2015 the water depth stood at 0.7 [m]. During this same period a nearby precipitation gauge measured a cumulative of 0.9 [m] of precipitation. A river feeds this pond. A river gauge measured an average of 2.0 [m3 hr-1] flow. The local farm uses the reservoir for water supply; during the year the amount used was 0.1 m3 hr-1. What was the evaporative loss from the lake during the May 2014 to May 2015 period? Problem #3 (20 pts) A large room in a museum has a 3-meter-high ceiling and its lateral dimensions are 10 by 15 meters. To preserve the artwork, it is imperative that the specific humidity be kept at 10 g/kg and air temperature maintained between 20ºC and 25ºC. Due to a power outage, the climate control system responsible for maintaining conditions in the room fails. You are called to help fix the problem and by the time you arrive your measurements indicate that the air relative humidity is 80% with an air temperature of 21ºC and air pressure of 1000 mb. You have a portable dehumidifier that takes in air from the closed room, removes water vapor by condensing the vapor into liquid, and then discharges the dry air back into the closed room. You run the dehumidifier until the specific humidity is lowered to the required value. a) What mass of water (in kilograms) will need to be condensed out to reach the required humidity level? b) What is the amount of energy released (in Joules) as a result of condensing the humidity? c) Assuming the energy computed in part b) goes into warming the air, what is the expected rise in air temperature due to the dehumidification. Note: The specific heat capacity of air is 1004 J/kg/K. For your calculation, you may assume that the change in air density (or change in air mass) is negligible as a result of the heating/dehumidification. d) Will the air temperature still be in the necessary range as a result of the dehumidification or will it need to be additionally cooled? Explain. Problem #4 (20 pts) Suppose the observed water vapor density from the surface to an altitude of 8 km at an arbitrary location is described by the following exponential decay function: where the surface vapor density (rv0) is equal to 11 g (H2O) m-3, and H is a length scale describing how quickly the variable decays with height. Suppose H = 4 km at this location. a) What is the precipitable water (in cm of water) of the 8 km atmospheric column? b) Increased greenhouse gases are expected to result in an increase in atmospheric temperature. The Intergovernmental Panel on Climate Change (IPCC) reports that the increase in the next 100 years may be between 0.6- 3.4 K. Conceptually, briefly discuss the effect that the increase in temperature will have on maximum water storage in the atmosphere (e.g., is air capable of holding more or less water?). Comment on how this might affect precipitation (frequency and rates). Problem #5 (20 pts) In order to plan the day’s irrigation rate, a farmer needs to estimate the maximum potential water loss rate by estimating the absorbed radiative energy at the surface. Over the irrigated field at noon, the ground temperature is generally 41ºC and the air temperature is 33ºC. The air dew point temperature is 16ºC. Assume a surface albedo of 30% and surface emissivity of 0.95. Based on location and day (Day 186), the cosine of the solar zenith angle at noon is estimated to be 0.974. Assume the atmospheric direct beam shortwave transmissivity at this time is 0.6, the diffuse shortwave scattering coefficient is 0.1 and the sky is cloud-free. a) Determine the net radiation at the surface. You can assume that εa = 0.809 (rather than use a model to estimate it). b) Assuming 65% of the net radiation goes into evapotranspiration (i.e., latent heat flux) at the surface, how much water should the farmer apply (in mm/day) to balance the evapotranspiration flux?
CMP-5003A Systems Analysis Learning outcomes The main overall learning outcomes are: 1) The importance of systems analysis 2) Knowledge of multiple approaches to systems analysis 3) Techniques used in the early stages of development 4) Analysis experience In more detail, by the end of this exercise students should be able to: · Demonstrate consideration of what is meant by the environment surrounding a system and the stakeholders involved · Draw up a specifications using soft systems and OO methods, and a range of techniques · Describe various elements of systems design · Carry out an analysis and preliminary design exercise for a real system · Work in a group to prepare both oral and written outlines for a proposed system Specification Overview The purpose of this assessment is to further develop an understanding of the analysis process, and to gain experience in the use of a variety of modelling techniques in a realistic setting. These skills will be gained through the analysis and preliminary design of an improved system for the UEA Parking and Permit Management system. Description UEA IT & Computing Services (ITCS) have issued the following brief on the real problem situation that will form. the basis for your summative assessment for this module. More information about this is given in the lecture session of week 5, and during a Q&A session with an ITCS representative to be organised later in the semester. “The UEA has over 3,500 staff and 17,000 students plus visitors, with a majority travelling and commuting to campus daily. The existing car parking hardware and computer system currently provided by an external supplier have reached their end of life and are causing revenue drainage. Sourcing replacement parts has become challenging and expensive, and the chip coin platform. used in the system is soon to be discontinued, posing a risk to income generation. Additionally, the system is associated with significant cyber risks. Inoperable barriers, missing chip coins, and high maintenance costs further add to the problems. The system's poor condition also poses reputational damage, which could impact UEA's public image, especially during open days. Some car parks within the scope of this project are barrier controlled with entry card readers or chip coins while others are open and use paper permits or pay and display tickets. For chip coin barrier entry, users are issued with a chip coin on entry to the car park, which they later take to a payment machine in order to pay their parking charge and have the chip coin validated for exit. Various car parks have permitted users based on their location and type of permit users hold. The following systems are involved in car park permit management and operations: PermIT System: Parking permit management software provided by an external supplier. It helps to manage parking permits for staff, students and some external user types, and implement some of UEA’s parking policy. PermIT resolves all the data from the Middleware system (see below), calculates the applicable parking charges based on the user’s pay band or category and produces a file to payroll at the end of the month