ECON2020-II&III : Intermediate Macroeconomics Spring, 2019 Midterm Exam 1 True or False Questions 1. The money multiplier might fall if the Fed increases the reserve require- ment for Banks, holding other things constant. 2. The personal consumption deflator (PCE) is a better price index than CPI because the basket of PCE varies over time and hence allows for substitution effect. 3. According to the quantity theory of money, the inflation rate equals the percentage change in money supply. 4. A tenant buys the house he has been renting, then GDP remains un- changed because there is no change in consumption, investment, govern-ment expenditure and net export. 5. For a closed economy with constant return to scale production function, national income equals to the sum of labor income and capital income. 6. In an open economy, since a country can always borrow from overseas, its trade balance might not necessarily equal net capital outflow. 7. Money is neutral in long run, but in short run it does affect real output. 8. Net export of a country will fall as its nominal exchange rate rises provided the ratio of domestic price and foreign price remains constant. 9. If the separation rate is 20% and the rate of finding a job is 40%, then the natural rate of unemployment is 3/1. 10. A fallin the capital-labor ratio results in a fallin the capital output ratio, holding other things constant. 2 Multiple Choice Questions 1. Discouraged workers are counted as: (a) part of the labor force (b) out of the labor force (c) employed (d) unemployed 2. If the adult population equals 250 million, of which 145 million are em- ployed and 5million are unemployed, the labor force participation rate equals (a) 50% (b) 58% (c) 60% (d) 96.67% 3. A competitive, profit-maximizing firm hires labor until the: (a) marginal product of labor equals the nominal wage (b) price of output multiplied by the marginal product of labor equals the nominal wage. (c) real wage equals the real rental price of capital. (d) wage equals the rental price of capital. 4. All of the following are causes of structural unemployment except: (a) minimum-wage laws (b) the monopoly power of unions (c) unemployment insurance. (d) efficiency wages 5. If the rate of separation (s) is 0.02 and the rate of job finding (f) is 0.08 but the current unemployment rate is 0.10, then the current unemployment rate is the equilibrium rate, and in the next period it will move the equilibrium rate. (a) above; toward (b) above; away from (c) below; toward (d) below; away from 6. In a small open economy, when the government reduces national saving, the equilibrium real exchange rate: (a) rises and net exports fall. (b) rises and net exports rise. (c) falls and net exports fall.. (d) falls and net exports rise. 7. If there are 100 transactions in a year and the average value of each trans- action is $10, then if there is $200 of money in the economy, transactions velocity is times per year. (a) 0.2 (b) 2 (c) 5 (d) 10 8. The ex ante real interest rate is based on inflation, while the ex post real interest rate is based on inflation. (a) expected; actual. (b) core; actual. (c) actual; expected.. (d) expected; core.. 9. If the real exchange rate is high, foreign goods: (a) and domestic goods are both relatively expensive (b) and domestic goods are both relatively cheap. (c) are relatively expensive and domestic goods are relatively cheap.. (d) are relatively cheap and domestic goods are relatively expensive. 10. In a steady state: (a) no hiring or firings are occurring (b) the number of people finding jobs equals the number of people losing jobs.. (c) the number of people finding jobs exceeds the number of people losing jobs (d) the number of people losing jobs exceeds the number of people finding jobs 11. A competitive, profit-maximizing firm hires labor until the: (a) marginal product of labor equals the nominal wage (b) price of output multiplied by the marginal product of labor equals the nominal wage. (c) real wage equals the real rental price of capital. (d) wage equals the rental price of capital. 12. Use the graph above to answer the question. In a small open economy, if the world interest rate is r1 , then the economy has (a) a trade surplus (b) balanced trade. (c) a trade deficit (d) negative capital outflows 13. Suppose the money supply in Canada grows more slowly than the money supply in the USA, holding other things constant. We would expect that? (a) The Canadian dollar should appreciate relative to the US dollar. (b) The Canadian dollar should depreciate relative to the US dollar. (c) The Canadian dollar should maintain a constant exchange rate with the US dollar because of purchasing power parity. (d) None of the above 14. A small open country recently passed a law to increasing military spend- ing. Then (a) Its normal exchange rate increases and net export falls. (b) Its real exchange rate falls and net export increases (c) Its real exchange rate increases and net export falls (d) Its real exchange rate increases and net export remains the same 15. Suppose k = 4y , δk = 0.2y and MP K × k = 0.4y, the economy’s real GDP grows an average of 5% per year (a) The economy is below the golden rule steady state (b) The economy is above the golden rule steady state . (c) The economy is just at the golden rule steady state. (d) Can not determine because of insufficient information.
Quiz 1 CSCI-567 – Machine Learning Fall 2024 1. Assume a linear regression model parameterized by θ . Denote the linear model by fθ (x(i)) = θ Tx(i) . Which of the following represents the gradient of the loss function L(θ) in gradient descent for linear regression? 2. Given a linear classifier f(x) = wTx + b, which of the following conditions must hold for a dataset to be linearly separable? a) 3w,b such that y(i)(wTx(i) + b) > 0 for all i b) 3w,b such that y(i)(wTx(i) + b) = 0 for all i c) w,b, y (i)(wTx(i) + b) = 1 for all i d) w,b, y (i)(wTx(i) + b) < 0 for all i 3. Which of the following optimization problems represents the regularized binary logistic regression objective with ℓ2-regularization? 4. Which of the following is an example of a convex surrogate loss function? a) Hinge loss b) Squared loss c) Cross-entropy loss d) All of the above 5. In gradient descent, the update rule is given by θ (t+1) = θ (t) − η∇J(θ(t)). What condition must be true for the algorithm to converge? a) η must be very large to speed up convergence b) ∇J(θ(t)) must be negative c) η must change at every step d) η must be small enough to ensure the loss function decreases at each step 6. Which of the following expressions represents the gradient of the logistic regression loss function L(θ), where fθ 1+e−θT x/1? 7. Which of the following surrogate loss functions is convex and differentiable, and used in binary logistic regression? a) L(θ) = max(0, 1 − yfθ (x)) b) L(θ = log(1 + e−yfθ (x)) c) L(θ) = (y − fθ (x))2 d) L(θ = |y − fθ (x)| 8. What is a surrogate loss function used for? a) To approximate the true error function in a more tractable form. b) To reduce the number of features in the model c) To avoid overfitting by adding a regularization term d) To directly minimize the classification accuracy 9. Which of data below is not linearly separable? (Hint: consider visualizing the data) a) Class 1: [(-1,0), (-1,2)], Class 2: [(1,2), (1,0)] b) Class 1: [(-1,0), (1,0)], Class 2: [(0,1), (0,-1)] c) Class 1: [(-1,0,0), (-1,2,0)], Class 2: [(1,2,0), (1,0,0)] d) Class 1: [(1,1,2), (2,1,1), (1,2,1)], Class 2: [(0,-1,-1), (-2,0,-1), (-1,-2,0)] 10. Given a dataset matrix X ∈ Rm ×n where m < n, and each column of X represents a data sample. Let C be the covariance matrix of the dataset, and assume the mean vector of the samples µ ∈ Rm is a zero vector. Which of the statements below is definitely correct? (a) C ∈ Rn ×n (b) rank(C) = rank(X) (c) C does not have any negative elements. (d) C is a positive definite matrix. 11. Which of the followings about neural networks is wrong? a) The attention layer in neural networks typically uses the ReLU activation function. b) Convolutional Neural Networks (CNNs) are inherently designed to be rotationally invariant. c) Dropout techniques are employed to enhance the generalizability of the model. d) Transformers can also be effectively trained to classify images. 12. Which of the following statements is wrong? a) Overfitting occurs when a machine learning model learns to capture noise or random fluctuations in the training data, resulting in poor generalization. b) Regularization techniques can help prevent overfitting. c) Both ℓ 1 and ℓ2 regularization can help in reducing the variance of a model by penalizing overly complex models. d) CNNs do not work well on linearly non-separable datasets. 13. Regarding activation functions in neural networks, which statement is true? a) The sigmoid activation function is preferred over ReLU in deep neural networks to avoid vanishing gradient problems. b) The tanh activation function outputs values in the range [0 , 1]. c) The ReLU activation function is defined as f(x) = max(0, x2 ). d) The softmax activation function is commonly used in the output layer for multi-class classification problems. 14. Regarding convolutional neural networks (CNNs), which of the following statements is false? a) Pooling layers in CNNs help to reduce the dimensionality and retain important features of the input. b) The stride in a convolutional layer determines the step size by which the convolution filter moves across the input. c) Increasing the number of filters in a convolutional layer always reduces overfitting. d) Padding is used in convolutional layers to control the spatial size of the output feature maps. 15. Consider aneural network with a ReLU activation function, f(x) = max(0, x). Let z = wtx+bbe the linear transformation of the input x with weight matrix W and bias vector b. What is the gradient of f(z) with respect to x when z < 0? a) w b) b c) 0 d) diag(z)W 16. Which of the following statements about the transformer architecture is false? a) Transformers use self-attention mechanisms to process input sequences in parallel, unlike recurrent neural networks (RNNs) which process inputs sequentially. b) The positional encoding in transformers allows the model to understand the order of elements in the input sequence without using recurrence. c) Transformer models always require both encoder and decoder components to function effectively for any task. d) The multi-head attention mechanism in transformers allows the model to focus on different aspects of the input simultaneously. 17. For a neural network with a hidden layer size of 128, if the ReLU activation is used, which of the following statements is correct? a) The output of the hidden layer will be bounded between 0 and 1. b) The gradient of the activation function can never be zero. c) Some units may output zero if the input is negative. d) The ReLU activation is linear for all inputs. 18. In the context of stochastic gradient descent (SGD), which of the following factors most directly influences convergence speed? a) Number of epochs b) Learning rate c) Batch size d) Number of features 19. Which of the following is the purpose of multi-head attention in transformers? a) To reduce the computational complexity of attention mechanisms. b) To ensure that attention focuses on a single position in the input. c) To allow the model to focus on different parts of the input simultaneously. d) To generate more robust positional encodings. 20. Consider the function f(x,y) = x2 −y2 . Which of the following statements about the function’s critical points is correct? a) The point (0, 0) is a saddle point b) The point (0, 0) is a global minimum. c) The point (0, 0) is a local minimum. d) The function has no critical points. 21. Which of the following is not a true statement about gradient descent (GD) vs. stochastic gradient descent (SGD)? a) Both provide unbiased estimates of the true gradient at each step. b) The memory and compute requirements of a single update step for both methods scales linearly with the number of features. c) The memory and compute requirements of a single update step for both methods scales linearly with the number of data points. d) GD is likely to converge in fewer updates/iterations than SGD, with a properly selected learning rate. 22. Write down a closed form solution for the optimal parameters θ that minimize the loss function in terms of the N × d matrix X whose i-th row is xi(T) and the N × 1 vector y whose i-th entry is yi. You may assume that any relevant matrix is invertible. a) w* = 2(XTX)−1X Ty b) w* = (XTX)−1X Ty c) w* = (XTX)−1Xy d) w* = (XXT ) −1X Ty 23. Which statement is true? (a) Logistic regression is not a probabilistic model. (b) Linear regression is best used for classification. (c) Logistic regression works well for non-linearly separable data. (d) We can use SGD to learn both logistic regression and linear regression models. 24. Consider a convolution layer with an input tensor of dimension 8 × 11 × 3 and an output tensor of dimension 3 × 4 × 3 tensor. What is the correct hyperparameter configuration of this layer? (a) Six 4 × 4 × 4 filters, the stride is three, 1 zero-padding (b) Six 2 × 2 × 3 filters, the stride is three, 2 zero-padding (c) Three 4 × 4 × 3 filters, the stride is three, 1 zero-padding (d) Three 3 × 4 × 3 filters, the stride is three, 1 zero-padding 25. Which statement is false? (a) CNN can be used for multiclass classification. (b) Feedforward neural networks can be used to model non-linear datasets. (c) Logistic regression is a linear model. (d) Transformer is a linear model. 