MFE104TC – Electrical Circuits 1st SEMESTER 2024/25 Assignment Undergraduate – Year 2 Submission Deadline: Dec. 8, 2024. 11:59pm Question – 1 (5/100) Application of Kirchoff’s Current Law (KCL)and Kirchoff’s Voltage Law (KVL). Determine Ix in the circuit shown in Figure 1. Figure 1 Question – 2 (10/100) Delta-to-Wye Transform. Apply the delta-to-wye transform method to find the current and power supplied by the 40V source in the circuit shown in Figure 2. Figure 2 Question – 3 (20/100, with 4 sub-questions, each 5/100) Nodal /Mesh Analysis/ Thévenin theorem/ Source transform. According to Figure 3-1, (a) Determine whether nodal or mesh analysis is more appropriate in determining i1 and v3 , if element A is replaced with a short circuit, then carry out the analysis. (b) Using both nodal and mesh analysis methods to determine v3 , if element Ais replaced with a voltage-controlled current source (i=v3) as shown in Figure 3-2. (c) Calculate the power developed on each element to determine whether supply or absorb energy and verify the energy conversion in this circuit. (d) Find the both Thévenin and Norton equivalent circuit with respect to the terminal A and B for the circuit in Figure 3-2. Figure 3-1 Figure 3-2 Question – 4 (10/100) Op Amp Circuit If the circuit shown in Figure 4 is to be used for operation vo2=3v1+6v2 , find Rf1 and R2. Figure 4 Question – 5 (20/100, with question (a) 5/100, (b) 10/100, (c) 5/100) Solving RL Circuit For the circuit represented by Figure 5, obtain expressions for i(t) (a) at t4s; Figure 5 Question – 6 (20/100, with 4 sub-questions, each 5/100) Sinusoidal steady-state AC analysis and power calculation (a) Draw the phasor representation of the circuit shown in Figure 6; (b) determine the Thevenin equivalent seen by the capacitor and use it to calculate Vc(t). (c) Determine the current flowing out of the positive reference terminal of the voltage source. (d) Please find the average power absorbed by ac voltage source. Figure 6 Question – 7 ( 15/ 100, with question (a) 2/100, (b) 3/100, (c) 5/100, (d) 5/100) Use of Laplace transform in analyzing RC circuit The initial voltage across the capacitor is v(0-)=1.5V and the current source is 700u(t) mA. (a) Write the differential equation which arises from KCL, in terms of the nodal voltage v(t). (b) Take the Laplace transform of the differential equation. (c) Determine the frequency-domain representation of the nodal voltage. (d) Solve for the time-domain voltage v(t) Figure 7
1 Sequent calculus Your task is to implement thesequent calculus in Haskell. In the mandatory part of the project, you will omit implication → and bi-implication $ and construct proofs as explained on pages 126–127 of the textbook using the rules given there: In the optional part, you will add rules for → and $. Recall that proofs start with a sequent and are constructed by applying rules, working upwards, until you reach simple premises involving only“bare”predicates, not containing any connectives. The proof shows that the sequent you started with follows from those premises. If the proof ends in the empty set of simple premises, you have shown that the sequent you started with is universally valid. Here’s an example of a sequent calculus proof, which proves that F ((¬p _ q) Λ ¬p) _p is universally valid: Here’s another example, which proves that F ¬ ((¬a _ b) Λ (¬c _ b)) _ (¬a _ c) follows from a, b F c: The number of times that a premise appears is immaterial, because we’re interested in the set of premises from which the conclusion holds. In this proof, the premise a, b F c happens to appear twice. There’s often more than one proof starting from a given sequent, but all end with the same set of premises. Because each rule of the sequent calculus eliminates one connective from an antecedent or a succedent, proofs always terminate. 2 Implementing sequent calculus in Haskell To implement the sequent calculus in Haskell, you will start with an algebraic datatype similar to the one in FP Tutorial 6 that defines propositions with variable names of type String: type Name = String data Prop = Var Name | Not Prop | Prop : || : Prop | Prop :&&: Prop | Prop :->: Prop | Prop :: Prop The last two cases will be required only for the optional part but they are included from the start to allow the optional part to be coded as a simple extension of the mandatory part. Sequents are defined as follows: data Sequent = [Prop] : |=: [Prop] infix 0 : | =: We use unordered lists of propositions, possibly with repetitions, to represent the sets of antecedents and succedents of sequents. You are provided with a file Sequent .hs containing the above type definitions together with code for declaring Prop and Sequent as instances of Show and (for purposes of QuickCheck test case generation) of Arbitrary. If you want to use QuickCheck to test your code (strongly recommended! always test your code!) then you will need to comment out the following two lines for generating test cases that include → and until you get to the optional part: , liftM2 (:->:) p2 p2 , liftM2 (::) p2 p2 Don’t make any other changes to Sequent .hs. Also provided is a skeleton file called Project .hs which imports Sequent. Your job is to define the function prove :: Sequent -> [Sequent] in Project .hs which, when applied to a sequent, produces the list of simple sequents from which that sequent follows according to a sequent calculus proof. You need not produce the proof itself, just the list of premises. For the first example above, it should produce > p = Var "p" > q = Var "q" > prove ([] : | =: [((Not p : || : q) :&&: Not p) : || : p]) [] For the second example, one correct result would be > a = Var "a" > b = Var "b" > c = Var "c" > prove ([] : | =: [(Not ((Not a : || : b) :&&: (Not c : || : b))) : || : (Not a : || : c)]) [[a,b] : |=: [c]] Because of our use of unordered lists to represent sets, another correct result would be > prove ([] : | =: [(Not ((Not a : || : b) :&&: (Not c : || : b))) : || : (Not a : || : c)]) [[a,b] : | =: [c,c],[b,a,b] : |=: [c]] and there are many others. 