for staff to be debited, while for external user types, direct debit is applied. PermIT receives data feeds from SPOT and can terminate access when people are no longer studying or working at UEA. Middleware System: Internal system maintained by the UEA development team. It processes the combination of car park entry and exit times into one single record for total charge for the day and sends it to the PermIT system. SPOT: SPOT is the identity management system that holds user information for both staff and other external users with access cards. SPOT feeds all the user information directly to PermIT every night. SPOT categorises users into a particular band, holds user email addresses and stores the status of users as active or inactive. With the data from Gallagher (below), SPOT provides the band for the user, username and status, and feeds the data to the Middleware system. Gallagher: At the car park entry barriers, users will swipe their cards on the Gallagher access control readers, and the system verifies from the SPOT identity management system if user is still active. If a user is active and permitted on the access point, access is granted. On exit, users will swipe their cards on the access control reader at the exit barrier. The Gallagher system records all car park entry and exit activities. As the current chip coin system must be phased out, UEA is keen to upgrade to ANPR (automatic number plate recognition) camera technology that can interface with the current payroll and direct debit systems. The ideal solution should be barrierless with a strategy to prevent unauthorised entry and offer multiple payment solutions. The solution should be future proof, resilient, with minimal downtime and easy to use for car park users. Additionally the solution should provide a full management framework to include processes for infringement and enforcement. There are few scenarios that would need to be catered for by the new system: · Sportspark members currently enjoy free parking after 4pm at the Main car park on weekdays and weekends. They collect a token at the barrier and validate chip coin using a chip coin validator at the Sportspark. UEA staff and students that are Sportspark members should also receive free parking benefits when they use the Sportspark rather than being charged applicable staff parking rates, but they must confirm they have used the facility before they are whitelisted for free parking. · During specific events or projects, such as clearing, the university may want to grant free parking privilege to a group of staff members. · Users may have multiple vehicles registered to their permit which can cause difficulty in determining parking charges. UEA ITCS needs help to analyse the existing processes and systems involved in permit and parking operations, in order to determine an optimal, modern, and secure solution to efficiently manage its parking system, without disrupting existing policies or causing reputational damages to the UEA.” Your group’s task is to investigate, analyse, recommend, specify and create a preliminary design for an improved system for the UEA Parking and Permit Management system. Your grouping for this exercise can be found under the Module Information tab on Blackboard. Minutes should be recorded at every group meeting. All minutes should be collated and should be submitted as an appendix in the group report. These minutes will also be used to verify your individual level of contribution to the group work, and your attendance in group meetings. The following sections describe the tasks which comprise this assessment. All tasks must be completed. Tasks Analysis tasks: 1) Produce a rich picture to depict the Parking and Permit Management “problem situation”. 2) Identify and analyse all stakeholders in the problem situation, and discuss how you would manage them based on their power and interest levels. 3) Write a short description of the possible solutions available to solve this problem, such as leaving the current system as it is, creating a custom-built solution, or using a pre-made solution. Recommend the best option from these alternatives. 4) Produce a prioritised requirements list for the proposed solution. 5) Complete a brief feasibility study of your chosen solution to the problem situation. 6) Produce a use case diagram to describe the functionality of the proposed solution. Outline design tasks: 7) Produce at least one appropriate OO UML diagram to model the system or a process within it. 8) Outline an appropriate user interface for the solution you have recommended, and describe how this design adheres to usability guidelines and heuristics. If choosing a pre-made solution, discuss the usability of the existing UI design, and produce prototypes of any changes you would make to it. 9) Outline an appropriate implementation plan for your recommended solution. Management reporting task: 10) Write a business case for your recommended solution. Meeting minutes: Record minutes at every group meeting. These should include who attended the meeting, who sends their apologies, the topics discussed, any decisions agreed upon and any actions to be taken. Individual report: Write a reflective account of your work and experience during this group project. This can include a professional critique of group engagement and progress, your view of how successful the project was, and an analysis of your own performance, including the processes and tools you used. Additionally, you should state your opinion of the contribution levels of the team by allocating a percentage rating to each member, including yourself, with the sum of all ratings being 100%. For example, in a group of 5, equal contribution for all team members would result in ratings of 20% for each person. Failure to submit your ratings of all group members will result in your own overall rating being reduced. Group presentation: Produce and deliver a presentation of your proposal to the teaching team and your fellow students, which will take place during week 12, with a date to be announced. Be prepared to answer any questions they may raise. Unauthorised absence from the presentation will result in a mark of zero for the individual(s). Those that are present but do not participate will receive only half of the group presentation mark. Attendance and participation: Each individual will receive marks for their attendance and participation in seminar sessions and group meetings from week 5 onwards. Seminar attendance will be manually recorded by the teaching team, and attendance at group meetings will be determined using the group minutes. Relationship to formative assessment Formative seminar exercises provide a good basis for many of the tasks included in this assessment. Deliverables Group report and minutes: Collate your work for tasks 1-10 above (analysis, outline design and management reporting tasks), and submit as a consistently formatted group report. Your report should state which group members were responsible for each task. Minutes of all group meetings should be submitted as an appendix to your group report. Please make sure that you include all group members’ names and registration numbers on the front page of your group report. Please state clearly any assumptions that you have made. You may also include a glossary of relevant terms as an appendix. Each member of the group should submit a copy of the group report (and minutes) with their individual report appended. Page recommendations for each task are shown below: 1) Rich picture, maximum 1 page. 2) Identification and discussion of stakeholders, recommended maximum 2 pages. 