26. Which one is an incorrect characterization of overfitting? (a) Increasing data size reduces overfitting. (b) Projecting the model to a more complex feature space will avoid overfitting. (c) Overfitting can be observed from high train accuracy and low test accuracy. (d) Regularization can help reduce overfitting. 27. Which one is not an activation function? (a) ReLU (b) GeLU (c) Indicator function (d) Sigmoid 28. Which one is an incorrect description of a transformer? (a) It is often used with position embeddings. (b) A transformer block consists of a linear self-attention layer and a feedforward network. (c) It is end-to-end differentiable. (d) It has a recurrent layer. 29. When training on an imbalanced dataset where the dataset contains more data with the first label than the three other classes (four class in total), which one of the following statements is true? (a) The model will overfit to the first class. (b) The model will overfit to the second class. (c) The model will overfit to the third class. (d) The model will overfit to the fourth class. 30. Let F = {f(x) = sign(wTx + b)|w ∈ R2 , x ∈ R2 , b ∈ R} be the set of binary classifiers in 2 dimensional space. Given there are only two possible options for the data label of each sample (y ∈ {−1, 1}), if you have a free choice in selecting the training data, what is the biggest possible number of training samples, that the 2 dimensional binary classifier can correctly classify no matter how the training data is labeled? a) 3 b) 4 c) 5 d) ∞ 31. Which of the following loss functions might still incur a loss penalty (non-zero loss) even though sign(wTx + b) and the ground truth label have the same value? a) Perceptron loss b) Hinge loss c) Logistic loss d) Both b and c 32. Which of the following statement is true about Perceptron? a) Perceptron always converges in a finite number of steps for any dataset. b) The update rule is affected by all samples. c) The choice for the learning rate significantly affects the prediction of weights. d) The update rule can be performed on more than one sample at a time. 33. What is the derivative of the function f(x) = lnσ(wTx+b) with respect to the parameter b ? (σ(x) = 1+e−x/1) 34. Figure 1, shows the change in the loss function of a model, during the training process. What is the primary reason for the way the loss function changes? Figure 1: The change in the loss function of a model, during the training process. a) Bad initialization b) Low learning rate c) High learning rate d) High batch size 35. During the training of a model we notice a growing gap between the training and validation perfor- mances. What would be the best approach to solve this problem? a) Increase regularization strength and decrease training data b) Increase regularization strength and increase training data c) Decrease regularization strength and decrease training data d) Decrease regularization strength and increase training data 36. One layer of the convolution neural network described below is wrong. If the input data is 224 × 224 colored images and the size of the flattened output is 8700, which layer has the wrong specifications? (For each Conv layer the provided specifications are mask size, output depth, stride size, and padding size. Same is true for Max Pool layers except there is no output depth.) a) Conv 4x4, 128 (s:2, p:1) b) Max Pool 2x2 (s:2, p:0) c) Conv 5x5, 256 (s:2, p:1) d) Conv 7x7, 324 (s:1, p:3) Convolution Neural Network Layers : Conv 3x3 , 96 ( s :1 , p :1) Conv 4x4 , 128 ( s :2 , p :1) Conv 5x5 , 168 ( s :1 , p :2) Max Pool 2x2 ( s :2 , p :0) Conv 3x3 , 212 ( s :1 , p :1) Conv 5x5 , 256 ( s :2 , p :1) Max Pool 2x2 ( s :2 , p :0) Conv 7x7 , 324 ( s :1 , p :3) Conv 2x2 , 348 ( s :1 , p :0) Max Pool 5x5 ( s :2 , p :0) 37. Which part of the attention layer best represents the distribution of the relationship between tokens? 38. Which activation function is defined as f(x) = ex + e−x/ex − e−x and outputs values in the range (a) Sigmoid function (b) ReLU function (c) Hyperbolic tangent (tanh) function (d) Softmax function 39. In a convolutional neural network (CNN), which of the following statements about the convolution operation is true? (a) The convolution operation reduces the spatial dimensions of the input. (b) The convolution operation uses the same weights (filters) across different spatial positions. (c) The convolution operation increases the number of channels in the input. (d) The convolution operation always uses a stride of 1 and no padding. 40. Which of the following statements about the attention mechanism in transformers is false? (a) Attention allows the model to focus on specific parts of the input sequence when generating each part of the output sequence. (b) The attention scores are computed using dot products between query and key vectors. (c) The value vectors are used to compute the attention scores. (d) The scaled dot-product attention includes a scaling factor to prevent softmax saturation. 41. In the context of multi-class classification, which loss function is commonly used when training a neural network? (a) Mean squared error (MSE) (b) Hinge loss (c) Cross-entropy loss (d) Absolute error loss 42. Consider a linear model with 100 input features, out of which 10 are highly informative about the label and 90 are non-informative about the label. Assume that all features have values between −1 and 1. Which of the following statements are true? (a) ℓ 1 regularization will encourage most of the non-informative weights to be exactly 0 .0. (b) ℓ 1 regularization will encourage most of the non-informative weights to be nearly (but not exactly) 0.0. (c) ℓ2 regularization will encourage most of the non-informative weights to be exactly 0 .0. (d) ℓ2 regularization will encourage most of the non-informative weights to be nearly (but not exactly) 0.0. 43. Which of the following options will decrease the generalization gap (difference between test error and training error) of a machine learning model? (a) Use more data to learn the model. (b) Add l2 regularization on the parameters when learning the model. (c) Consider a more complex model class, which is a superset of the original function class. (d) Simplify the model by reducing its complexity. 44. Which of the following statements about supervised learning are true? (a) The test set should not be used to train the model, but can be used to tune hyperparameters. (b) The generalization gap (difference between test and training errors) generally decreases as the size of the training set increases. (c) We cannot estimate the risk of a predictor (its average error on the data distribution) solely with the data used to train it. (d) If training and test data are drawn from different distributions, then low error on the training set may not guarantee low error on the test set even if the size of the training set is sufficiently large. 45. In the context of gradient descent optimization, what is the primary advantage of using mini-batch gradient descent over batch gradient descent? (a) It always converges to the global minimum. (b) It reduces the variance of the parameter updates, leading to more stable convergence. (c) It computes the gradient using the entire training dataset, making it more accurate. (d) It requires less memory and allows for faster computation per update.
Macroeconomics Coursework Assignment Consider a discrete-time infinite-horizon real business cycle (RBC) model aug- mented for investment shocks. The economy features two agents: i) a rep- resentative household who consumes, supplies physical capital to the firm (i.e., invests), supplies labor to the firm, and receives the firm's profits (if positive); ii) a representative firm that rents capital and demands labor from the household and pays the householdís wage and capital incomes. The representative household derives utility from consumption (Ct ) and ex- periences disutility associated with labor (Nt ) according to the following sepa- rable lifetime utility function: where E0 represents the expectations operator, 0 0. Finally, the firm produces output according to a standard constant-returns- to-scale (CRS) production function: Yt = At Kt(α)Nt1 -α: (5) where At is a total factor productivity (TFP) shock that follows an AR (1) process and satisfies A = 1 in steady state. Please Answer the Following Questions: 1. Taking the investment tax rate, the wage rate, and the rental rate of capital as given, formulate the householdís dynamic optimization problem and calculate the first-order conditions with respect to Ct ; Kt+1 ; and Nt : Define λt as the Lagrange multiplier on the householdísbudget constraint (2) in the optimization problem and substitute (3) in (2) for It. What is the marginal utility of consumption and how is it related to the interest rate rt and the investment shock μt? Explain the Euler equation with respect to the physical capital stock. 2. The firm maximizes its profit function Πt = At Kt(α)Nt1 -α — WtNt — rt Kt : Explain the profit function and calculate the firm's first-order conditions with respect to Nt and Kt taking input prices Wt and rt as given. Explain the intuition behind the optimality conditions and why the firm earns zero profits in equilibrium. In particular, show that WtNt + rt Kt = Yt : 3. Combining (2), (3), (5), and the firm's zero profit condition derived in question (2), derive and explain the economyís market clearing condition. 4. Write down and explain the general equilibrium conditions of an economy with TFP and investment shocks. . 5. Derive the steady state equilibrium conditions of the model. In the steady state you may assume that there are no investment shocks, i.e., μt = μ = 1. Explain your answer. 6. Assume that the parameters of the investment shock are set to Pμ = 0:95 and sdμ = 0:01. Simulate the model following a 1% drop in μt : On the same graph, compare the model-implied dynamics of this investment shock with those resulting from a 1% fall in At : Set the TFP persistence parameter and standard deviation to PA = 0:95 and sdA = 0:01, re- spectively. All other structural parameters should remain consistent with those used in the baseline RBC simulations from the lecture slides and the RBC_Basic.mod file. Discuss the general equilibrium e§ects of each shock, and explain the transmission channels involved. For the purpose of displaying your results, please simulate the log-linear annualized dynam-ics of Output, Consumption, Investment, Capital, Labour, MPK, Interest Rate, Wage Rate, and the Investment / TFP Shock (a 3x3 display in the display matlab file) . Read Greenwood et al. (1988); Justiniano et al. (2010, 2011); Brinca et al. (2016) to build further intuition. 7. Compare the modelís standard deviations of (log) output, (log) invest- ment, and (log) consumption following a TFP shock and a combination of TFP and investment shocks to their U.S. data counterparts from 1970:Q1 to 2020:Q1, obtained from Federal Reserve Economic Data (FRED) pro- vided by the St. Louis Fed. Ensure that the empirical data series are converted to quarterly percentage changes to align with the logarithmic model variables. Create a simple table showing the standard deviations for the data, TFP shock, and TFP + investment shocks. Which model better explains the variability of the key variables in the data?
Final Project – The Video Game GOAT Due December 10th, 2024 This project ties together everything we have discussed throughout the course into one project where you will make an argument for what is the greatest game of all time. Tell me about your GOAT’d game: • What game is it and when did it release? How was it received critically? • What makes it the GOAT in your opinion? • How was the game advertised and what do you find interesting about it? Was the advertising effective? • Talk about the game’s aesthetics; how are rules enforced in the game, how would describe the geography and representation, how many players is the game designed for, and is there anything else you would like to add? • What communities have emerged around this game? Do they have any reputation as a community? Is there a competitive scene to this game (i.e. speed runs, tournaments, etc.)? • Does this game have a narrative? If so, describe the narrative, if not how is the gamespace setup to promote gameplay without a narrative? Write 5-6 pages, 12-point Times New Roman, double-spaced, and if needed a bibliography in APA format for your sources. Please note that titles, headers, extra spaces between paragraphs, etc., do not count towards the page total. You can and should draw from your previous assignments where you can, but do not copy them verbatim. We will discuss this further in class, but feel free to contact me via e-mail for any questions. OPTIONAL EXTRA CREDIT OPPORTUNITY: if you would like to earn as many as 3 points onto your final course grade (not the project grade) you can make a 4–5-minute video accompanying your paper. You do not need to be on camera in it, but you will need some sort of presentation (PowerPoint, Google Slides, Prezi, etc.) or captured game play of you playing the game along with you either narrating the game play or presenting the slides. I can briefly go over how to capture game play with you if do not know how, but feel free to follow up if you still have questions. Points will be awarded at my discretion and commensurate with the amount of effort put into the presentation.