3 How to proceed How you proceed is up to you. That’s why this is a project and not a tutorial exercise. One way to go is to start by defining the rules of sequent calculus as Haskell functions, with for instance orL :: Sequent -> Maybe [Sequent] Note that the result type is Maybe [Sequent]. We use [Sequent] rather than Sequent because the _L rule has more than one premise. And we use Maybe [Sequent] rather than [Sequent] because _L isn’t applicable to all sequents, only ones having an antecedent with _ as its main connective. For the _R rule, you could define a function orR :: Sequent -> Maybe Sequent since it can produce at most one premise, but it’s probably better to use the same type for all rules for the sake of the next step: orR :: Sequent -> Maybe [Sequent] You might even want to define a type for rules: type Rule = Sequent -> Maybe [Sequent] orL, orR :: Rule Hint: The main work in applying a rule to a sequent involves finding appropriate items in the antecedent list and/or the succedent list and replacing them with new items. You might find useful functions for doing this in Data.List. Now you can use the definitions of the rules to search for a proof. Given a sequent, prove tries rules until one applies (that is, it produces Just seqs instead of Nothing). Then do that to each of the sequents in seqs . Keep going until none of the rules applies. The result is a list of simple sequents from which the original sequent follows. The definition of prove will probably rely on helper functions that try individual rules and/or lists of rules. How you do this is up to you. 4 Optional Material: Adding implication and bi-implication Following the Common Marking Scheme, a student with good mastery of the material is expected to get 3/4 points. This section is for demonstrating exceptional mastery of the material. It is optional and worth 1/4 points. The definitions of the types Prop and Sequent already allow for them to contain implication → and bi-implication . Here are the sequent calculus rules for these connectives from page 222 of the textbook: Here is a proof that a → b F ¬b → ¬a is universally valid, using these rules and the ones given earlier: For the optional part, extend your function prove :: Sequent -> [Sequent] to dosequent calculus proofs of sequents involving propositions that may contain implication and/or bi-implication. Applying prove to asequent that doesn’t contain implication orbi-implication should produce the same result as before. If you do the optional part, you don’t have to submit two versions of prove; the extended version, that works for implications and bi-implications as well as the other connectives, will suffice. For the example above, it should produce > a = Var "a" > b = Var "b" > prove (a :->: b : |=: Not b :->: Not a) [] If you use QuickCheck for testing your code, remember to restore the two lines of the declaration in Sequent .hs that Prop is an instance of Arbitrary so that it will also generate test cases including :->: and ::. 6 Marking The programming project will be marked by your tutor, and is worth 20% of the mark for Inf1A. Your mark on the scale 0–4 will be based mainly on the following tests. (Your tutor will also check your code to make sure, among other things, that it is not written to pass only these tests.) The tests have been set up in the automarker. They will be run every time you submit Project .hs, and you will be able to check the results. You can submit as often as you like, but only the last submission before the deadline will be taken into account in the marking. The sequents in the tests below have been typeset to make them a little easier to read, but of course the actual tests use Haskell syntax. Because of our use of unordered lists to represent tests, lists of assumptions that are equivalent to those shown as results are also correct. To get 1 point, it’s enough that any one of the following one-step proofs is correct. Non-working code that shows some sensible work may also be awarded 1 point. > prove ( F a _ b ) -- _R [ F a, b ] > prove ( a _ b F ) -- _L [ a F , b F ] > prove ( F a Λ b ) -- ΛR [ F a , F b ] > prove ( a Λ b F ) -- ΛL [ a, b F ] > prove ( F ¬a ) -- ¬R [ a F ] > prove ( ¬a F ) -- ¬L [ F a ] > prove ( a,b F b,c ) -- I [ ] To get 2 points, the following proof, which uses only the non-branching rules, must also be correct. > prove ( F (¬ (e Λ (f Λ ¬¬c))) _ (¬a _ c) ) [ ] To get 3 points, most or all of the following proofs must also be correct. > prove ( F ((¬a _ b) Λ ¬a) _ a ) [ ] > prove ( F ¬ ((¬a _ b) Λ (¬c _ b)) _ ¬a _ c ) [ a, b F c ] > prove ( ¬c _ (f _ b), (a _ d) Λ (b _ b) F ¬ (d _ b), ¬c _ (f Λ e), (f Λ b) _ (c Λ e), ¬ (a _ c), ¬e Λ (c _ e), e ) [ ] > prove ( (b _ f) _ (d _ c), ¬ (d _ a), ¬ (d Λ f), ¬ (a Λ f), (a _ e) Λ (b _ c), c F ) [ b,c, e F a,d , b,c, e F a,d,f , b,c,e, f F a,d , c, e F a,d , c, e F a,d,f , c,e, f F a, d ] > prove ( b,(d _ b) _ ¬c, ¬ (d Λ f) F (b _ d) Λ (c Λ e), (a _ c) _ ¬f, ¬ (f Λ f), (e Λ d) Λ (a _ d),c, (b Λ b) Λ (e _ b) ) [ ] > prove ( (f _ a) Λ ¬f,(a _ a) Λ (c _ b), ¬a _ ¬c, ¬d _ (a _ d) F (f Λ d) _ (d _ d), (c _ e) _ (d _ e), (b _ a) Λ (f _ e) ) [ a, b F c,d,e, f ] > prove ( (a _ b) _ ¬d,(e _ b) Λ ¬f F a, (f Λ f) _ (f Λ f), ¬f Λ (e _ c), (f Λ b) Λ (e _ a) ) [ b F a,e,c,f , b F a,c,d,e, f ] > prove ( (a _ f) _ (c _ d), (b Λ f) _ (f _ a), (f _ f) Λ ¬f, ¬a _ (c _ e) F ¬e Λ ¬c, (b Λ c) _ ¬a, ¬ (e _ d) ) [ ] > prove ( F (d _ b) _ (e Λ a), ¬f Λ (d Λ b), (c Λ b) Λ ¬e, (c _ c) _ (d _ d), ¬e Λ (a Λ e), (a _ e) _ (c _ d), (d _ d) Λ ¬e, (c Λ d) Λ ¬a, ¬ (e _ c) ) [ ] To get 4 points, most or all of the following proofs involving implication and/or bi-implication must also be correct. > prove ( a → b F ¬b → ¬a ) [ ] > prove ( (c _ d) → (d Λ f), (a a) (b _ a) F (c b) (b → a), ¬ (b _ d), ¬ (b Λ d), (d f) → (c _ e) ) [ b,a,f,d F c, e ] > prove ( ¬ (f f), ¬f (d e), (d → f) (a _ b) F (d Λ c) → (c d), ¬ (c e) ) [ ] > prove ( (e → b) → (a → f), (b _ a) _ ¬f,(d b) (f Λ e) F (d d) _ (e c), ¬ (d → f) ) [ ] > prove ( (f _ b) Λ (b → d), (e _ d) _ ¬e, (f → e) → ¬f,(e _ d) Λ (d c), ¬ (c b), ¬f Λ (a Λ a), (f c) → (c _ f) F (e Λ f) _ (f Λ a), ¬f Λ ¬d,(f _ c) (f b) ) [ ] > prove ( (e Λ f) (a Λ e), (b _ d) Λ (f c), ¬ (d f), (d Λ e) _ (c → f), (e f) (b _ d) F (e _ d) Λ ¬e ) [ f,e,a,b, c F d ] The challenge part isn’t worth any points, but the following tests are included in the automarker. Correct for these means getting the same assumptions with a proof that is no longer than the number of rules shown, which is achieved by the sample solution. > proveCount ( (a _ f) _ (c _ d), (b Λ f) _ (f _ a), (f _ f) Λ ¬f, ¬a _ (c _ e) F ¬e Λ ¬c, (b Λ c) _ ¬a, ¬ (e _ d) ) ([ ], 168) > proveCount ( ¬c _ (f _ b), (a _ d) Λ (b _ b) F ¬ (d _ b), ¬c _ (f Λ e), (f Λ b) _ (c Λ e), ¬ (a _ c), ¬e Λ (c _ e), e ) ([ ], 39) > proveCount ( b,(d _ b) _ ¬c, ¬ (d Λ f) F (b _ d) Λ (c Λ e), (a _ c) _ ¬f, ¬ (f Λ f), (e Λ d) Λ (a _ d),c, (b Λ b) Λ (e _ b) ([ ], 58) > proveCount ( (c _ d) → (d Λ f), (a a) (b _ a) F (c b) (b → a), ¬ (b _ d), ¬ (b Λ d), (d f) → (c _ e) ) ([ a,b,f,d F c, e ], 144) > proveCount ( ¬ (f f), ¬f (d e), (d → f) (a _ b) F (d Λ c) → (c d), ¬ (c e) ) ([ ], 105) > proveCount ( (e → b) → (a → f), (b _ a) _ ¬f,(d b) (f Λ e) F (d d) _ (e c), ¬ (d → f) ) ([ ], 306) > proveCount ( (f _ b) Λ (b → d), (e _ d) _ ¬e, (f → e) → ¬f,(e _ d) Λ (d c), ¬ (c b), ¬f Λ (a Λ a), (f c) → (c _ f) F (e Λ f) _ (f Λ a), ¬f Λ ¬d,(f _ c) (f b) ) ([ ], 229) > proveCount ( (e Λ f) (a Λ e), (b _ d) Λ (f c), ¬ (d f), (d Λ e) _ (c → f), (e f) (b _ d) F (e _ d) Λ ¬e ) ([ a,b,c,e, f F d ], 491)
Through the lens of industries: The Impacts of the 1997 Asian Financial Crisis and the 2008 Global Financial Crisis on Thailand Purpose of the paper This research paper is going to do the comparative study of the impacts of Thai industries within the two most globally effective financial crises: the Asian Financial Crisis in 1997 and the 2008 Global Financial Crisis. Research Questions The hypothesis addressed in the paper is that Thailand’s progressive government regulations stimulated by the Asian financial crisis, have played a beneficial role in helping this country recover from the global financial crisis occurred later. To analyze the credibility of this hypothesis, the paper is going to focus on two main questions: (1) What are the differences in the adverse and positive impacts of the two financial crises on major industries (agriculture, manufacturing, service) of Thailand?; and (2) What are the differences of transmission mechanisms between these two financial crises ( transmission mechanism)? Has this transmission mechanism evolved? Importance of this study There have been many in-depth published studies and analyses on the impacts of Thailand on the Asian financial crisis or the global financial crisis, but there is a gap of comparing them. Haughton and Khandker’s (2013) study analyze the effects of the 2008 great recession from the of microeconomic perspective by actual data analysis, manifesting that among various social groups in Thailand, most people, including low-income households and non-urban residents, are not threatened with a reduction in real expenditure, and even have a positive effect. This finding is dramatically opposite with the severe economic deterioration of Thailand experienced in 1997, inspiring my study to analyze if there was a connection between Thailand’s government regulations improved by the Asian financial crisis and the benign influences caused by the 2008 global crisis. Since comparing Thailand's performance in the two crises with the impact on the country's economy leaves room for debate in this area. Therefore, doing comparative research is helpful for providing a more general contexts of crises, enabling policymakers to grasp more development patterns of industries, guiding the long-term development and structural adjustment of industries in Thailand and various countries around the world may get implications. Methodology This study would start with a brief discussion about the background and progress of the two financial crises through document analysis and literature reviews, reveal their influences on Thailand afterwards ( the third-generation model of crisis crises , relative key terms). In the second part of the paper, it is going to theoretically analyze the transmission mechanisms and channels of financial crises in industries. To achieve this purpose, this study will then conduct