3) Description of possible solutions and recommendation, recommended maximum 2 pages. 4) Prioritised requirements list, recommended maximum 2 pages. 5) Feasibility study, recommended maximum 2 pages. 6) Use case diagram, recommended maximum 1 page. 7) UML diagram(s), recommended maximum 3 pages. 8) User interface designs and discussion of usability, maximum 8 pages. 9) Discussion of proposed implementation plan, recommended maximum 2 pages. 10) Business case, maximum 2 pages. Individual report: Each member of the group should add their individual reflective report (maximum two pages), including the contribution ratings of each member, to the end of the group report and submit a copy of this individually. You are not required to share the contents of your personal account with the other group members. Group presentation: Deliver your group presentation, each held in 20 minute slots, to include 15 minutes maximum on the presentation itself, followed by 5 minutes for questions and changeover. Presentations will be held in-person during week 12 and you will be presenting to a small number of other groups as well as the markers. A running order for the group presentations will be released in week 11. Resources · A representative from the UEA ITCS will provide further information on the brief, and will be able to answer any questions you have, during a Q&A session to be organised later in the semester. · Rich pictures are explained in the lecture content for week 7 and practical experience is gained during the following seminar. · Feasibility considerations are covered in the week 2 lecture content, and week 3 seminar. · Stakeholders and requirements are covered in detail in the week 3 lecture content. · OO modelling techniques are covered in the week 8 lecture and seminars. · Implementation methods are introduced in the week 10 material. · The recommended textbook, “Business Analysis”, Third Edition, by Debra Paul, James Cadle and Donald Yeates, published by the BCS, contains useful material on the majority of these tasks. · If you need further information, please contact a member of the teaching team via email or Teams, or after the teaching sessions. Please pass any questions related to the case study itself to the teaching team, so they can be collated and passed to ITCS. Responses to these questions will be fed directly back to your group, or posted to the announcements on Blackboard. Marking scheme Report: Group report (55%) – Marks will be awarded for tasks 1-10 (analysis, outline design and management reporting tasks) and the quality of the report and meeting minutes, and totalled to produce a group mark. This mark will be scaled for each individual according to the contribution ratings they have received from their group members, using the CMP Group Work Policy. (See appendix A for report mark scheme) Individual reflective report (10%) – Marks will be awarded for the quality and level of detail of reflection on your experience during the group project. Presentation: Group presentation (30%) – This will be assessed on both presentation quality and content including: overview, contents, visuals, structure, timing, delivery, and summary. Competence in answering questions is also important. (See appendix B for mark scheme) Attendance (5%) – Marks will be awarded for attendance levels in seminars and group meetings. Plagiarism, collusion, and contract cheating The University takes academic integrity very seriously. You must not commit plagiarism, collusion, or contract cheating in your submitted work. Our Policy on Plagiarism, Collusion, and Contract Cheating explains: · what is meant by the terms ‘plagiarism’, ‘collusion’, and ‘contract cheating’ · how to avoid plagiarism, collusion, and contract cheating · using a proof reader · what will happen if we suspect that you have breached the policy. It is essential that you read this policy and you undertake (or refresh your memory of) our school’s training on this. You can find the policy and related guidance here: https://my.uea.ac.uk/departments/learning-and-teaching/students/academic-cycle/regulations-and-discipline/plagiarism-awareness The policy allows us to make some rules specific to this assessment. Note that: In this assessment, you are permitted to work within groups proscribed by the assessment setter. Discussing solutions to tasks between groups, or groups otherwise working together to perform. the assessed tasks will be considered as a breach of university regulations. Please pay careful attention to the definitions of contract cheating, plagiarism and collusion in the policy and ask your module organiser if you are unsure about anything.
MODEL BUILDING IN MATHEMATICAL PROGRAMMING 14.24 Yield management Numbers of seats have been rounded to nearest integers where necessary. Period 1 Sell tickets at the following prices up to what is available: First £ 1200 Business £900 Economy £500 Set provisional prices for period 2: First £1150 if scenario 1 in period 1 £1150 if scenario 2 in period 1 £1300 if scenario 3 in period 1 Business £1100 for all scenarios in period 1 Economy £700 for all scenarios in period 1 Set provisional prices for period 3: First Business Economy £1500 for all scenarios in periods 1 and 2 £800 for all scenarios in periods 1 and 2 £480 for all scenarios in period 1 and scenarios 1 and 2 in period 2; £450 for all scenarios in period 1 and scenario 3 in period 2 (Provisionally) book three planes. Expected revenue is £169 544. Period 2 Rerunning the model with the demand given (with hindsight) for the price levels decided for period 1 results in the following recommended decisions for period 2. Sell tickets at the following prices up to what is available: First £1150 Business £1100 Economy £700 Set provisional prices for period 3: First £1500 for all scenarios in period 2 Business £800 for all scenarios in period 2 Economy £480 for scenarios 1 and 2 in period 2 £450 for scenario 3 in period 2 (Provisionally) still book three planes. Expected total revenue is now £172 969. Period 3 Rerunning the model with the known demands and price levels for periods 1 and 2 results in the following recommended decisions for period 3. Sell tickets at the following prices up to what is available: First £1500 Business £800 Economy £480 (Provisionally) still book three planes. Expected total revenue is now £176 392.