5MMCS007W – 3D Interactive Media Development Assignment Specification CW2 (2024/25) Unit Coursework 2 – 3D Interactive Media Product Weighting: 50% Qualifying mark 30% Description The coursework rationale is to introduce the students to the practical nature of 3D interactive media development. It realizes the above by setting two pieces of coursework that assess student knowledge and understanding in different aspects of composing a 3D interactive media product: Learning Outcomes Covered in this Assignment: This assignment contributes towards the following Learning Outcomes (LOs): - LO3 - Demonstrate an applied understanding of 3D animation concept, properties, controllers and use of scripting techniques to create in 3D animation; - LO4 - Demonstrate an applied understanding of scripting techniques to incorporate interactive behaviours on 3D concept; - LO5 - Work individually, as well as an effective team member to develop a common 3D interactive media product; - LO6 - Communicate design concepts by oral and visual means and provide documentation for a 3D interactive media product in written form. Handed Out: October 2024 Due Date A. Work in Progress Video - 12th December 2024 B. Peer Review Video - 19th December 2024 C. All CW2 files including the above video files - 9th January 2025 Expected deliverables A. Submit 1 YouTube video Link per Group on Spreadsheet link in Blackboard B. Submit 1 YouTube video Link per Group on Spreadsheet link in Blackboard C. Submit 1 per Group on Blackboard a link to the Group OneDrive with: C.1 1 submission per group of Unity Project file consisting of Interactive Media Product as Unity project and a Video showing full interactivity. C.2: Named folders with all Individual reports and individual work from each student in group. C.3: Named folder with Group work electronic files, and any planning evidence. WIP, PR, and Final Video files. DO NOT DO NESTED ZIPS! Method of Submission: Group Electronic submission on BB via a provided link close to the submission time. Type of Feedback and Due Date: Written feedback on the CW will be provided within 3 weeks after the submission (the mark and comments on marking scheme form). All marks will remain provisional until formally agreed by an Assessment Board. BCS CRITERIA MEETING IN THIS ASSIGNMENT 2.1.1 Knowledge and understanding of facts, concepts, principles & theories 2.1.2 Use of such knowledge in modelling and design 2.1.3 Problem solving strategies 2.1.4 Analyse if/how a system meets current and future requirements 2.17 Knowledge and understanding of commercial and economic issues 2.1.8 Knowledge of management techniques to achieve objectives 2.2.1 Specify, design or construct computer-based systems 2.2.4 Deploy tools effectively 2.3.1 Work as a member of a development team 2.3.2 Development of general transferable skills 3.1.3 Knowledge of systems architecture 3.2.2 Defining Problems, managing design process and evaluating outcomes 4.1.1 Knowledge and understanding of scientific and engineering principles 4.1.3 Knowledge and understanding of computational modelling Assessment regulations Refer to section 4 of the “How you study” guide for undergraduate students for a clarification of how you are assessed, penalties and late submissions, what constitutes plagiarism etc. https://www.westminster.ac.uk/sites/default/public-files/general-documents/Handbook_of_Academic_Regulations_2023_V2.pdf Penalty for Late Submission If you submit your coursework late but within 24 hours or one working day of the specified deadline, 10 marks will be deducted from the final mark, as a penalty for late submission, except for work which obtains a mark in the range 40 – 49%, in which case the mark will be capped at the pass mark (40%). If you submit your coursework more than 24 hours or more than one working day after the specified deadline you will be given a mark of zero for the work in question unless a claim of Mitigating Circumstances has been submitted and accepted as valid. It is recognised that on occasion, illness or a personal crisis can mean that you fail to submit a piece of work on time. In such cases you must inform. the Campus Office in writing on a mitigating circumstances form, giving the reason for your late or non-submission. You must provide relevant documentary evidence with the form. This information will be reported to the relevant Assessment Board that will decide whether the mark of zero shall stand. For more detailed information regarding University Assessment Regulations, please refer to the following website: https://www.westminster.ac.uk/current-students/guides-and-policies/academic-matters/academic-regulations Coursework Description The coursework rationale is to introduce the students to the practical nature of 3D interactive media development. It realizes the above by setting two pieces of coursework that assess student knowledge and understanding in different aspects of composing a 3D interactive media product: Coursework 2 Description Assesses the student practical ability to use appropriate scripting language to create required animation and interactivity to a proposed 3D interactive media product. In addition, it assesses the student’s skills documenting their implementation in a report using a professional style. and their entrepreneurial skills by the creation of a working video demo of their project. Padlet: https://uowdigital.padlet.org/fergusj/3DIMD_CWSamples Schedule: Schedule 3DIMD 2024.docx IMP: Interactive Media Product Groups: Each group will be represented by different students in weeks 10, 11 and 12 to present progress on CW2, in second half of lecture or tutorial as time permits. Peer review will occur between lectures and is based on padlet review of video blogs. Artificial Intelligence Content: You are not expected to use AI to assist you in your project. If you chose to use it, you should adhere to the following university guidelines: https://www.westminster.ac.uk/current-students/studies/study-skills-and-training/guidance-for-students-on-using-generative-ai You should demonstrate understanding of coding, and only use AI to better this understanding, if used at all. Currently all major companies will not allow general AI use, due to copyright issues, so this is in line with industry. - For code assist or asset generation by AI, you are to fully reference the work in your individual report and also in comments in the project code. If you choose to use AI to assist with code, make sure it is for understanding code better, or to support debugging enhanced work, Not for the tutorial work that is handed in as part of the CW submission, and not for writing sections of code. - Image or video AI can be referenced as normal, and can be used throughout the project, but does not receive as much marks as original work, unless it is used well or in an innovative way. - AI for writing reports is not allowed, and any use to assist research should follow the guidance above. - At all times, please do consult the Module Leader for acceptable AI use. - Misuse of AI can result in 0 marks or an Assessment Offense. Coursework Elements 1. Group Interactive Media Product Unity Project File and Video (1 per group) a. Group Unity Project file with all Visual Studio code and assets included b. Screengrab video (Final Video) of project in action with voiceover (Video in submission, youtube link in submission and on BB Padlet) Assets – at a minimum this should be: · Individual Modelling and Animation Contributions: • 1 Lego Mini Figure model with animation per student created from tutorials (3DS Max File and Unity Implementation in scene – triggered animations) • A variety of Lego Brick objects from Mecabricks or similar • Note: This is the bare minimum of items and should be exceeded for full marks. • More complex Mecabricks builds or modelling from 3D program with tutorials and researched techniques is an extra. · Group work and design using assets above, other work modelled in class, and work outside of class or sourced online • Part of total should be original from class - Lego Figures • Up to 90% of total assets can be otherwise outside sourced: LeoCad, Mecabricks assets, or similar • Additional original modelled assets is an extra Design, Lighting, Materials · 3D and 2D design aesthetics · Lighting, Shaders, and Texturing · Note: Investigate Unity Options or use those in Tutorial 12 Concept - Project should be at its most basic a 3D Diorama with interaction and animation - some extras to consider are: · Exploding 3D Diagram (w/slider options and interaction and animation) · Gameplay additions and Minigames · Narrative Elements · Advanced camera work · Note: There should be aesthetic as well as technical and procedural design in evidence – for examples please see the padlet, lecture material, and book Scripting, Navigation, Interaction, Animation: Note: For more see Module Tutorials or “3D User Interfaces: Theory and Practices” · Navigation Framework (with mouse and perhaps keyboard) Some Examples are: · 3rd person controller with collision, mouse for camera zoom and object selection · Objects or Waypoints for navigation, with mouse orbit of Waypoint · Interactivity (Selection and Manipulation, Triggers with mouse and keyboard) Some Examples are: · 3D Interaction – Unity UI 2D Buttons/Menus in 3D World Space · 3D Interaction - Mouse interaction directly with object via 3D Gimbal · 2D Interaction – Unity UI 2D Heads Up Display · State changes/Triggers with keyboard · Collisions · Note: For more see the Tutorials, Lectures, or “3D User Interfaces: Theory and Practice” · Animation, Movement and Narrative Some Examples are: o Figure and non-figure Animation, Triggered and Narrative o Transforms and Scripted Movement or Interaction o Note: Investigate Unity Options or use those in Tutorials 2. Individual Report and roles (Individual Analysis Reports, 1 per student) Individual Pdf detailing individual contributions to group and reflections on final IMP Note: This helps me to mark you a. Pdf will be approximately 1500 words and include rendered images of individual contributions to above group file, screenshots of the product, and diagrams expanded from CW1 b. All individual reports will be included in the group submission c. Includes analysis of group contributions and overall group work (in relation to fulfilment of CW1 planning and report) d. Includes analysis of Individual roles and contributions (pick 2 roles from below) 1. Assets: Asset sourcing and conversion 2. Assets: Asset creation 3. Management: Versioning and file management 4. Management: Presentations and milestone updates 5. Management: Meetings, Admin, Schedule 6. Design: Aesthetic design and design bible 7. Design: Interaction design and diagrams 8. Design: Concept and requirements 9. Coding: Interaction 10. Coding: Movement and camera 11. Coding: Animation and trigger 12. Animation: Animation and Implementation 13. Animation Coding: Scripted movement 3. 3DS Max Lego Minifigure and animation (Student individual work) a. 3DS Max file of a modelled Lego Minifigure as per tutorials 8-10 b. 3DS Max Animation as per tutorial week 10 Note: 3DS Max required for this, additional models may be done in any program. 4. Group Collaboration Any appropriate evidence of group collaborative work including contributions to Group work files seen in submission, contribution on videos and in class, and evidence of group meetings and planning (meeting minutes, online discussions). This is a good amount of points that should be easy to get, please do show your contribution in your report. 5. Videos (1 x Work in progress, 1 x Peer review, 1 x Final Video per group) a. Group work-in-progress video (WIP - 1 per Group, approx 5 minutes) b. Group peer review video (PR) of another Groups’s WIP video (PR - 1 per Group, approx 5 minutes - as assigned to your group) c. Final CW1 Video (1 per Group, Approx 10 minutes – including): CW2 Presentation - Screen capture of an interactive demonstration of the files in (1.) above (Unity3D) and a voiceover or presentation to camera. 6. Excelling Individual Expectations in any category of requirements Individual Investigation, experimentation and work beyond tutorial material. This is to capture and reflect exceptional marks above 90/100, if they are achieved. Only truly exceptional work will achieve marks from here. Coursework Marking scheme The Coursework will be marked based on the following marking criteria: Criteria Mark per component Mark provided Comments 1.Group Unity Project File – Overall Group collaboration and effort reflected in final project 25 Group collaboration on project and the submission content. - Assets - Design, Lighting and Materials - Navigation Framework - Interactivity, Selection, Manipulation - Animation, Movement, Narrative 5 5 5 5 5 1/2. Group Unity Project – Individual Role Contributions 30 Individual Contributions - At least 2 group project contribution roles as defined in CW brief above and documented in below report - Role Contribution 1 – define in report - Role Contribution 2 – define in report 15 15 2.Individual Report – with details of Individual Role contributions 10 Self - Reflection, individual contributions and group collaboration information 3. 3DS Max Lego Minifigure and animation 15 - 3DS Max Modelling - 3DS Max Animation - Unity Import and functionality (at least to CW1 level) 5 5 5 Individual Modelling and Animation in 3DS Max for Lego Minifigure (required, additional assets work can be done using any program) 4.Individual Collaboration with Group 5 Individual collaboration with group development 5. Videos (WIP, PR, Final) 5 Reflecting presentation and group and individual contributions 6. Excelling Individual expectations in any category of requirements. Individual Investigation, experimentation and work beyond tutorial material. This is to capture and reflect exceptional marks above 90/100, if they are achieved. Only exceptional work will achieve marks from here. 10 Total 100
MGDI61961 - Financing Projects2024 - 2025 ASSIGNMENT 1 [Worth 50% of overall course unit mark] Word Limit: 1000 Task The University of Manchester (UoM) seeks to find solutions to its accommodation shortage by requesting private sector organisations to propose how single room accommodation for 100 postgraduate students could be procured using project finance. The UoM will provide the site in north Manchester, 7 miles from the university campus on Oxford Road. The university and the private sector will determine an equitable concession period. Your report outlining your proposal should include estimates of costs and revenues, the financial instruments to be considered, the major risks and a cumulative cash flow of the base case and worst case scenarios. Your report should be a maximum of 1000 words (not including cash flow and diagrams / tables and no more than 10 pages long) and be uploaded on to Blackboard at 2 pm on 13th December 2024 Marking Scheme will be as follows: Presentation/Structure (25) Content (25) Analysis (40) Referencing (10)