empirical analysis, including the application of the vector autoregressive (VAR) model to verify the transmission channels of financial crises on major industries (Chudik & Fratzscher, 2011), and further analyze whether the two financial crises have a significant impact on major industries. Since the VAR model has shown a good performance in measuring Thailand’s economic growth led by exports in large time span (Romyen et al., 2019), this model would be helpful for capturing the impacts of the two crises on Thailand and making a more intuitive horizontal comparison. Meanwhile, the study would apply the data collection techniques from the Thai official survey to achieve quantitative credibility and reliability, using survey data provided by the World Bank/Bank of Thailand/IMF ( ; looking at real GDP per capita, % of employment and unemployment in three industries, well-being of people in these industries). In the third section, the study would discuss if Thailand applied the lessons learned from the Asian Crisis and response better to the 2008 global crisis. References Chudik, A., & Fratzscher, M. (2011). Identifying the global transmission of the 2007–2009 financial crisis in a GVAR model. European Economic Review, 55(3), 325-339. Edey, M. (2009). The global financial crisis and its effects. Economic Papers: A journal of applied economics and policy, 28(3), 186-195. https://ideas.repec.org/a/eee/eecrev/v55y2011i3p325-339.html Haughton, J., & Khandker, S. R. (2013). The surprising effects of the Great Recession: losers and winners in Thailand in 2008–09. World Development, 56, 77–92. https://doi.org/10.1016/j.worlddev.2013.10.018 Romyen, A., Liu, J., & Sriboonchitta, S. (2019). Export–Output Growth Nexus Using Threshold VAR and VEC Models: Empirical Evidence from Thailand. Economies, 7(2), 60. https://doi.org/10.3390/economies7020060
Fall 2024 STAT 5140 Final Project Dataset • (Preferred) Option 1: Find your own data set of interest. If you have difficulties to find your own data set of interest, you can consider the following two options. • Option 2: The dataset for this project is collected from a clinical trial by the Mayo Clinic between 1974 and 1984 on studying Primary Biliary Cirrhosis (PBS). The dataset can be downloaded from the website http://lib.stat.cmu.edu/datasets/pbc. Please read the data description and variable definition carefully on the website. • Option 3: The SEER datasets. Follow the instructions on https://seer.cancer.gov/data/ to extract the SEER datasets. After extraction the data files are in the incidence directory. The same directory contains the important file seerdic.pdf that explains the meaning of the fields in the data files. The SEER database is extremely large and you will probably want to subset it. The www site of the American Cancer Society (www.cancer.org) has a great deal of interesting information about cancer that can help you choose meaningful projects. Indeed the statistics on that site are obtained through analysis of the SEER database. Project Content You are to use the methods of this course to analyze your selected data set. The goal of the project is to demonstrate how much survival analysis you have learned. Projects should have the following components: • Some preliminary elementary calculations (Chapters 4, 5, and 7) • Some type of regression modeling using both a Cox proportional hazards model (Chapters 8-9) and a parametric regression model (Chapter 12). The results of these models should be compared. • Model validation using residual analysis (Chapter 11 and Section 12.5) for both the proportional hazards and parametric regression models. • Both categorical and numerical variables, as well as some interaction terms, should be considered for inclusion in the model. • An interpretative section outlining your important conclusions written in nontechnical language,e.g. “Being married decreases the risk of breast cancer by 40%” instead of “The coefficient of marital status is -0.51 and is significant” . Note log(.6) = -0.51. It would be nice if you had some significant interaction terms, both categorical*categorical and categorical*numerical, to interpret. Final Report requirement The project final report should focus on data introduction, question to answer, analysis techniques, and analysis result interpretation. The following format for the final report is suggested. The final report should be no more than 15 pages from Section 1 to Section 5, without computing codes. Computing codes should be attached as an appendix. The final report should be submitted to Canvas website in pdf format. • Section 1: Introduction to data. • Section 2: Questions to address. Formulate appropriate questions on your own that can be addressed by analyzing this dataset. • Section 3: Statistical analysis. A technical section describing in great detail what you did. Relevant portions of R/SAS output should be ‘cut and pasted’ into this section. This section should be self-contained and not require rummaging through mounds of output. In particular, I should not have to read the appendix (see below) to know what you did and to assess the correctness of your analysis. • Section 4: Interpret the analysis results and answer the question(s) in Section 2. • Section 5: Discussion on limitation of the analysis. Pros/cons of the used methods. • Appendix: codes. • If you choose Option 3, attached a copy of your SEER confidentiality agreement showing, in the upper lefthand corner, you name and number.