FMTV 218 - Audio Post Assignment – “Tiny Donuts” Assignment Value – 25 marks Take a short documentary thru audio post, from Intermediates to Rough Mix. • The Intermediates: An AAF file, an audio guide track, and a TCBI file, and supplementary Audiofiles to be provided by the student (sound effects and music). • Name the Session SecX_LastName_TinyDonuts. (e.g. SecA_Smith_TinyDonuts) This assignment tests your recall of the techniques we learned in class, techniques that I expect you to know. So I won’tlist every ‘requirement’ . But, if you checkout my YouTube videos, you’ll get reminders. And here area few helpful hints: DIALOG: • Choose your preferred microphone (lav or boom) for each subject. • The three characters are Grandfather, Father, and Son. • The panning must be corrected before you edit the dialog. B-ROLL, AMBIENCES and SPECIFICS: • Three B-roll sequences need audio, • walking towards the shop at 00:21, the entry into the shop at 02:07, and dropping the bag of flour at 03:04. MUSIC: • The AAF contains temp music that you can’t use; you must find your own (NO MP3’s). • Non-stop music is not allowed, nor is it a good idea. Find effective in & out points. • More than one piece of music is encouraged. MIX: • Balance the dialog so that all three subjects are roughly the same volume. • Balance the b-roll audio so that it doesn’t interfere with dialog, but so that it is fairly strong when featured on its own. • Balance the music so that it provides energy without interfering with the dialog. Students tend to mix music too low (barely audible) or too high (louder than dialog). BOUNCE: • Do a ‘Bounce to Disk’, Interleaved format. • Ensure it is stored in the Bounced Files folder. Criteria for grading: • OMF and PT file management /3 • PT Session organization & track management /5 • Managing mono, dual mono, & stereo /5 • Basic Dialog Editing, SFX editing, and Music editing /5 • Fades & crossfades /2 • Basic Mixing /3 • Bouncing /2 SUBMIT: • The entire PT Session Folder, including the Bounce. Do NOT include the Video File. • ZIP the Session Folder and send it to me via WeTransfer. • DEADLINE 11:59 PM. THURSDAY DEC 12th. • Late = 5 mark penalty instantly (Friday Dec 13th 12AM) + 2 marks every 24 hrs
ASSIGNMENT GUIDELINE Module: Integrity and Anti-Corruption Course (IACC) Kursus Integriti dan Antirasuah (KIAR) This guideline consists of: 1. Individual Assessment 2. Group Assessments GROUP ASSESSMENT Case Study Analysis 1. This assignment is to assess the forms of corruption and abuse of power in everyday and organizational activities (MLO2). 2. Each group is required to choose ONE local and ONE international bribery or corruption cases and do an analysis of the cases. The report must be not more than 15 pages and must include the item below. I. Introduction II. Case background III. Type of offence convicted. IV. Impact on an individual, social and organization V. Significant learnings VI. Conclusion VII. List of References 3. Use the provided template attached on myTIMeS. 4. The report needs to be submitted in PDF version. Only representative (or group leader) need to submit their group work on myTIMeS before 11 PM, 23 Feb 2025 (Sunday). 5. Include a list of all group members (full names and student IDs) on the cover page of the assignment. 6. Avoid all forms of plagiarism (e.g., copy-pasting) and ensure proper citation using the APA format. Students found guilty of plagiarism will receive zero (0) marks and may face disciplinary action. 7. Ensure compliance with the submission deadline. Late submissions will not be accepted, and no marks will be awarded for any late submission. Short Video Task 1. The format of the video is based on students' creativity. The video can be presented in the news, talk shows, drama, forums, advertisements, etc. Video must be creative, precise, and produced by the students. 2. Your group video duration must be between 3 to 5 minutes. All members don't need to appear in the video, but all group members must contribute to the project's production. 3. Start your video by introducing the chosen issue, followed by the video content and end the video with the conclusion and credit. 4. Students must use English as a medium of communication throughout the video. However, other languages are allowed depending on the situation and need. Please provide the subtitles in your video if you are using other languages. 5. The video must be uploaded on YouTube, and each group member must promote the YouTube video link on their personal social media (FB, Instagram, TikTok). All the group members need to screen shoot/ snap the posting/sharing on social media as proof of their social media campaign. 6. Share the proofs of the social media campaign of all group members in your e- report (under the section social media campaign). 7. Avoid recording videos or discussing sensitive issues, fake news, or false information. Verify the accuracy of all information or facts before publishing. Each group is responsible for the content they publish on digital platforms. 8. Please use the hashtag below in your social media campaign: #IACCTaylorsUniversity #KIARTaylorsUniversity #fightcorruption #saynotobribery
EE6221-ROBOTICS AND INTELLIGENT SENSORS SEMESTER 2 EXAMINATION 2022-2023 1. A robotic manipulator with seven joints is shown in Figure 1. Figure 1 (a) Obtain the link coordinate diagram by using the Denavit-Hartenberg (D-H) algorithm. (12 Marks) (b)।Derive the kinematic parameters of the robot based on the coordinate diagramobtained in part (a). (8 Marks) 2.The dynamic equations of a robot, which is in contact with a frictionless surface, are given as follows: where qi, q2, q3 are the joint variables, u1, u2, u3 are the control inputs and g = 9.8 m/s2 is the acceleration due to gravity. The first two joints possess unmodelled resonance at 12 rad/sec and the third joint possesses unmodelled resonance at 16 rad/sec. The contact force exerted on the environment is given by: f = 10(q3 - 0.1). (a) Design a hybrid position and force controller for the robot so that the first two joints are critically damped, and the third joint is overdamped with a damping ratio of 1.2. The gains should be as large as possible, and the system should not excite all the unmodelled resonances.(14 Marks) (b) The controller designed in part (a) is now implemented on the robot in a space station, without any modification. Explain the possible effects and derive the error equations.(6 Marks) 3. (a) A mobile robot with two castor wheels, one standard wheel and one steered standardwheel, is shown in Figure 2 on page 3. A local reference frame. (xr, yr) and a steered angle ß are assigned to the mobile robot as shown in Figure 2. The radius of each standard wheel is 7cm and the radius of each castor wheel is 3cm. If the rotational velocities of the steered standard wheel, standard wheel and the two castor wheelsare denoted by φss, s,c1 and φc2, respectively, derive the rolling and slidingconstraints of the mobile robot. (10 Marks) Figure 2 (b) A robot manipulator with three joint variables q1, 92, 93 are mounted on a mobile robot. The link-coordinate homogeneous transformation matrix from the base coordinate to the tool coordinate of the robotic manipulator is given as: where S1 = sin (q1), S2 = sin(q2), C1 = cos (q1), C2 = cos (q2). (i) Solve the inverse kinematic problem using an analytic method to express (q1, q2, q3)T in terms of the position of the end effector (x, y, z)T. (Note: orientation is not required). (ii) If the robot is used to pick up an object with a centroid given as x= 0.15, y=0.25, z=0.025 units, calculate the joint configuration. Discuss the problems associated with this task. (10 Marks)