Econ 323 Comprehensive Problem Set (CPS) [Optional] This problem set will be made up of three blocks. Block 1 is published, and 3 will be filled in later in the semester. This block corresponds to material between MT1 and MT2. The CPS is optional. If a student elects to do it, it may replace one of the three midterm exams. That is, the CPS can replace a low midterm score, or if a student has not taken one of the exams for some reason, it can fill in for the missing test. As described in the syllabus and in class, the CPS is intended to be incrementally more challenging than the “standard” practice problems or exam problems we do routinely. You can think of it as a “stretch” assignment. All questions will have a small novelty or two that the student needs to work out on their own. The problems should then seem harder than our “standard” work. Answer 2 of the following 3 problems. 1) Congestion Pricing This problem models market inefficiencies related to a congestible public good: a road. We did this model one day in class. You should refer to that work for guidance. I’ve made one adjustment in the effect of traffic on speed. The ingredients of the problem are: N- the number of cars on the road. n=2000 the threshold number of cars. Below this threshold, cars do not lead to traffic slowdowns. N above n will lead to a slowdown in traffic speeed, as described in the formula below. a=55 the speed of traffic, in MPH, withen N
CS211: Fall 2024 Defusing a Binary Bomb Due: December 15th, 2024 CS211 Sections 5 - 8 1 Introduction The nefarious Dr. Evil has planted a slew of “binary bombs” on our class machines. A binary bomb is a program that consists of a sequence of phases. Each phase expects you to type a particular string on stdin. If you type the correct string, then the phase is defused and the bomb proceeds to the next phase. Otherwise, the bomb explodes by printing "BOOM!!!" and then terminating. The bomb is defused when every phase has been defused. There are too many bombs for us to deal with, so we are giving each student a bomb to defuse. Your mission, which you have no choice but to accept, is to defuse your bomb before the due date. Good luck, and welcome to the bomb squad! Step 1: Get Your Bomb Due to firewall restrictions, to obtain your bomb, you must first loginto aniLab machine using weblogin. Next, open a web browser on any iLab machine you choose from the WebLogin list. Navigate to: http://h418-1 .cs .rutgers .edu:17220 This will display a binary bomb request form for you to fill in. Enter your netid and email address and hit the Submit button. The server will build your bomb and return it to your browser in a tar file called bombk .tar, where k is the unique number of your bomb. Save the bombk .tar file to a (protected) directory in which you plan to do your work. Then give the command: tar -xvf bombk .tar. This will create a directory called ./bombk with the following files: • README: Identifies the bomb and its owners. • bomb: The executable binary bomb. • bomb .c: Source file with the bomb’s main routine and a friendly greeting from Dr. Evil. • writeup.{pdf,ps}: The lab writeup. If for some reason you request multiple bombs, this is not a problem. Choose one bomb to work on and delete the rest. Step 2: Defuse Your Bomb Your job for this lab is to defuse your bomb. You must do the assignment on one of the class machines. In fact, there is a rumor that Dr. Evil really is evil, and the bomb will always blow up if run elsewhere. There are several other tamper-proofing devices built into the bomb as well, or so we hear. You can use many tools to help you defuse your bomb. Please look at the hints section for some tips and ideas. The best way is to use your favorite debugger to step through the disassembled binary. Each time your bomb explodes it notifies the bomblab server, and you lose 1 point in the final score for the lab. So there are consequences to exploding the bomb. You must be careful! Each Phase will be 7 points. the maximum you maybe able to get is 102. Although phases get progressively harder to defuse, the expertise you gain as you move from phase to phase should offset this difficulty. However, the last phase will challenge even the best students, so please don’t wait until the last minute to start. The bomb ignores blank input lines. If you run your bomb with a command line argument, for example, linux> ./bomb psol .txt then it will read the input lines from psol .txt until it reaches EOF (end of file), and then switch over to stdin. In a moment of weakness, Dr. Evil added this feature so you don’t have to keep retyping the solutions to phases you have already defused. To avoid accidentally detonating the bomb, you will need to learn how to single-step through the assembly code and how to set breakpoints. You will also need to learn how to inspect both the registers and the memory states. One of the nice side-effects of doing the lab is that you will get very good at using a debugger. This is a crucial skill that will pay big dividends the rest of your career. Logistics This is an individual project. All handins are electronic. Clarifications and corrections will be posted on the course message board. Scoreboard You can keep track of how you are doing by looking at the class scoreboard at: http://h418-1 .cs .rutgers .edu:17220/scoreboard This webpage is updated continuously to show the progress for each bomb. Hints (Please read this!) There are many ways of defusing your bomb. You can examine it in great detail without ever running the program, and figure out exactly what it does. This is a useful technique, but it not always easy to do. You can also run it under a debugger, watch what it does step by step, and use this information to defuse it. This is probably the fastest way of defusing it. We do make one request, please do not use brute force! You could write a program that will try every possible key to find the right one. But this is no good for several reasons: • You lose 1 point every time you guess incorrectly and the bomb explodes. • Everytime you guess wrong, a message is sent to the bomblab server. You could very quickly saturate the network with these messages, and cause the system administrators to revoke your computer access. • We haven’t told you how long the strings are, nor have we told you what characters are in them. Even if you made the (incorrect) assumptions that they all are less than 80 characters long and only contain letters, then you will have 2680 guesses for each phase. This will take a very long time to run, and you will not get the answer before the assignment is due. There are many tools which are designed to help you figure out both how programs work, and what is wrong when they don’t work. Here is a list of some of the tools you may find useful in analyzing your bomb, and hints on how to use them. • gdb The GNU debugger, this is a command line debugger tool available on virtually every platform. You can trace through a program line by line, examine memory and registers, look at both the source code and assembly code (we are not giving you the source code for most of your bomb), set breakpoints, set memory watch points, and write scripts. The CS:APP web site http://csapp.cs.cmu.edu/public/students.html has a very handy single-page gdb summary that you can print out and use as a reference. Here are some other tips for using gdb. – To keep the bomb from blowing up every time you type in a wrong input, you’ll want to learn how to set breakpoints. – For online documentation, type “help” at the gdb command prompt, or type “man gdb”, or “info gdb” at a Unix prompt. Some people also like to run gdb under gdb-mode in emacs. • objdump -t This will print out the bomb’s symbol table. The symbol table includes the names of all functions and global variables in the bomb, the names of all the functions the bomb calls, and their addresses. You may learn something by looking at the function names! • objdump -d Use this to disassemble all of the code in the bomb. You can also just look at individual functions. Reading the assembler code can tell you how the bomb works. Although objdump -d gives you a lot of information, it doesn’t tell you the whole story. Calls to system-level functions are displayed in a cryptic form. For example, a call to sscanf might appear as: 8048c36: e8 99 fc ff ff call 80488d4 To determine that the call was to sscanf, you would need to disassemble within gdb. • strings This utility will display the printable strings in your bomb. Looking for a particular tool? How about documentation? Don’t forget, the commands apropos, man, and info are your friends. In particular, man ascii might come in useful. info gas will give you more than you ever wanted to know about the GNU Assembler. Also, the web may also be a treasure trove of information. If you get stumped, feel free to ask your instructor for help. submission You must compile the phase inputs into a single new-line delimited file called mysolution.txt. You can validate that this file is accurate by running the command stated in Step 2. You have to submit the assignment using Canvas. To create the tar file that you will submit after finishing your programming assignment, you will use the following command line, in the parent directory of bomb[ID]: $ tar -cvf bomb[id] .tar bomb[id] your tar should contain the folder, bomb[id], which contains the same files that were downloaded plus your solutions file. To verify, you can run the following command. If you get a similar output, you’re fine: ajf277@assembly:˜/Documents/CS211/testbomb$ tar -tvf bomb1 .tar drwxr-x--- ajf277/ajf277 0 2024-11-22 01:43 bomb1/ -rw-r----- ajf277/ajf277 71 2024-11-22 01:34 bomb1/README -rw-r----- ajf277/ajf277 4069 2024-11-22 01:34 bomb1/bomb .c -rwxr-x--- ajf277/ajf277 38600 2024-11-22 01:34 bomb1/bomb -rw-r----- ajf277/ajf277 64 2024-11-22 01:41 bomb1/solution .txt ajf277@assembly:˜/Documents/CS211/testbomb$ You should be able to pass in the solution.txt file into your bomb program, and pass all phases. Frequently Asked Questions Q. When I try running the bomb, it prints ”Initialization error: Running on an illegal host [2].” Has Dr. Evil defeated me? A. No, this simply means that you’re not running the bomb on a valid machine. make sure you use one of the computers in the CAVE, command center or meltdown lab. Q. Is there any extra credit for solving the secret phase? A. What secret phase? ;-)
6SSMN961: APPLIED ECONOMETRICS MIDTERM COURSEWORK 2024/25 INSTRUCTIONS TO CANDIDATES: 1. The deadline for submission of the coursework is Thursday 7th November at 10:00 am GMT. Work should be submitted on KEATS. 2. The file that you upload on KEATS should contain two parts: • Short written answers to the questions • The STATA output in pdf format You can merge two pdf files using Acrobat Professional or an online pdf merger. 3. The word limit is 1,000 words, excluding STATA output and the cover sheet. 4. Your submission should be your own words and not be generated by AI software. 5. You must complete the coursework coversheet. This is very important to ensure that your work can be identified. In addition, you should name the file with your candidate number as follows: Candidatenumber.pdf. 6. To avoid collusion, each student is given a unique version of the datasets. This means that you should answer the questions with the datasets that have been provided to you. If your answers or the STATA output file are based on the datasets given to another student, you will lose marks and face an allegation of collusion. Because the datasets are different, you should not expect to replicate the results in the papers exactly. This question is based on a paper by Lee, Miguel and Wolfram (2020) which presents results from an experiment that randomised the expansion of the electricity grid in rural Kenya. In April 2014, the authors randomly divided communities into treatment and control groups. 380 unconnected households in 25 communities were in the treatment group and received an opportunity to connect to the electricity grid with a 100% subsidy, which meant they could connect for free. The control group consists of 1,150 unconnected households in 75 communities who received no subsidy. Between May and August 2014, each treatment household received a letter describing a limited-time opportunity to connect to the electricity grid at a subsidised price. The dataset electricity.dta contains information on household and community characteristics at baseline (between February and August 2014), a treatment indicator equal to 1 if the household was randomly assigned to be in the treatment group and 0 if the household was in the control group (variable treatment) and an indicator equal to 100 if the household was connected to the grid after 2014 and 0 if not (variable connected). a) Complete the table below comparing the means of a set of observable household characteristics of the control and the treatment groups and the p-value of the ttest of the difference between the means of the two groups. Do the two groups appear balanced? Explain why it is important to check for balance. (16 marks) Differences between electricity grid control vs. treated households at baseline Control Treatment p-value of difference Number of members High-quality walls (%) Age (years) Attended secondary schooling (%) Senior citizen (%) Chickens Not a farmer (%) Has bank account (%) Employed (%) b) Explain how you can use the information provided to test whether the subsidy increases the probability of connecting to the electricity grid. Write down the estimated equation and explain in detail. (12 marks) c) Estimate the model in part b) with and without controlling for household and community characteristics. Cluster the standard errors by community (variable siteno). Interpret the results. Explain why you would want to use clustered standard errors. (14 marks) d) Does the inclusion of controls in part c) have a substantial impact on the results? Explain in detail. (12 marks) e) You would like to test whether the effect of the subsidy on the probability of being connected to the grid is larger when the household head is more educated. The hypothesis is that more educated individuals understand better the benefits of having electricity. i. Explain how you can adapt the model in part b) to test this hypothesis. (16 marks) ii. Estimate this modified model, including controls for household and community characteristics. Explain the results in detail. (12 marks) You would like to test the effect of being connected to electricity on some energy outcomes and some noneconomic outcomes. The dataset outcomes.dta contains data on these outcomes and the treatment indicator. To test the effect on these outcomes, you use the model in part b), but instead of having the take-up indicator as the dependent variable, you look at energy outcomes and non-economic outcomes. f) Estimate the model separately for five energy outcomes: number of appliance types owned, owns mobile phone, owns radio, owns television, and owns iron. Interpret the results. (11 marks) g) Estimate the model separately for two noneconomic outcomes: life satisfaction and the political and social awareness index (which captures whether the household head was able to correctly identify the presidents of Tanzania, Uganda, and the United States). Interpret the results. (7 marks) References: Lee, Kenneth, Edward Miguel and Catherine Wolfram (2020). “ Experimental Evidence on the Economics of Rural Electrification”, Journal of Political Economy, Vol. 128, No. 4.