Assignment 3: Recurrent Neural Networks for Stock Price Prediction, Deep Learning Fundamentals, 2024 Due: 11:59pm, 07/12/2024 Abstract This assignment is to implement, describe, and test Re- current Neural Networks (RNNs) to predict stock prices in the future, using Google Stock Price dataseton Kaggle. The submission will take the form. of a conference paper. 1. Introduction You need to explain the application, and review the re- lated work here. 2. Task and dataset You are required to build a RNN to predict Google Stock Price using the Kaggle dataset available from https://www.kaggle.com/rahulsah06/gooogle-stock-price. You can use vanilla RNN, Gated Recurrent Unit (GRU), or Long Short-Term Memory (LSTM) as taught in the lecture and demonstrated in the workshop. The dataset provides the training set and testing set. But you should further split the original training set to train- ing and validation sets. Validation set is for you to tune the hype-parameters. The dataset contains open, high, low, close, volume of the stock price for each day. The task is to train a model that is able to predict future stock price. A common way in stock market prediction is to use the past N days data (e.g. N = 30, including all open, high, low, close, volume) to predict the next M days data (e.g. M = 1, 2, 3, including all open, high, low, close, volume). There are other design choices, and it is up to you how to model this problem using a RNN. Whatever choice you make, you need to justify and evaluate your choice. Make it realistic. For example, people wish to predict the next day’s price (M = 1), because they wish to predict to sell or buy. Your work will be mostly evaluated on performance, justifi- cation, analysis, insights, and usefulness (i.e. if it would be useful at all if someone wishes to use it). 3. Submission The submission takes the form of a conference paper, and your code. 3.1. Report/paper submission You need to write a paper such as might be submitted to a conference, and specifically a paper such as might be sub- mitted to CVPR[1], which is one of the best conferences in Computer Vision. The paper must be in the CVPR format, and submitted as a pdf document. By far the easiest way to achieve that is to use LATEX. LATEXis a very powerful docu- ment formatting package, it’s free, and it is the only way to generate well formatted documents that contain maths. It’s also the easiest way to generate well formatted documents in general. All the information about the CVPR paper format is available on their web site[1]. The paper must be all your own work, with no text copied from any other doc- ument. The paper you submit must be in the format speci- fied for CVPR 2020, which is specified as part of the author instructions[1]. The easiest way to achieve this is to down- load the LATEXtemplate and use that. You can use some other means if you really want to, but your paper needs to con- form to the CVPR style specification. The only exception is that I don’t mind if you use a4 paper rather than their preference for letter paper (it’s a US conference). Good paper exemplars? Go to the CVPR websites (not limited to 2020), and read the BEST PAPER AWARD pa- pers and the oral papers in the past. You may not fully un- derstand the the papers, but they should give you some idea what a look paper should look like. 3.2. Assessment The purpose of the paper is to demonstrate that you un- derstand the problem, and the solution. This means that your submission should have sections which broadly cover the following • An introduction, which describes the problem, and the method/algorithm (5 points); • A background section which describes competing ap- proaches to the problem. Achieving this requires that you understand what the competing approaches do, how they do it, their advantages and shortcomings, and how they compare to the current approach. The meth- ods you compare against here may well perform. better than the method you are describing. The idea of this section is not that you show that yours is necessarily the best method available, but rather that you show that you understand enough about the literature in the area to be able to put it in context (10 points); • A description of the method. This will typically re- quire explaining some part of the algorithm in detail, and providing examples illustrating its effects and de- ficiencies. If you propose an improvement then you should describe how your method works, in enough detail that a reasonably skilled person would be able to implement it (30 points). • Experimental Analysis. Describe the tests you have run, and your motivation for having run them. Re- port the results of the tests and the conclusions that you have drawn. Again, the goal is not to show that your method outperforms all comparators, but rather that you understand what the method aims to achieve, and can devise, execute, and report upon a set of tests which demonstrate whether it does so. If you have im- proved upon the base method then you have an oppor- tunity here to show that your improvement is well mo- tivated, and possibly even that it works (30 points); • Code. In order to be able to test the method you will need to implement it. You can implement it in Python (preferred) or Matlab. You will also need to submit your code in https://github.com, which leaves the timestamp. You can make your github code pub- licly available, OR it is important for you to give ac- cess to the teachers and tutors. The tutor will not be marking the quality of your code, only checking that it shows enough evidence that you wrote it yourself, sub- mitted in time, and no obvious error(s). Please make a separate section (Code section) in the report with the link to your github code (20 points). • Conclusion. Demonstrate that you have learned some- thing worthwhile from the process, possibly including ideas about what you might do to improve the method you are reporting on (5 points). References [1] CVPR. Ieee computer society conference on computer vision and pattern recognition. See http://cvpr2020.thecvf.com/submission/ main-conference/author-guidelines.