Financial Risk Management-Fixed Income Markets In this project you will implement the mortgage calculator is Python: 1. Write a function in Python that will calculate the mortgage payment with the following parameters: a. yield (the yield should be already adjusted to unit of payment) b. Principal c. Number of payments 2. Create a payment column using a replication function in python (hint use numpy arrays and repeat function (see here: R function rep) in Python (replicates elements of a list/vector) - Stack Overflow) 1. Create a PV column using the formula : P/(1+y) ^ (payment number) 2. Create a first element in the column for outstanding principal assuming that Now Principal utstanding = Intial PrincipalUse the for loop to update the next element in each of the columns as follows; 1. calculate the interest paid through the formula: next interestpaid-principal outstanding*yield 2. calculate the next principal paid as principal paid = payment - interest paid 3. calculate the next amount utstanding = principal outstanding - principal paid 4. complete the amortization table by calculating the PV of principal and interest paid.
Case 14: Chestnut Foods Suggested questions for discussion: 1. What is the imminent key issue faced by the firm? What is the key issue of the case? 2. What are the implications of Meyer’s diagram for capital allocation at Chestnut Foods? Would allocating capital on the basis of the risk-adjusted hurdle rates create or destroy value? 3. What are your comments on Suchecki’s opinion against the use of multiple hurdle rates? Evaluate each viewpoint one by one. 4. What are the appropriate hurdle rates for the two segments? 5. Is the Instruments division underperforming, as suggested by Van Muur? 6. How about the Food Products division? Is it creating or destroying value? 7. What are your recommendations on: a. the appropriate hurdle rate(s) on the two segments? b. how to respond to Van Muur?
HR9537: Management Research and Analysis Instructions on Assessment: During the course of the module, you will develop a research question in enough depth that you are able to create a research strategy. The ultimate deliverable you must provide is an INDIVIDUAL research strategy of 2,500 words in length (excluding appendices) which is worth 100% of the module grade. Your research strategy must include comparison, appraisal, and reflection on the detail you include on the elements outlined in the template below. You are advised to include examples of the work developed throughout the module as appendices e.g. rich pictures, surveys, hypothesis tests, thematic maps, analysis and dashboards etc. which you will refer to in the strategy to support your appraisal and reflection of your choices. The report developed will provide the foundations for developing research proposals in workplace research projects and / or Level 6 study, therefore aiding placement work and final year capstone options: Dissertation, Management Enquiry and Consultancy Project. The following section provides a template structure for you to use as a guide to develop your strategy. Research Strategy Template with Mapping to Module Teaching This is a draft template structure for a research strategy, based on the areas covered in the module. The table below provides a brief overview of the elements to be included in the strategy, with mapping to where the different elements are covered in the module i.e. the week(s) the topics are covered in the Teaching and Learning Plan (TLP). Key points: Ø The Research strategy must be developed based upon an individual research question which you will develop during the teaching weeks of the semester. Tutors will offer guidance in the development of this strategy. Ø You need to develop an individual research strategy, which must be made up of the elements outlined in the table below. It is strongly recommended that you add and amend the strategy as you work through the module so that a full draft is complete by the start of week 12 in time for the revision support sessions. Ø The strategy should include any appropriate documentation and / or links developed over the course of the module as appendices e.g. rich pictures, survey designs, hypothesis tests, thematic maps, example analysis outputs. Ø You must include reasoning and justification of your choices throughout the strategy, supported by appropriate appendices. Strategy Sections (with grade weighting for each section). The grade for each section will take into account the appropriate supporting work provided in the appendices) Wk(s) covered 1. Introduction (10) · In this section, you will introduce the strategy, highlight the problem, and provide background / rationale for the topic. It must address the overall aim of your proposed study. · You must set the scene by outlining the area / topic of investigation. This must be a business-oriented research topic · The topic could be based on a rich picture. If you do this it is useful to add your rich picture to an Appendix Week 2 2. Development of research questions / hypotheses (10) · This section will provide further insights into the topic, incorporating academic literature and industry insights to highlight the problem · Research question(s) and discussion of how you arrived at this research question (e.g. based on insights gathered from literature) · Provide hypotheses and your rationale for choosing them. Week 3 3. Research Methodology Choice (20) · An overview of the methodological options (qualitative or quantitative) that could be used to understand the problem further and answer the research questions / hypotheses. · Whilst some larger research projects with greater resource and time may use a mix of methodologies, you must select only one method as your primary choice. You must justify your choice of methodology with reasoned argument, analysis, and comparison as to why you feel it is the best choice in comparison with the other options. Weeks 3-4 4. Detailed description of the Research Method to be used (20) · What specific method do you propose to use (e.g. survey, interview, focus groups, experiment, observation, etc.) and why · What resources would be needed to carry out this method (e.g. access to software, time, human resources, consent, etc.) · Who would be the ideal participants of the study e.g. customers, general public, employees, managers, competitors, etc., and the number of participants to be involved e.g. number of interviewees, focus groups or survey sample size · Ethics: Outline of the ethical considerations to be adopted e.g. data security, anonymity, participant rights Weeks 3-4 5. Analysis Methods (20) · What method(s) of analysis do you propose to use e.g. statistical, thematic, content · What tools would be needed to conduct the analysis e.g. SPSS, Excel, Power BI, NVivo) · What specific tests / analysis would be conducted on the data gathered from the quantitative or qualitative research e.g. statistical, thematic etc. Whilst you would not normally have collected data at this stage you will be provided with some sample data related to a case study in class for you to carry out appropriate tests on. You will then be able to include examples of the test outputs in your appendices to demonstrate and support your reasoning for proposing the use of them in relation to your research question. Weeks 5-10 6. Deliverables (10) · How do you propose to present the analysis to the appropriate audience e.g. report, tables, graphs / charts, dashboard etc. Examples should be included in appendices. Week 11 7. Conclusion (10) · A short conclusion of the strategy to summarise the key points made Week 11 Marking & Feedback: Marks will be awarded against the Template outlined on Page 2. The comments and feedback can be generally categorised as follows, and refers to the skill level evidenced in areas like writing, cross referencing, examples, use of references and appendices, presentation, grammar, layout Excellent / Outstanding Very Good Good Average Below Average Poor Comments 70-100% 60-69% 50-59% 40-49% 30-39% 0-29% Feedback and Feedforward comments will be provided in a marking rubric on Turnitin.