Sample Quiz 2 CSCI-567 – Machine Learning Fall 2024 1. Instruction tuning is often formulated as a supervised learning problem where the model is fine-tuned on input-output pairs. If yi is the expected output, xi is the input, and zi is the instruction, which of the following is the correct loss function? a) L(θ) = − log Pθ(xi|yi, zi) b) L(θ) = − log Pθ(yi|xi, zi) c) L(θ) = − log Pθ(zi|xi, yi) d) L(θ) = − log Pθ(zi|xi) 2. In Gaussian Mixture Models (GMMs), each component is a Gaussian distribution characterized by its mean and covariance. When fitting a GMM to data, what is the role of the covariance matrices, and how do they affect the shape of the clusters? a) The covariance matrices determine the orientation and shape of each cluster, allowing for elliptical clusters. b) The covariance matrices are always identity matrices, resulting in spherical clusters. c) The covariance matrices affect only the size but not the orientation of the clusters. d) The covariance matrices are used to normalize the data before clustering. 3. Which of the following best explains how boosting improves model performance? a) By training all weak learners independently and averaging their outputs. b) By using a single strong learner trained on the entire dataset. c) By sequentially training weak learners where each learner focuses on correcting the errors of the previous ones. d) By randomly selecting subsets of data and features to train each weak learner. 4. Decision tree: Which one of the following statements is false? a) Decision tree and AdaBoost both maintain the explainability of the model. b) Decision tree can be applied to both classification and regression tasks. c) Decision tree works with real and categorical data. d) Decision tree is a linear model. 5. The Naive Bayes classifier makes a strong assumption about the features in the dataset. Which of the following best describes this assumption? a) The features are linearly dependent on each other. b) The features are conditionally independent given the class label. c) The features follow a uniform. distribution regardless of the class label. d) The features have equal variance across all class labels.
CS101 Final Project Due @ 11:59PM Tues Dec 10 This is the latest possible due date I am allowed to assign. However, this is not the last possible moment I am allowed to accept assignments. The submission portal will remain open for a few days after the "due date." Overview The goal of this project is to design a simple dynamic web page and understand what it does and how it works. You will be provided with a specific prompt to use with ChatGPT that will guide you in producing a website following the MVC pattern we have studied in class. There is no requirement for the web page beyond it being dynamic. Specifically, it must include at least one triggerable event (in addition to window .onload) that changes the appearance of the web page. The expectation is that you should be able to earn 90 points with minimal effort. Additional points can be earned by making your web page more interesting. Details about earning additional points are discussed below. Deliverable You will need to submit the following: 1. Prompts: ○ A complete list of all your prompts to ChatGPT. ○ Do not include ChatGPT’s responses. 2. Final Code: ○ The final code created by ChatGPT for your webpage. 3. Documentation: ○ Overview: ■ A brief paragraph summarizing the purpose of your website. ■ Explain how users can interact with it. ○ Model / View / Controller Descriptions: ■ Provide a description of each object in no more than four sentences. ■ Address the primary state and functionality each object contributes to the website. 4. Class-Based Diagrams: ○ Provide a class-based diagram for each of the model, view, and controller objects. 5. Control-Flow Pseudocode: ○ For each event triggered on the webpage (e.g., click events, mouse events, onload events), provide pseudocode tracing the control flow between objects. ○ Include: ■ Which object methods are called and in what order. ■ How these methods interact with the webpage’s state. ■ What values are passed/returned. ■ What properties are updated. Collaboration Policy Everyone must submit their own assignment and documentation. However, you are welcome (and encouraged) to create the webpage in teams of two or three. Guidelines for Using ChatGPT ● You cannot cut and paste ChatGPT’s output verbatim and submit it as your own work. ● However, you are encouraged to use ChatGPT to explain as much as possible before writing your own answers. Grading Rubric You can earn up to 85 points simply by re-doing HW5 with your own prompt. Additional points can be earned as follows: 1. Design Decisions (5 points): ○ Earn points by making deliberate design decisions (e.g., specific styling or layout choices). 2. Complexity (10 points): ○ Earn points for adding complexity to your project, as described below. The minimum requirement is that there be at least one triggerable event (in addition to window .onload) that changes the appearance of the web page Generic Starter Prompt: Please create a simple webpage in JavaScript. using the MVC framework. Follow these guidelines: 1. Structure: ○ Place the HTML, CSS, Model, View, and Controller code in separate files. 2. Encapsulation: ○ Ensure all functionality is encapsulated in objects with appropriate getters and setters. ○ Don't use classes and constructors. Create all objects as constants. 3. Dynamic Interactions: ○ Do not use form. submissions or page reloads. ○ All interactions should happen dynamically using JavaScript. 4. Session Independence: ○ You do not need to store information between sessions. The collection can reset when the page reloads. 5. Functionality: ○ (Insert your project idea here.) Example project ideas You may use these or come up with your own ideas. ● Create a webpage that allows me to add songs from my music collection and filter them by artist, genre, etc. ● Create a webpage that allows users to enter text and displays anagrams of what they've typed ● Create a webpage that presents you with a (hard-coded) list of multiple-choice questions and keeps track of how many you get right ● Create a webpage that displays inspirational quotes (e.g. from the Zen Quotes API). Active design decisions Begin with a prompt along the lines of "now make it look good." This is not an active design decision and is not worth any points. Now start describing how you would like the page to look. Here are examples: ● Move the input area further down the page and center it / uncenter it. ● Create more space around the element. ● Make the background of the page FloralWhite / #FFFAF0 ○ A completelist of available colors can be found here: HTML Color Names (w3schools.com) Including APIs APIs are like asking another website to provide information that can be incorporated into your web page. For example, we saw an example of getting the weather for a zip code using the OpenWeather API. There are many APIs that do interesting things that are either free or give you a certain number of free requests per minute / day / month. ● Note that while you are highly unlikely to go over these limits, they may require you to provide a credit card. You do not have to do this. You can very easily earn all 100 points without providing any personal information. Here are some examples of free APIs. Here's a database. You can also google for more and / or specific ones or ask ChatGPT to list some. ● NASA has a large number of APIs ● A database of Pokémon info ● Horrible jokes ● AI text summarization. There are many, many, many of these ● Recipes and other food facts ● Stock market info (These also exist for cryptocurrency. Go ahead. I dare you ) Complexity The goal of the complexity component is to allow you to do more interesting things while you still have the opportunity to discuss it with us face-to-face in office hours and labs. You are encouraged to ask us how complex we think it is before you begin. You can also ask ChatGPT to rate it on a scale from 1 to 10. Note: in my experience ChatGPT under-reports the complexity of projects Here are questions you can ask. ● I don't knowhow to program, but I understand the MVC pattern and can more or less read javascript, especially with explanations. Here is an explanation rt prompts>. On a scale of 1 to 10, how complex would you say a web page that does ?
Assignment 1: ETX2250/ETF5922 Question 1 (5%) In tutorial 2, you worked in groups to review one beautiful visualisation and one ugly visuali- sation. For the assignment you will revisit this exercise. You task is to go back to the visualisations’ submitted on the discussion forum and polish your answer. This part of the assignment is an individual exercise, so you are able to change, update or extend what was submitted on the discussion forum as a group. Rate your chosen visualisation on a scale from 1 to 5 stars, where: • 1 star - This visualisation is absolutely dreadful, and you’d get no marks if you handed something like this in. • 2 stars - This visualisation is below average, its violates most of the graphical principles we’ve learnt. • 3 stars - This visualisation is average. Some aspects of this plot are good, but equally some are bad. • 4 stars - This visualisation is above average. It adheres to most of the graphical principles we’ve learnt. • 5 stars - This plot is so beautiful I should show Kate! Support your rating by discussing what we’ve learnt about what makes a good data visu- alisation. This discussion should include how well your chosen visualisation adheres to the principles of graphical excellence and a recognition of any relevant elements of human percep-tion. In selecting your visualisation, be sure that there is enough sophistication in the figure to display your knowledge on this topic. Question 1 Submission Submit both visualisations, your star ratings and your explanations in a pdf or html file. Use the template provided. Question 2 (5%) In tutorial 3, you worked in pairs to create one beautiful visualisation and one ugly visualisation. For the assignment you will revisit this exercise. Go back to the visualisations’ you submitted on the discussion forum, and complete the oppo- site role. (i.e. If you were the good visualiser, now be the bad visualiser and vice versa). This part of the assignment is an individual exercise, so you are expected to update or extend what was submitted on the discussion forum as a pair. Note: You are not restricted to use the same plots or plot types, and you are welcome to change your submission entirely if you want to. Question 2 Submission Submit a pdf or html that contains your updated visualisations’ alongside a dot point summary of what aspects of the visualisation you changed to make the visualisation bad, and what aspects of the visualisations you changed to make the visualisation good. Use the template provided. Question 3 (10%) Learning to create good visualisation takes a lot of practice. For this task you will create a range of different plots to explore the different aspects of what makes a good visualisation. Pick a dataset from Moodle and use it in this exploratory analysis task. You can also use other data sets if you get approval from your lecturer. This question is inspired by the following story of 1000 pictures, where producing quantity lead to better quality in the end. Your challenge is to create at least 20 different plots. Explore your data and experiment with different kinds of geometries, aesthetics, scales and themes. These plots must each demonstrate a different skill or a different aspect of visualisation. Making simple variations to a plot, for example only changing text or fixed colour, will not count as a new plot. Plots created using facets or ggpairs also only count as a single plot. Note do not waste time polishing these plots, that is not the goal here. The goal here to create lots of different visualisations. Polishing plots will come on the next assignment. Question 3 Submission For each of the plots, write 1 - 2 sentences about what you learnt about your data and or data visualisation. You might find some plots do not help you learn anything additional about your data, but were useful for you to see new geometries. That is okay, but write about why. Create at least 5 plots in PowerBI, and at least 10 plots in R. You can chose how you create the remaining 5 plots. Save your chosen 20 plots, and put them in a single Powerpoint or quarto presentation for marking. Put a single plot on each slide. Use the template provided. Also upload your R Project, your data/data sets, any code you created, and your .pbix files in a single zipped folder. Note • Your next assignment will build upon the exploratory analysis in question 3, and you will use the same data set. • For those who did not complete Tutorial 2 or Tutorial 3, you are expected to complete these tutorials as an individual (don’t worry about posting to the discussion forum), and then do the assignment question. • If you use generative AI on your assignment, you must also acknowledge and submit the prompts used to support your work. If you do not, and you are found to have used generative AI this will constitute a form of academic misconduct.