ENG 06 Final Project Up to now in ENG6, we have focused on teaching you the “how” of programming. In the team project you will be taking your own ideas and bringing them to fruition through your knowledge of computer programming. Software development is rarely a one-person effort, so the project will be team-based. Teams can be formed with members of any section. You can form your own team of three. No other team size will be allowed. Only if strictly needed, the TAs may form smaller teams or add members to teams. Beyond the Core part of the final project, we ask you to implement a number of special features, called Reach elements, so as to make your project different from your classmates. Important: We discourage the use of other Matlab code written by someone else, however, if you use it you must reference the code creator. Project: Card Game Project Description: Implement a MATLAB program of a card game of your team’s choice. You may create your own rules. The card game must involve more than two players. The players should be able to make strategic decisions to win the game. The program should draw cards upon request, apply the rules, and score the game. Each team makes decisions on what programming elements to use to interact with the users. Special attention should be paid to: 1. Clarity on how to play the game. 2. How the users should interact with the program. 3. The visual, auditory cues and special effects (e.g. animations, a sound clip when the cards are drawn, etc.). You are allowed to use simple pictorial representations for each card, for example a King of hearts card with a large K and a heart, etc. 4. The users should be able to play the game with each other over the internet. The implementation of the card game should involve two players at different locations using a keyboard or a mouse to play their turn. All projects should have the following elements: 1. A graphical user interface. 2. An animation. 3. A sound effect. 4. Make use of a user-defined OOP class in at least one programming element. 5. One or more tables. 6. The capability to play the game between two, or more, players remotely. 7. Clearly indicate in your code and your video where these elements are implemented. In your YouTube video, please point out how you implemented some features (especially in the Core and the Reach) inside your code. What functions did you use? Did you use any data structures such as structs etc? What was challenging about implementing a certain feature and why? Links to external resources: Controlling Random Number Generation. Play Audio Collaboration Policy: Once the teams are formed you are only allowed to talk and collaborate with members within your team. Team members are expected to equally participate, and collaboratively work towards the completion of the project. Other than contacting the teaching assistants for clarification, you may not seek the assistance of other persons to complete your team's project. (Of course, general discussions about how to implement a GUI, OOP, and other programming constructs could be discussed with other students, but your team must be totally responsible for the implementation of code used in your project). Grading Criteria: The projects are open ended. As long as your program can perform the assigned tasks, there will be no correct or incorrect approaches. Certainly there will be more acceptable and attractive solutions, and these will be judged in comparison with competing solutions submitted by your classmates. The final project will be graded in 6 parts: 1. Project proposal: Each team submits a 2-3 page project proposal via Canvas describing the project they have selected, a general description of how they will implement the main components of their project, and a clear description of the Reach features that their team proposes. Essentially, the scope of the project should be both challenging enough to merit full credit and doable within the timeline. An Appendix should contain a breakdown of programming tasks, and who will be responsible for what, along with a timeline that will meet the submission deadline (we suggest you make use of a Gantt chart.. The expectation is that each team member must take responsibility for a specific aspect of the project and grading for each member will be adjusted according to how the project tasks were delegated and who was responsible for what aspects of the project. The more specific you can be in defining the programming tasks, what functions should exist, and what each function should accomplish, the better. 2. Core: Complete the basic project as outlined in the project description. For example: Show whose turn it is to play, an image background such as a green table, an image showing the card draw outcome, the scores, etc ● Features of the GUI: ○ A button to shuffle and draw a card ○ A button to play a card. ○ Display scores edit fields ○ Anything else you need or want to add depending on the game you have chosen. 3. Reach/Special Features: Implement the project enhancements described in your proposal. Your completion of the Core and the creativity of your proposal will be taken into account during the grading process. ● Animations ○ Drawing a card ○ Play a card ○ A scoring system ○ Others ● Sound ○ Shuffling card ○ Playing a card ○ A scoring system ○ Others 4. Additional Core Requirement: Remote players and Tables with ThingSpeak: ● The GUI interface must include a table keeping a record of the rolls for each player throughout the game. ● The capability to play the game between two, or more, players remotely. 5. Youtube Video Requirements: Youtube has several examples of ENG6 videos (search ENG6). The format of the video is entirely up to your team as long as the following criteria are met: A. The maximum length of the video is 10 minutes B. Each team member must be seen in the video to present their work and contributions C. A clear and easy to follow demonstration that shows the correct functionality of your program (show your program actually working in the video – not screen shots of before and after.) D. Use visual aides to help explain your steps (whiteboard, markers, poster, etc.). The video does not have to be fancy, just effective in relaying the most important information. 6. Team Evaluations: Each member must provide a brief personal summary of her/his involvement and contributions. Each team member is required to submit evaluations of her/his teammates’ contribution, one for each of Core and Reach. For example, if your team has members A, B, C, your evaluation can be similar to the following for a single member. Team Member A: was in charge of writing the code to execute the equalizer filters. For the Reach, A was in charge of adding 2 different analysis plots that could show power spectral density plot and frequency content of audio file. Team Members B, C agree that A performed these tasks for the project. Project Deadlines: Deadline #1: TBA: Submit the Project Proposal: A team member must submit the project proposal. The proposal needs to include project name and names of all team members at the beginning. Only one team member should do this! The submission should also contain an image of the design view with the main components of your game. The image must show the buttons, axes, images, edit fields, etc. Deadline #2: TBA: Submit the Core. Each team will submit all relevant code files. Each team will submit a zip file of all the code, the zip file will have the mlapp. and all .m files, all .img files and any other files that are needed for the game to run. The submitted .mlapp GUI must allow the game to be played by one or two players using the same keyboard. Note: The UI components for the remote player locations and tables can be avoided in this stage. Deadline #3: TBA: Submit the Final Project. The final project must include the Additional Core Requirements and the Reach/Special features: Implement tables, remote locations and the project extensions described in your proposal. Each team will submit all relevant coding files, a link to Youtube video and team evaluation materials. Each team will submit a zip file of all the code, the zip file will have all .mlapp files, all .img files and any other files that are needed for the game to run. In addition, it should contain a PDF of the team evaluation document. The link of the Youtube should be accessible to all those who use the link. Figure 1: Example of a design view: Crazy Eights card game.