Investments: Practice Exam I Question 1 Stock A’s market cap is $167.71 million and the company has 3.1 million shares outstanding. What is Stock A’sprice? A) $54.1 B) $58.6 C) $546.1 D) $38.5 Question 2 You observe that a stock’s price goes up by 45% in a week and try to interpret that using the present value relation. That is, prices are discounted values of expected future cash flows, like in the Gordon growth formula. Which of the following are potential drivers of this high return? I. Investors revised their cash flow expectations for the company upward upon a positive earnings surprise. II. Investors discount future cash flows from the company at a lower rate as the company has become riskier. III. The stock has become more valuable due to a decrease in risk. A) Only I B) Only III C) I and III D) II and III Question 3 You are an entrepreneur who is deciding between offering a defined contribution (DC) or a defined benefit (DB) pension plan to your employees. Which of the following is a correct consideration for your decision? A) A DC plan is riskier for your company due to fixed liabilities in the future. B) With a DB plan, your company would take more investment risk while the employees take less risk. C) If you decide to offer a DB plan, your pension fund does not need to make portfolio choice decisions. D) Investors are guaranteed to have a higher retirement income if you choose a DB plan. Question 4 Suppose that new petroleum reserves are discovered in a country and oil extraction is expected to generate a substantial revenue stream for the government. The government wants to leave some of the accumulated wealth to future generations in the form of a diversified portfolio that is not strongly affected by oil price fluctuations. Which one of the following strategies for using oil revenues would help achieve that goal? A) Building a sovereign wealth fund that invests in international equity and bonds B) Investing the revenues in equity shares of domestic oil companies C) Relocating resources from other industries to the oil industry D) Investing oil revenues into the domestic infrastructure Question 5 Suppose that you are comparing two stocks: Stock A ranks high in terms of price-earnings ratio while Stock B has a much lower price-earnings ratio. Which of the following characterizations of Stocks A and B is correct on average? A) Stock A is a value stock and Stock B is a growth stock. B) Stock A is likely to have a higher dividend yield compared to Stock B. C) Stock A’s price is driven by cash flow expectations in the longer run compared to Stock B. D) Stock A and B’s prices have likely had a similar path in the recovery period from the Covid-19 crisis. Question 6 The current order book (CLOB) is as follows: BID ASK Price Size Price Size 82 200 84 300 81 200 86 500 79 500 87 200 77 300 90 400 Suppose you place a market order to buy 1000 shares. What is the average price you pay per share? A) $84.0 B) $85.0 C) $85.6 D) $86.6 Question 7 The current order book (CLOB) is as follows: BID ASK Price Size Price Size 82 200 84 300 81 200 86 500 79 500 87 200 77 300 90 400 Suppose you place a limit order to buy 100 shares at $82.2. What are the new bid and ask prices? A) $82.2 and $84 B) $82 and $84 C) $81 and $84 D) $82.2 and $86 Question 8 You have deposited $250,000 into a new margin account. You buy 200 shares ofTSLA, which currently has a share price of $2,100. You are required to maintain a 25 percent maintenance margin. If TSLA’s share price drops to $1,000, how many shares (to the nearest integer) will you have to liquidate to be in compliance with your margin agreement assuming that you cannot deposit any additional funds? (Assume zero interest on the margin account.) A) 60 B) 70 C) 80 D) 90 Question 9 You deposit $13,750 of your own funds into a margin account with the intention of buying shares of XYZ which is currently selling for $275 per share. How many shares can you purchase given an initial margin requirement of 50 percent? A) 50 B) 20 C) 40 D) 100 Question 10 The best bid and best ask prices for the ARKK ETF are $82.00 and $82.50, respectively. 1000 shares are available at the bid price and 300 shares are available at the ask price. You decide to submit a market order to sell 450 shares. Just before you get your order in, another trader submits a limit order to buy 350 shares at a price of $82.45 or better. What is the total price that you end up being paid (not including any brokerage commission) for your 450 shares? A) $37,102.5 B) $37,057.5 C) $36,900.0 D) $34,567.5 Question 11 Which of the following statements is correct? A) Exchanges provide a decentralized market structure where prices are determined via bargaining. B) The central limit order book lists all limit orders from best to worst from the perspective of investors that place market orders. C) Most corporate bonds trade in centralized markets while stocks typically trade in decentralized markets. D) The same security is more likely to trade at different prices at the same time in a centralized market than a decentralized market. Question 12 Suppose you have $125,000 in a margin account with a 50% initial margin. You decide to use your entire buying power to purchase the Apple stock. The broker requires a maintenance margin of 40%. Now suppose that the Apple stock falls by 23%. What is the size of the margin call you will receive? A) $14,250 B) $16,100 C) $18,400 D) $9,500 Question 13 You decide to purchase 700 shares of the ARKK ETF using 50% margin. The current price of the ARKK ETF is $310 per share. Assume that all interest rates are zero. If the price of the ARKK ETF falls to $280, what is the rate