BPE Senior Seminar Fall 2024 Third assignment (100 points) INSTRUCTIONS: This assignment is due on December 8th by 11.59 p.m. Please use the following guidelines to complete the assignment and submit our work through NYU Brightspace. For this assignment, you are required to work in groups of three students. At the beginning of your write- up, clearly state the full names and net IDs of all three members of your group. It's essential that the members of your group remain the same throughout the semester. You have the option to use either Stata or R software for this assignment. Once you've made your choice, please stick with that software for the entirety of the semester. Your main write-up should be formatted using the Times New Roman font at a 12pt size with 1.5 spacing. If you're including any code in your assignment, place it at the end in a section titled "Appendix." This section should be formatted using the Courier New font, which is best suited for displaying code in documents. It's important to note that each member of the group must upload the assignment separately. When you're ready to submit, go to the Assignments tab in BrightSpace. Make sure your document is saved and uploaded as a PDF file. An important detail to remember is to name your file using your network ID. For example, if your net ID is "rcd306", the file you upload should be named "rcd306.pdf". Although your code will not be graded, you are required to submit it. One or two groups will be randomly selected to present their code. The files for completing this assignment are available on Brightspace and Dropbox 1. Plotting the Raw Data (20 points) Download the Excel file announcement_replication.xlsx. In a do-file or R-file write down code that can perform. the following tasks. Remember that you need to use the foreign package to open the excel file. We are going to replicate Figure 2A from “The Rise and Persistence of Illegal Crops: Evidence from a Naïve Policy Announcement” (Prem, Vargas, and Mejia, 2023) (page 348). • Use the group_by and summarize commands to get an average of the suitability for coca and non-coca areas for each year. Don't forget to call library(dplyr) (5 points). • Replicate the plot. You must distinguish between the two areas based on their suitability on the plot. Use a legend as in the paper (10 points). • What is this plot suggesting? (5 points) 2. Replicating Table 1 (30 points) Write down the code that replicates only columns 1, 2, and 3 of Table 1. The standard errors in this case will be the same as in the papers because the code automatically clusters at the municipality level, so don't worry about it. • Replication of the main table (30 points) (Hint: You need to include different fixed effects and controls depending on the specification. Your outcome is coca_area_grid and your independent variable is suit_announce. Read what is included in the specification of each column. For example, column 2 includes not only Municipality FE and Year FE, but also Department Year FE. Thus, you need to interact -using *- coddepto with time FE. The specification of column 3 also includes the following controls: indrural_announcement, discapital_announcement, IPM_announcement, lpobl_tot_announcement; which are interactions of these with a cease fire dummy that identifies the period after 2013. Just include these you don't need to interact anything with the controls). 3. Interpretation (50 points) • What is the control group? What is the treatment group? What is the pre-treatment period? What is the post-treatment period? (10 points) • What is b capturing in all of these three cases? (5 points) • Explain what does the suit_announce variable measures in this setup? (5 points) • Why do the authors mention that the controls (municipality characteristics) are measured before the announcement? (10 points) • Why do the authors include FE departments in column 2? If they include these FE, what are we comparing when we get our b? (10 points) • We know that the parallel trends assumption is crucial in a diff-in-diff setup. o What resources do the authors use in the paper to provide some suggestive evidence that low and high coca-suitable municipalities follow similar trends? Be specific (5 points). o What resources do the authors use in the paper to provide formal evidence that low and high coca-suitable communities follow similar trends? Be specific (5 points). 4. Extra (25 points) • Do a placebo regression in which you change the date of the announcement. Run the same regressions as you did in columns 1, 2, and 3. Show your results using a table (20 points). • Are these result consistent with what we should have expected? (5 points)
ELEC6255 IoT Networks Coursework 2024/25 Assignment Set: Wednesday 09th October 2024, during lecture Assignment Type: Individual coursework Submission Deadline(s): Fri 13th December 2024, by 4pm (written report) Feedback: We aim to return your mark and written feedback by Friday 17th January 2025, though will try to get this to you before the examination. Mark Contribution: This assignment is worth 40% of your mark for ELEC6255 Required Effort: You should expect to spend up to 60 hours on this coursework Examiners: Professor Geoff Merrett and Dr Alex Weddell Learning Outcomes Having successfully completed this coursework, you will be able to: • demonstrate understanding of the principles of layered networking models, architectures and protocols which enable IoT networking • use simulation to test and evaluate networking algorithms, protocols and architectures • find, read and evaluate technical literature, and interpret standardisation documents • communicate your technical work Task Summary A billionaire property owner has contacted you to provide an IoT smart lighting system for one of their mansions. They want you to investigate and provide some assurances about the reliability, latency, and additional energy consumption of such a system. Your task, in this coursework, is to use OMNeT++ to simulate, evaluate and analyse the performance of such a smart lighting network, based loosely around the ‘Hue’ smart lighting system. This coursework is solely assessed through your individual report, which must be submitted along with your source code. The report must document your investigation into this system, using network simulation as a tool. This is not a ‘lab’ exercise: we are not posing an exhaustive list of specific questions that we want you to answer, we want you to perform your own research and investigation of a system within the scope of the coursework brief, and this is reflected in the marking scheme. Full Task Description Part 1: Model your Network Your first task is to create an OMNeT++ network using the IEEE 802.15.4 PHY and DLL layers,and the AODV NET layer. You can find the floor plan for the property that you are to model in Appendix 1 (listed by name), at the end of this document. You do NOT need to model this building perfectly, and can estimate the sizes and locations of rooms from the floor plan. You should arrange the following nodes throughout the property: 1 ‘hub’, 25 ‘smart light’, and 10 ‘light switch’ nodes. You can place them where you like, ensuring that you spread them out all around the property. Each light switch controls all of the smart lights that are located in the same room as it. All packets sent from light switches get transmitted to the hub (possibly being routed via other light switches and/or smart lights). The hub uses a lookup table to identify the addresses of the smart lights that are located in the same room as the light switch, and then transmits packets to each of them (possibly being routed via other light switches and/or smart lights). Your simulation should approximately (but realistically) represent the system that you are modelling. You should make appropriate and justifiable modelling decisions, including: • Consider your choice of parameters in the physical layer (e.g. radio transmit power, sensitivity, data rates, frequency, bandwidth, etc). You could assume that the nodes will be based around the NXP JN516X radio module/system-on-chip. Consider your choice of an appropriate indoor wireless channel model, to model path loss and noise. Your chosen radio transmit power, channel model, hub location etc MUST ensure that multi-hop packet routing is required; it should not be possible for all nodes to be able to communicate directly (in asingle-hop) with the hub. You may want to put your hub at one side of the network (not the centre) to help with this. Make sure you set sensible dimensions for your simulated floorplan, and locate devices inappropriate locations. • Consider how to model packet generation, and model/approximate the flow of data through the various types of node in the network. You can model light switch presses however you wish, in order to explore system operation. For example, do you think it is realistic that every light switch might be pressed once per second? Are they all pressed at the sametime? • Consider how to model the energy properties (e.g. the capacity of the energy stores, their initial values, and the radio power consumption in various receive/transmit/idle states) of the different devices. The light switches are battery powered, while the hub and smart lights are powered from mains. Your nodes should NOT harvest energy (i.e. you should not model any energy generation). • Consider how long your simulations should run for. You should simulate the network for a period of time sufficient to explore expected/typical behaviour. How many simulation runs do you need in order to be able to draw general conclusions? You are strongly encouraged to look at what you are required to write about in Section 1 of your individual report, to ensure that you are making the necessary decisions and appropriately recording your justification for them. Part 2: Check your Model and Simulation Setup To check that you have made appropriate modelling decisions, and setup the simulation correctly, you should first check that the network performs as you would expect it to. Use OMNeT++ to simulate a small subset of the network (1 hub, 2 switches, 4 lights). All devices should be located sufficiently close to each other that they can communicate directly (i.e. multi-hop routing is not needed for this Part). Obtain simulation results for: - Reliability: what proportion of the packets that are sent reach their destination? Based on your chosen floorplan dimensions and radio transmit power, is the resulting transmission range appropriate for your simulation? - Latency: how long does it take for a packet to be received at its destination? - Energy Consumption: How much power do the various nodes consume? We encourage you to look at the what you need to write for Section 2 of your report. Consider whether or not you have enough results, or if you need to conduct more simulations. Part 3. Analyse the Network Performance Add all of the nodes back into the network (1 hub, 32 lights, 8 switches), located inappropriate places on your simulated floorplan. Your task is to analyse the performance of the full system you have modelled, in particular investigating the customer’s concerns,i.e. reliability, latency and energy consumption. This document does not provide a prescriptive or exhaustive set of questions for you to answer. However, you may wish to consider: • Reliability: How often might a switch be pressed, but the light not respond? Is this acceptable? If anode fails, does the rest of the network recover and operate correctly? • Latency: How long does it take for a light to turn on after pressing a switch? Is this an acceptable delay? What is the delay caused by; how might you improve it? • Energy Consumption: How much energy do the nodes consume? How large would a switch’s battery need to be to last a year; is this realistic? Consider the energy consumption of switches and lights, but ignore the energy consumption of the hub. Interpret your results; if the reliability/latency/power differs at different nodes in the network, what are the causes of this? How can you evidence this? Explore the routing table formed by the network. What does it look like, and does it change during simulation? Do the results depend on the type of node, or its distance/number of hops from the hub? Consider the different results you could obtain/plot to explore behaviour – for example, for energy consumption you could obtain data for Remaining Energy vs Time, or you could obtain results for the Remaining Energy at the end of the simulation. What is most appropriate? Think about suitable statistical methods to use to analyse and evaluate your results. It is unlikely that the average (mean) alone will ‘tell the whole story’ , and you may wish to consider additional appropriate metrics (e.g. range, variance, standard deviation, interquartile range). Note that analysis is more than just presenting data/results and describing them. You should discuss why the behaviour is seen, and what is happening to cause this. You may need to do some reading of the technical literature and/or further experiments to answer this. Deliverables and Marking Scheme There is a single deliverable, marked out of 40, contributing 40% of the credit for this module. Individual Report [total of 40 marks] (due by 4pm on Friday 13th December 2024, online hand-in) You should produce a written report documenting your investigation. The report must be single-spaced, single-column, use 12pt Times New Roman, and contain the following sections: Section 1: Network Model [maximum of 1000 words] You should include a screenshot showing the network topology, i.e. where various device types (light switches, smart lights and the hub) were spatially located. State and justify the physical dimensions of your floorplan (as defined in your .ned file). You should then state and justify all of the modelling decisions (i.e. the models and parameters that you chose) that you made in Part A of the coursework. Justify how all of these were appropriate for the scenario that you are modelling. You should refer to the contents of your simulation’s .ini file (which you will include in Appendix 1), referring to individual lines (or ranges of lines) of this by line number. As a minimum, you should ensure that you state and justify all of the following: • Radio and wireless channel o Radio transmit power, sensitivity, data rate, frequency, bandwidth o Channel background noise, path loss model, and associated parameters • Power/energy o Initial and nominal capacities of energy storage devices o Power consumption models and parameters in various states • Packet flows o How did you configure the devices to model packet flows through the network? o Packet generation start times, send intervals, packet lengths etc • Simulation setup o Simulation time/duration o Number of simulation runs Where appropriate, you should substantiate your modelling decisions using references to standards, datasheets, and the technical literature. Section 2: Model Validation [maximum of 1000 words] Present and explain how your results from Part 2 show that that packets are being appropriately sent, received and propagated through the small network at desired times, that energy is decreasing during the simulation, etc. For all of your results, you should consider ‘Is this what I expect?’ and ‘Why is this the case?’. For example: • If only 50% of sent packets are received, why are some of them getting lost? If 100% of sent packets are received, what’s ensuring that none of them get lost? Do you have results that evidence that this is indeed what is causing this behaviour? Is this supported by the technical literature? • If some nodes deplete their energy store more/faster than others, why is this? What influenceshow much energy they consume? Do you have results that show that this is indeed what is causing this behaviour? Is this supported by the technical literature? Section 3: Network Analysis [maximum of 1000 words] Present the results you obtained from simulating your full network in Part 3, and discuss the conclusions that you draw from them. Are your conclusions from Section 2 the same, or does the network behave differently at scale? Explore why the network performs as it does. Support your analysis and conclusions using the technical literature, where appropriate. For both Section 2 and Section 3, do not underestimate the importance of results – this is where you explore various aspects of your simulation (e.g. energy, latency, lost/received packets). Make sure that you evidence your conclusions (i.e. use figures to show data/graphs/networks etc) with results, and that you explain them in detail (i.e. don’t leave the reader(s) to draw their own conclusions from your results). Label all axes on graphs, and state units where appropriate. We encourage you to replot data obtained from your simulations (e.g. in Excel), rather than just pasting OMNeT++ screenshots in your report (as these can be difficult to read and omit important information) .