Fall 2024 Project Rules ACTSC 445/845: Quantitative Risk Management Purpose The purpose of this project is for you to analyze a topic related to quantitative risk management. It can be the description of theoretical concepts with applications in QRM (for instance, quantile estimation which can be applied to VaR estimation; risk measures beyond the ones we saw; going into proofs of EVT that we skipped), a data analysis related to QRM (for instance, applying different methods going beyond the ones in the course to a dataset from the realm of finance and insurance), the description of a QRM problem along with a simulation study (e.g., change dependency in a credit risk model and compare resulting risk) or something in between Ultimately, you should pick the topic that you are most interested in, which has applications to QRM, and has a quantitative aspect (not just a qualitative description of, for instance, the three pillar concept). A list of ideas is at the end of the document. Project Proposal To ensure that the topic chosen is appropriate, every group (consisting of one or two students) will submit a project proposal. The proposal should consist of the following The title of the project. The name(s) and student ID(s) of the group member(s). A 0.5 - 1 page summary of what you intend to work on in the project. This should include a brief description of the references, the methods used, the dataset used (if applicable) and the goal of your project. It should also include a reasoning as to why the chosen topic is relevant for QRM. The project proposal will be due on November 20, 11:59PM via Crowdmark, but you are more then welcome to submit much earlier. If you are unsure if the topic chosen is appropriate or if you need help finding a topic, please do not hesitate to contact me earlier! Project Paper Students can work on the project either individually or in groups of up to two students. In the former case, the project should have at least 7 pages, in the latter, at least 10 pages (+ Appendix, if applicable). Any paper should not be much longer than 20 pages, unless instructor approval was given. If a data analysis or simulation study is performed, reproducible and well documented code should be included in the appendix. The project paper should be a stand-alone paper: If I read it, I should understand the main idea without having to look at references. As a guideline: Your peers having taken this course should be able to understand it. The project paper should be well written (complete, grammatically correct and coherent), well formatted (no screenshoted formulas, for instance) and well researched (proper citations, critical thought). All three aspects influence the final grade. There should be an introduction, a main part, a conclusion, and an appendix (if applicable, can include code, additional graphics or tables,...). You may use any R package you find, so long as you cite it. The project paper will be due on December 9, 11:59PM via Crowdmark. Only one group member needs to upload their project paper, so long as the other group member has been added to the Crowdmark page. Topic ideas The following shall just give ideas for the project topic. The list of references is also anything but exhaustive: Google scholar is your friend! a) Theoretical topics and proofs. In the course, we have skipped a number of proofs and technicalities. A project could be to explain the theory and math behind such a concept. Ideas include The generalized inverse (aka quantile function) extends the usual notion of an inverse of a bijective function. There are similarities and differences. Furthermore, the quantile function plays a key role in simulation. This is discussed in Embrechts and Hofert (2013), for instance. Extreme value theory. An excellent introduction to EVT is Embrechts, Klüppelberg, et al. (1999). Project ideas include giving details on proofs we have skipped or introducing aspects of EVT not discussed in the course, such as EVT for multivariate models or EVT in financial time series. Coherent risk measures. We have motivated the four axioms of coherence, and proved that expected shortfall is coherent. There’s so much more! For instance, one can show that any coherent risk measure can be written as a generalized scenario. Or one can try to “improve” value-at-risk to repair subadditivity. An excellent reference is Artzner et al. (1999). Copulas and multivariate distributions. There will be a number of theorems we won’t have the time to prove in the lectures! b) Financial Time Series, see Taylor (2008). We have only slightly touched the topic of financial time series, so there are plenty of topic ideas here: Introduction to ARMA and GARCH processes. Multivariate Financial Time series. Copula modelling in Financial Time Series. For instance, you might define ARMA and GARCH processes, illustrate how they work using a simulation study and explain how to estimate parameters. Of course, you do not need to code everything by yourself - there’s a number of R packages! c) Non-market Risk Management. This course was mainly focussing on market risk management, but there are important others: Operational risk, credit risk and more. An introduction and good references can be found, for instance, in McNeil et al. (2015). For credit risk models, Crouhy et al. (2000) give a comparative study; Carey and Hrycay (2001) use ratings to estimate default probabilities; Byström (2019) look at the future of risk management in light of technological advances, such as blockchain. Regarding