of return on your investment (assuming no distributions)? A) -19.36% B) -9.68% C) 19.41% D) 38.82% Question 14 Consider a mutual fund that that has 15 shares outstanding and you are trying to calculate the NAV of the fund. The fund holds $3,400 in cash, 17 shares of Apple (AAPL) and 45 shares of JPMorgan Chase (JPM). AAPL is current selling for $121 per share, and JPM is selling for $98 per share. What is the NAV of the whole fund? A) $9,867 B) $10,720 C) $6,467 D) $4,789 Question 15 Which of the following statements about investment funds is correct? A) If the underlying assets of a mutual funds are illiquid as is the case for corporate bonds, the fund may face a high redemption volume by investors that exploit the difference between NAV and the underlying asset value. B) Passive funds charge higher fees than active funds as compensation for portfolio research. C) A hedge fund manager who observes the NAV of a fund being higher than the fair value of the fund’s portfolio would buy more fund shares. D) The choice between two funds that have expense ratios of 0.4% and 2% typically only depends on the risk and return profile, and not on the difference in fees. Question 16 Fund X’s total annual revenue originating from fees is $2.2 million and the fund’s assets under management (AUM) is $400 million. What is Fund X’s expense ratio? A) 0.45% B) 0.55% C) 0.75% D) 5.50% Question 17 Which of the following statements is correct regarding ETFs and mutual funds? A) ETF prices are determined in a centralized market throughout the trading day. B) Mutual funds are more transparent about their portfolio holdings compared to ETFs. C) An investor considers one active and one passive fund for his $1,000 investment, for which Fund A charges a $8 annual fee while Fund B charges only $1.3. Fund B is more likely to be the active fund. D) Investment funds lost their popularity among equity investors over the last seven decades. Question 18 Suppose that Stock A has 3 million shares outstanding and the share price appreciates from $40 to $137. In the meantime, the stock pays a dividend of $1.5 per share. What is the total amount of profit that the investors make from holding the stock? A) $296 million B) $291 million C) $551 million D) $430 million Question 19 Suppose that Stock A’s share price is $199 at the end of January and $234 at the end of the following February. At the end of February, the stock pays a dividend of $12. What is Stock A’s monthly rate of return? A) 23.6% B) 17.6% C) 6.0% D) 12.1% Question 20 An investment fund invests in three stocks: Stocks A, B and C. The fund’s allocation to A is $270 million, allocation to B is $230 million, and allocation to C is $170 million. Suppose the monthly return was -20% for A, -37% for B, and 4% for C. What are the equally weighted and value- weighted monthly returns of the fund portfolio, respectively? A) -15.0% and -9.4% B) 18.3% and 14.2% C) -17.7% and -19.7% D) 18.3% and -9.4% Question 21 Suppose that Stock A’s share price is $160 at the end of December and $220 at the end of the following January. At the end of January, the stock pays a dividend of $10. What is Stock A’s monthly capital appreciation rate? A) 41.2% B) 37.8% C) 14.6% D) 5.1% Question 22 Suppose that Stock A’s share price is $160 at the end of December and $220 at the end of the following January. At the end of January, the stock pays a dividend of $10. What is Stock A’s monthly dividend yield? A) 44.4% B) 38.9% C) 6.3% D) 12.1% Question 23 Suppose that the annual risk premium of a stock is 14% and the standard deviation of the stock’s annual returns is 31%. What is the stock’s Sharpe ratio? A) 0.35 B) 0.45 C) 0.55 D) 0.65 Question 24 Which of the following ranking of asset classes from the least risky to the riskiest is correct where risk is measured by return volatility? A) World stocks, Treasury bills, U.S. houses B) Treasury bills, U.S. large stocks, U.S. small stocks C) U.S. large stocks, U.S. long-term Treasury bonds, U.S. large stocks D) Treasury bills, U.S. large stocks, U.S. long-term Treasury bonds Question 25 You are assessing the Sharpe ratio of a fund and find that it is 0.64 based on historical data. The riskless rate is 2% and the fund’s annual return volatility is 18%. What is the fund’saverage annual return? A) 7.5% B) 9.5% C) 11.5% D) 13.5%
FIT5202 DATA PROCESSING FOR BIG DATA Semester Two 2019 Question 1 a. What is Apache Spark? What are the two advantages of unified stack in Spark? (1 + 2 = 3 Marks) b. Define RDD. RDDs are lazily evaluated. What does it mean? Can RDDs be shared between SparkContexts? Please explain. Give two examples of how RDD can be created using SparkContext (assume ‘sc’ as a SparkContext object). (1 + 1 + 1 + 2 = 5 Marks) c. Write a program that does word count of the words ERROR and WARN found in the web server logs called “logs.txt”. The contents of “logs.txt” file is shown below. Please complete the program in the space provided. (2 Marks) INFO This is a message with content INFO This is some other content WARN This is a warning ERROR Something bad happened WARN More details on the bad thing from pyspark import SparkContext # Start your code here sc = SparkContext(master=“local[2]”, appName=“Errors and warnings Count”) lines = sc.textFile(“logs.txt”) Question 2 a. What are broadcast variables? Why do we need broadcast variables when working with Apache Spark? (1 + 1 = 2 Marks) b. We want to perform. a log analysis. The input data consists of log messages of varying degrees of severity, along with some blank lines. We want to compute how many log messages appear at each level of severity. The contents of “input.txt” file is shown below. INFO