ECO4217 Monetary Policy Making 2024 Fall MONETARY POLICY MAKING — Project Report Instruction December 2, 2024 1 Logistics Congratulations! You have finished two midterm tests and all the quizzes,i.e., most of the coursework. This project report is the last piece for your evaluation. You have one week to finish the report. The deadline for submission is Dec 11, at 14:30 exactly. Be sure to submit your report on time. As we have agreed, late submissions will be penalized. This report’s format is problem-solving. In class, we focused on the operation and impact of monetary policies. We did not talk much about the design of monetary policies, i.e., how the central bank should do monetary policy-making. This report aims to complete this missing piece. Through this exercise, we will see why good monetary policy-making is hard at some fundamental level. Think about this problem using the concepts and methodologies you learned in class, and present your work as much as you can. Discussions between students are encouraged, but individual report writing is required. Copy-and-paste work will be assigned zero points! 2 Motivation Think about this. A professor cares about students learning something and, in addition, about them being happy. To motivate students to learn, at the beginning of the semester, the professor may announce that the class content is difficult and that students can pass only by studying very hard. However, when the professor has to give grades at the end of the semester, should the professor enforce his rules and standards strictly? Probably not. Because how much the students have learned is fixed and will no longer be affected by grades. But, you can always make students happier by inflating the evaluation. Taking this into consideration, however, students may not take the professor’s announcement (threat) very seriously at the very beginning. Monetary policy-making shares this logic. Suppose low inflation is desirable for society in the long run, and so the Fed announces that it intends to achieve such a target. Observing this announcement, wage contracts are made in the private sector. Now, does the Fed want to carry out the low inflation policy after wage contracts have been signed? No! By increasing the inflation rate (expansionary monetary policy), the Fed can reduce the real wage and stimulate the economy. Considering this, the private sector would not believe the low inflation target in the first place. This is actually the contribution of Kydland and Prescott (1977), and helped them earn the Nobel Prize in 2004 “for their contributions to dynamic macroeconomics: the time consistency of economic policy and the driving forces behind business cycles” . Digest the idea a bit, and solve the following exercise step by step. This exercise is a simplified version of Kydland and Prescott (1977). 3 Exercise The starting point is the supply curve we introduced in class π = πe + λ(Y − YP) where YP is the potential output, π e is the expected inflation of the private sector (or the public), and λ is the responsiveness of inflation to the output gap. Note that we omit the supply shock here. In the lecture, we emphasize the interpretation of the supply curve ashow the output gap shapes inflation. In this exercise, it is useful to think the other way around. Note that we can write the supply curve as follows. Y - Y P = λ/1(π − π e ) (1-1) where λ 1 can be thought of as the responsiveness of the output gap to inflation surprises. Therefore, there will be a positive output gap if inflation is higher than expected. Suppose that the Fed has the following loss function, which can be thought of as the negative of the social welfare function L(π, Y ) = (π - πO )2 + η(Y - YO )2 (1-2) where YO is the output target, π O is the inflation target, and η reflects the Fed’s relative weights on output stabilization. A higher η means the Fed puts more weight on output stabilization over inflation stabilization. π O and YO are what the Fed thinks is the best for the economy. The Fed tries to minimize the loss function. Without loss of generality, we assume π O = 0, i.e., the Fed has a zero inflation target. The structure of the game, including the value of YP and YO , are all public information. For simplicity, we think that the Fed can directly choose the level of inflation. The game between the Fed and the private sector (the public) is as follows. 1. The Fed announces an inflation target π F . 2. The private sector, after observing π F , forms an inflation expectation π e. 3. The Fed chooses π to minimize the loss function. 4. Production and social welfare realize. To complete the description of the environment, we must specify how the private sector’s inflation expec- tations are formed. Here, we adopt the notion of perfect foresight that we introduced in the first class, that the private sector’s inflation expectation must turn out to be exactly correct. Therefore, unless otherwise specified, we have assumption I as follows. Assumption I: π e = π (1-3) A second assumption we will always maintain is that the target output YO is higher than the potential output YP . A defense to this assumption could be that the potential output is too low for various distortions and frictions in the economy. Assumption II: Y O > Y P (1-4) Make sure you fully understand the environment, and answer the following questions. (Suggestive answers for questions 1-3 are given to you. I recommend you think about them by yourself first and still show your work in your own words.) 1. What is the unconstrained social optimal inflation and output? Note here that “unconstrained” means you can specify inflation and output directly and not be constrained by the supply curve. 2. Suppose the Fed has a commitment technology, i.e., it can creditably promise that it will enforce the announced level of inflation. Find out the equilibrium π F , π , π e , Y , and L. 3. Now, suppose the Fed does not have any commitment technologies. If the Fed still announces the π F you found in the previous question, does it have an incentive to deviate later? Find out L in the case that the Fed announces π F you found in the previous question and the private sector believes the Fed, but the Fed deviates in the end. Note here you need to abandon the perfect foresight assumption. 4. Solve for the equilibrium π F , π , π e , Y , and L when there are no commitment technologies and perfect foresight holds. 5. Redo 1-4 with π O > 0. Feel free to make necessary assumptions if needed. 6. Read this Vox article on Paul Volcker’s fight against inflation (click here). Use what you have learned from this exercise, analyze (verbally) why Volcker could successfully curb the inflation in the 1980s. 7. (optional, try this one only if you are interested) Note that the Fed’s attitude η is important. In the above analysis, we assume η is fixed and publicly known. However, in reality, the public may not know whether the Fed is weak (with a high η) or strong (with a low η) in fighting inflation, i.e., η can be private information. Therefore, the Fed may care about its reputation in a repeated game (think about Paul Volcker in the 1980s). In other words, a weak Fed may have the incentive to pretend to be a strong one if it sufficiently cares about the future. Try extending the above model to illustrate this idea. 4 Suggestive Answers 1. Unconstrained social optimum means π = 0 and Y = YO . Therefore, L = 0. 2. Under commitment, π = πF , and π e = π F . The game is reduced to a one-shot game. The loss function L = π2 + η(YP - YO )2 To minimize it, the Fed will choose π = 0 = π F = π e. Output Y = YP and L = η(YP - YO )2 . Note the social loss here is higher than the unconstrained optimum. 3. Without commitment technologies, the game is dynamic. A common routine for thinking about dynamic problems is to use backward induction. According to the question, the public naively believes the Fed, i.e., π e = 0, and therefore, Y = Y P + π . Use backward induction, the loss function will be L = π2 + η(Y - YO )2 = π2 + η(YP + λ/1π − YO)2 Now we see the Fed’s motive to deviate: by increasing inflation, there is a gain in production. As- sumption II is important here: only when the potential output is too low for the Fed can the deviation be justified. To find the optimal π, it’s convenient to use some calculus. If you are unfamiliar with calculus, the high school method for quadratic functions can also do the job. Either way, we end up getting π = η+λ2/ηλ (YO - YP ) > 0. Note that the deviated inflation is higher than zero, and output is higher than YP.
Final 291 section 2 300 points 1) A school has 1600 students and they are going to vote as to whether they will completely convert the school completely off fossil fuels. How many students would you have to poll to be 95% confident of the outcome within +/- 0.5% of the vote? (25 points) 2) Earthquakes can be broken into two classes based on the directions the earth moves when they fracture. The classes can be compared across time, for whether earthquakes occur in a given region in a small time period, and if they occur in the next small time period, and it can be added up across several time periods, below is a table for a region off Indonesia over a 30 year period Second time period, earthquake happens Second time period no earthquake happens Marginal Sums First time period, Earthquake happens 148 274 422 First time period no earthquake happens 276 2626 2902 Marginal sums 424 2900 3324 Is happening of an earthquake in one time period statistically independent of happening in the next time period? Test at the .01 level (20 points) These are earthquakes of the same type, Which cells have higher than expected occurrence if independence is true. (Use the deviation table). (10 points) 3) The earthquake chart is the same chart, only comparing when earthquakes of different types follow one another Second time period, earthquake happens Second time period no earthquake happens Marginal Sums First time period, Earthquake happens 5 314 319 First time period no earthquake happens 314 2691 3005 Marginal sums 319 3005 3324 Are they statistically independent now (.01 level again) (16 points) How do the deviations from expectation under independence differ from the chart in problem 2 (hint look at the pattern of pluses and minuses) (8 points) If you think about what each cell means, what do these differences mean in terms of the way the two types of earthquakes interact (6 points) 4) In NCI60 in the ISLR data set (100) a. Identify the cancer types with more than 3 cell lines present. b. From those Identify cancers with hyper or hypo active genes at the 0.2 FDR level (not independent) c. Identify common genes between every pair of the cancers identified in b. d. Are there any genes shared as strangely active between 3 cancers? 5) The diabetes data set is a prospective study of onset of adult diabetes given a number of risk factors among the Pima Indian tribe. Using the diabetes.csv data set (100) a. Separate the first half of the data from the second half, use the first half for training, second for testing b. Using the training data i. Construct the full logistic regression model for outcome ii. Using backwards selection construct the logistic regression model with every p value for the coefficients < .05 (Show Steps!!!) c. Predict the “response” (eg type=”response”) for the full logistic regression model for i. the training data set, ii. the test data set, d. Predict the “response” for the smallest logistic model from the backwards selection exercise i. the training data set, ii. the test data set, e. Using random forest, build a model on the training data f. You now have 3 models, Full Logistic, smallest logistic, and random forest. For predictions of each calculate and tabulate i. Number of correct positives ii. Number of False positives iii. Number of correct negatives iv. Number of false negatives. g. Using the results off, is there one of the 3 methods which appears best in modeling new results, or does it depend on whether it is more important to identify positives (predict diabetes) or negatives (predict health) h. Now redo analysis twice using random selection of 384 out of 768 for training and the complement for testing. Is there anything you can conclude with this additional information about the merits of each approach? 6) Conceptual question: Suppose you have a null and alternative hypotheses that are completely defined in terms of the specific probability distributions they represent. What is the main difference between using a likelihood ratio test, and using bayes rule to decide between the two. (20)
LUBS2227 Financial Econometrics Semester One, 2024/2025 50% Assignment The Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT) models offer competing specifications of econometric models of stock returns. In this assignment your task is to build your own dataset and perform. econometric analysis, which sheds light on whether CAPM or APT offers more empirically valid econometric models of stock returns. Your analysis should cover 10 randomly selected companies included in the S&P 500 index and you should use datasets with at least 100 monthly observations to estimate any of your econometric models. In your written report you should address the following key points: · Explain the theoretical motivation for your econometric models. · Explain how you have built your dataset and include descriptive statistics for all variables. · Report and interpret your econometric results. This should include tables presenting coefficient estimates, a measure of their statistical significance, a measure of fit of your models to the data and names of all variables at minimum. · Draw conclusions on the empirical validity of CAPM and APT style. models based on the results obtained from your econometric models. Any tables included in your assignment should be presented in a style. of a peer reviewed journal in accounting, finance or economics. It is not appropriate to submit screenshots of tables from STATA as part of your assignment and marks for presentation and style. will be deducted for doing so. Assignments should be a maximum of 1,000 words in length. This excludes a maximum of 5 tables/figures, which will not count towards the word limit. All coursework assignments that contribute to the assessment of a module are subject to a word limit, as specified on the assessment brief. The word limit is an extremely important aspect of good academic practice, and must be adhered to. Unless stated otherwise in the relevant module handbook (if one has been provided), the word count includes EVERYTHING (i.e. all text in the main body of the assignment including summaries, subtitles, contents pages, tables, supportive material whether in footnotes or in-text references) except the main title, reference list and/or bibliography and any appendices. It is not acceptable to present matters of substance, which should be included in the main body of the text, in the appendices (“appendix abuse”). It is not acceptable to attempt to hide words in graphs and diagrams; only text which is strictly necessary should be included in graphs and diagrams. You are required to adhere to the word limit specified and state an accurate word count on the cover page of your assignment brief. Your declared word count must be accurate, and should not mislead. Making a fraudulent statement concerning the work submitted for assessment could be considered academic malpractice and investigated as such. If the amount of work submitted is higher than that specified by the word limit or that declared on your word count, this may be reflected in the mark awarded and noted through individual feedback given to you. The deadline date for this assignment is 12:00:00 noon on Thursday 19th December 2024. An electronic copy of the assignment must be submitted to the Assignment Submission area within the module resource on the Blackboard MINERVA website no later than 12:00:00 noon prompt on the deadline date. Faxed, emailed or hard copies of the assignment will not be accepted. Failure to meet this initial deadline will result in a reduction of marks, details of which can be found at the following place: https://students.business.leeds.ac.uk/assessment/code-of-practice-on-assessment/ SUBMISSION Please ensure that you leave sufficient time to complete the online submission process, as upload times can vary. Accessing the submission link before the deadline does NOT constitute completion of submission. You MUST click the ‘CONFIRM’ button before 12:00:00 noon for your assignment to be classed as submitted on time, if not you will need to submit to the Late Area and your assignment will be marked as late. It is your responsibility to ensure you upload the correct file to the MINERVA, and that it has uploaded successfully.
Assignment – PNG Images Version 1.00 1. Submission Guidelines Deadline: 9:00 AM on Friday 13 December Submission procedure: Submit only one file labelled png.py through blackboard (via TurnItIn) Version requirement: Your code must run using Python 3.11.4 on a PC Allowable import modules: math and zlib only. No other modules may be imported. 2. Overview The Portable Network Graphics (PNG) format is a format for storing images. For this assignment, you are tasked with writing code that can be used to open a PNG file, store its contents in the program, and save modified PNG image files. This is a real-world assignment, meaning that you are working on a practical problem that requires that you find additional information to complete the task. The official PNG documentation is available online. https://www.w3.org/TR/png/ We expect that all students will use the internet and textbook resources to learn concepts needed for this assignment. There will be many concepts, in-built functions, and other aspects of Python coding, which have not been directly taught in labs or lectures. It is up to you to fill in these gaps in knowledge by engaging in both the course and self-learning. Keep track of the version number of this assignment and ensure that your code meets the latest specifications by the deadline. This assignment challenge is very similar to a real-world situation where specifications evolve with the person writing code for the specifications. We will be updating the assignment descriptor throughout the week to add clarity, remove ambiguities, and fix descriptor errors. Post on the Ed Discussion board any clarification requests, ambiguities, or descriptor errors. The Teaching Assistants will then raise this with the instructor who will then update the version of the assignment and announce it via email. 3. PNG Format You must adhere to the PNG version specified in the hyperlink above. However, you will only need to implement only a small subset of the total PNG specifications. Implement only the following chunks: • IHDR • IDAT • IEND • Other chunks. There may be other chunks present in the PNG image file. For this assignment, you do not need to implement those other chunks. However, your programme should run smoothly even if they are present. In other words, ignore the other chunks if they are present in the image. You will only need to read and write images that have the following image header (IHDR) specifications: • A bit depth of 8 • A colour type of 2 • A compression method of 0 • A filter method of 0 • An interlace method of 0 When you read the example image (brainbow.png), you should be able to find these values within the file. We have uploaded brainbow.png as a sample image for you to open and use. Your code should be able to handle other PNG files, including images with different widths and heights. We may also test very large images. We will certainly test other .png files to ensure that your code is not hard coded to produce the sample outputs. 4. Class PNG Create a class named PNG that will allow users to interact with PNG files. 4.1. Attributes Objects of type PNG should have the following variable attributes: data A value of type bytes. This represents the data bytes from the png file. The default values should be b'' info A value of type str. This represents information about the png file. The default value should be '' and should become either the name of the file or 'file not found'. width A value of type int. This represents the width of the image in pixels. The default value should be 0. height A value of type int. This represents the height of the image in pixels. The default value should be 0. bit_depth A value of type int. This represents the bit depth of the image data stored in the png file. The default value should be 0. color_type A value of type int. This represents the color type of the image data stored in the png file. The default value should be 0. compress A value of type int. This represents the compression method of the png file. The default value should be 0. filter A value of type int. This represents the filter method of the png file. The default value should be 0. interlace A value of type int. This represents the interlacing method of the png file. The default value should be 0. img A value of type list. This represents a 3-dimensional array where the 1st dimension is the image rows, the 2nd dimension is the image columns, and the 3rd dimension is the red, green, or blue values. The red, green, and blue values should be of type int and should range between 0 and 255 only. The default value should be an empty list. 4.2. Methods For this assignment, you may assume that all arguments put into each method are of the correct type. init (self) Return values: None Arguments: A reference to itself, self. Implementation: This method should initialise all of the above member attributes to their default values. 10 marks for implementation load_file (self, file_name) Return values: None Arguments: A reference to itself (self) and a file name (file_name) of type str. Implementation: The load_file() method should open the file specified by file_name and store all of the file’s data into the member attribute data. If the file is found, then assign file_name into the info member attribute. If the file is not found and a FileNotFoundError exception is raised when trying to open the file, then assign 'file not found' into the info member attribute. 10 marks for implementation valid_png(self) Return values: True or False Arguments: A reference to itself, self. Implementation: Reads the PNG signature and returns whether the correct values were found or not. Returns True if the PNG signature was correctly specified in the file. Returns False if the PNG signature was not correctly specified in the file. 10 marks for implementation read_header (self) Return values: None Arguments: A reference to itself, self. Implementation: Reads the image header chunk (IHDR) and updates the relevant member attributes. Update the width, height, bit_depth, color_type, compress, filter, and interlace attributes according to the information stored in the image header chunk. 10 marks for implementation read_chunks (self) Return values: None Arguments: A reference to itself, self. Implementation: Reads through all the chunks and updates the img attribute. Read through the remaining chunks following the PNG signature and IHDR chunk. This includes IDAT and IEND chunk types that include the image data. There may be other chunk types present in the PNG file, but gracefully ignore those chunk types. Your program should still run smoothly even if those chunk types are present in the PNG file. One of the challenges of this method is being able to extract the image data from the IDAT chunk(s). After extracting the image, store it in the member attribute img. img represents a 3-dimensional array where the 1st dimension is the image rows, the 2nd dimension is the image columns, and the 3rd dimension is the red, green, or blue values. The red, green, and blue values should be of type int and should range between 0 and 255 only. 20 marks for implementation 10 marks for efficiency save_rgb (self, file_name, rgb_option) Return values: None Arguments: A reference to itself, self, a file name (file_name) of type str, and a saving option (rgb_option) of type int. Implementation: Saves the red, green, or blue channels of the img attribute into a PNG file named file_name as determined by the setting of rgb_option. When rgb_option is set to 1, store the red channel of the image into the PNG file. When rgb_option is set to 2, store the green channel of the image into the PNG file. When rgb_option is set to 3, store the blue channel of the image into the PNG file. We should be able to open your PNG file using a regular image viewing application. 10 marks for implementation 10 marks for efficiency 5. Coding style and commenting Ensure that your code is readable and well commented. We will be marking according to spacing, naming, comments, length, and general readability. 10 marks for coding style and commenting 6. Coding rules Only the PNG class definition and imports to the math and zlib modules should be in the global space of png.py. There should be no other global variables or definitions. You are free to define methods within the PNG class, which we will not call. A main() function could be used if you so desire, but it is not required. Only the math and zlib modules are allowed for this assignment. No other module is allowed. 7. Marking criteria We will mark your submitted png.py code according to the following categories: • 70 marks: Implementation and evidence of coding knowledge • 20 marks: Coding efficiency • 10 marks: Coding style and commenting We will import your png module near the top of our script. to test your code. We will run several test conditions against each of your classes, including its attributes and methods. After running each method, we will investigate what was assigned in your attribute variables. So, the type, length, etc. of your attribute variables must perfectly match how we defined them in this specification. We will test far more test cases than the single example we have included in this assignment sheet.