This is a message with content INFO This is some other content (empty line) INFO Here are more messages WARN This is a warning (empty line) ERROR Something bad happened WARN More details on the bad thing INFO back to normal messages The expected output of the operations is as below. [(‘INFO’, 4), (‘WARN’, 2), (‘ERROR’, 1)] Write the code that will produce the expected output Assume spark context object ‘sc’ has already been initialised. (5 Marks) from pyspark import SparkContext # Start your code here sc = SparkContext(master=“local[2]”, appName=“Errors and warnings Count”) Input = ssc.textFile(“input.txt”) c. List four different sections of Spark Web UI and briefly explain all of them. (1 + 2 = 3 Marks) Question 3: a. What is data visualisation? List two importance of data visualisation? What are the factors you need to be aware of before visualising the data? Please explain. (1 + 1 + 2 = 4 Marks) b. What are the benefits of using Apache Spark and MongoDB together? Assume you have a database named “FIT5202” and a collection named “zips” in MongoDB database. The information on the attributes are as follows ● The _id field holds the zip code as a string. ● The city field holds the city name. A city can have more than one zip code associated with it as different sections of the city can each have a different zip code. ● The state field holds the two-letter state abbreviation. ● The pop field holds the population. ● The loc field holds the location as a latitude longitude pair. Assume that spark session object (i.e. spark) has been initialised. The analysis required is “Find the states with populations above 10 Million”. Alice, our data analyst, is only familiar with SQL queries so she provided you with the following SQL query: SELECT state, SUM(pop) AS totalPop FROM zips GROUP BY state HAVING totalPop >= (10*1000*1000) You read the data from the MongoDB using the command below: zips_df = spark.read.format("com.mongodb.spark.sql.DefaultSource").load() Use the functions provided by dataframe. to find the states with populations above 10 million. (1 + 5 = 6 Marks) Question 4 a. What is Machine Learning and why should you use machine learning with Spark? In Apache Spark, machine learning pipelines provide a uniform. set of high-level APIs built on top of DataFrames. It makes easier to combine multiple algorithms into a single pipeline, or workflow. The key concepts introduced by the Pipelines API are DataFrame, Transformer, Estimator, Pipeline, and Parameter. What is a Transformer and an Estimator? (2 + 2 = 4 Marks) b. Suppose we have a set of data comprising; height, weight and shoe size of some customers. The aim is to predict the shoe size of a new customer given only height and weight information. Height (in cm) Weight (in kg) Shoe Size 158 58 36 158 59 36 158 63 36 160 59 38 160 60 38 163 60 38 163 61 38 163 64 40 165 64 40 165 61 40 165 62 40 168 65 40 168 62 40 Write the formula to calculate the Euclidean distance? A new customer named “Matthew” has height 161 cm and weight 61 kg. Using kNN, for k = 5, what is the (most) unlikely shoes size of Matthew? (2 + 4 = 10 Marks) Question 5 a. What is the difference between Supervised Learning and Unsupervised Learning? Mention any two differences. (2 Marks) b. Consider the following data set consisting of the scores of two variables on each of seven individuals: Use the k-means algorithm to cluster the data in two clusters. The distance of each data point and the centroid (or mean) is calculated using Euclidean distance. The formula to calculate the euclidean distance is given below. (8 Marks) Question 6 a. ‘People who bought this also bought…’ recommendations seen on Amazon is based on which algorithm? What is the difference between Association Rules and Collaborative Filtering? (1 + 1 = 2 Marks) b. A simplistic implementation using ALS (Alternating Least Squares) is given below. The goal is to recommend movies to users. Details of the dataset is given below. The full data set (“u.data”), 100000 ratings by 943 users on 1682 items. Each user has rated at least 20 movies. Users and items are numbered consecutively from 1. The data is randomly ordered. This is a tab separated list of user id | item id | rating | timestamp The time stamps are unix seconds since 1/1/1970 UTC. The sample data contents of “u.data” file is shown below. 196 242 3 881250949 186 302 3 891717742 22 377 1 878887116 244 51 2 880606923 166 346 1 886397596 Write the necessary code below to develop an ALS based recommendation model for movie recommendations. First examine the dataset and perform. necessary steps to convert the dataset into DataFrame. to make it ready for the algorithm. (8 Marks) from pyspark import SparkContext from pyspark.sql import SparkSession, Row from pyspark.ml.recommendation import _______________________________ from pyspark.ml.evaluation import_____________________________________ appName="Collaborative Filtering with PySpark" # initialize the spark session spark = SparkSession.builder.appName(appName).getOrCreate() # get sparkcontext from the sparksession sc = spark.sparkContext # Step1: the data is loaded to an RDD movielens_rdd = # Step 2: process the data into appropriate structure # Step 3: convert the rdd to dataframes # Step 4: split the dataset into training and test data (70% training and 30% test) (trainingData, testData) = # Step 5: build the recommendation model using ALS on the training data # Use maxIter = 5, regParam = 0.01, coldStartStrategy = “drop”, # implicitPrefs = False # Step 6: predict the top movies for some selected users predictions = model.transform(testData) # Step 7: find and print the accuracy of the model