operational risk management, see Pakhchanyan (2016) for a literature review or Cornalba and Giudici (2004) for some statistical models. d) Machine Learning. Generalized linear models, and more generally machine learning techniques, are used in non-life insurance pricing (eg, car insurance). There are plenty of project topics, such as an introduction to GLM and an application to an insurance data-set which demonstrates how to price such insurance contract, see, e.g., Ohlsson and Johansson (2010). For a credit risk application, see Galindo and Tamayo (2000). e) Estimation methods. A model in QRM is only useful if it can reasonably well fitted to data. Parameter estimation for multivariate distributions often comes with numerical challenges; see, e.g., Erik Hintz et al. (2022), E. Hintz et al. (2021), Protassov (2004), Luo and Shevchenko (2010), Nadarajah and Kotz (2008). A project topic could be to pick a multivariate distribution and discuss estimation methods, ideally supported with a data analysis or simulation study. f) Simulation studies. Many problems require Monte Carlo simulation to estimate risk measures, such as in Gaussian or t copula credit risk as in Glasserman and Li (2005). Project ideas include explaining these models (how do they model the loss? What is their X, its distribution and f?) and doing a simulation study (how do the answers change if I change a certain parameter and does this make sense? What happens if I change the dependency?). We have seen the Gaussian credit portfolio in the tutorial. Useful references for possible projects include Salmon (2012), Salmon (2009), Furman et al. (2016). g) Introducing multivariate distributions. We will get to know a number of classes of multivariate distributions (in particular, normal variance mixtures and elliptical distributions). But there are more complicated multivariate models that can be used in QRM, see, e.g., Weibel et al. (2020). A project topic could be to introduce a multivariate model not discussed in the course, motivate why it would be a useful model and perform a simulation study. References Artzner, P., Delbaen, F., Eber, J., and Heath, D. (1999), Coherent Measures of Risk, Mathematical Finance, 9(3), 203–228. Byström, Hans (2019), Blockchains, real-time accounting, and the future of credit risk modeling, Ledger, 4. Carey, Mark and Hrycay, Mark (2001), Parameterizing credit risk models with rating data, Journal of banking & finance 25(1), 197–270. Cornalba, Chiara and Giudici, Paolo (2004), Statistical models for operational risk management, Physica A: Statistical 338(1-2), 166–172. Crouhy, Michel, Galai, Dan, and Mark, Robert (2000), A comparative analysis of current credit risk models, Journal of Banking & Finance, 24(1-2), 59–117. Embrechts, P. and Hofert, M. (2013), A note on generalized inverses, Mathematical Methods of Operations Research, 77(3), 423–432, doi:10.1007/s00186-013-0436-7. Embrechts, P., Klüppelberg, C., and Mikosch, T. (1999), Modelling extremal events, British actuarial journal, 5(2), 465–465. Furman, Edward, Kuznetsov, Alexey, Su, Jianxi, and Zitikis, Ričardas (2016), Tail dependence of the Gaussian copula revisited, Insurance: Mathematics and Economics, 69, 97–103. Galindo, Jorge and Tamayo, Pablo (2000), Credit risk assessment using statistical and machine learning: basic methodology and risk modeling applications, Computational economics, 15(1), 107–143. Glasserman, P. and Li, J. (2005), Importance Sampling for Portfolio Credit Risk, Management Science, 51(11), 1643–1656. Hintz, E., Hofert, M., and Lemieux, C. (2021), Normal variance mixtures: Distribution, density and parameter estimation, Computational Statistics and Data Analysis, 157C, 107175, doi:10.1016/j. csda.2021.107175. Hintz, Erik, Hofert, Marius, and Lemieux, Christiane (2022), Computational Challenges of t and Related Copulas, Journal of Data Science, 20(1), 95–110, issn: 1680-743X, doi:10.6339/22-JDS1034. Luo, X. and Shevchenko, P. (2010), The t copula with multiple parameters of degrees of freedom: bivariate characteristics and application to risk management, Quantitative Finance, 10(9), 1039–1054, doi:10.1080/14697680903085544. McNeil, A., Frey, R., and Embrechts, P. (2015), Quantitative Risk Management: Concepts, Techniques and Tools, Princeton University Press, doi:10.1007/s10687-017-0286-4. Nadarajah, S. and Kotz, S. (2008), Estimation methods for the multivariate t distribution, Acta Applicandae Mathemati 102(1), 99–118. Ohlsson, E. and Johansson, B. (2010), Non-life insurance pricing with generalized linear models, vol. 2, Springer. Pakhchanyan, Suren (2016), Operational risk management in financial institutions: A literature review, International Journal of financial studies, 4(4), 20. Protassov, R. (2004), EM-based maximum likelihood parameter estimation for multivariate generalized hyperbolic distributions with fixed λ , Statistics and Computing, 14(1), 67–77, doi:10.1023/B:STCO. 0000009419.12588.da. Salmon, Felix (2009), Recipe for disaster: the formula that killed Wall Street, Wired Magazine, 17(3), 17–03. Salmon, Felix (2012), The formula that killed Wall Street, Significance, 9(1), 16–20. Taylor, Stephen J (2008), Modelling financial time series, world scientific. Weibel, M., Luethi, D., and Breymann, W. (2020), ghyp: Generalized Hyperbolic Distributions and Its Special Cases, R package version 1.6.1, http://CRAN.R-project.org/package=ghyp.
Assignment Remit Programme Title Department of Economics Module Title LM Economics of Financial Markets and Institutions Module Code 07 40063 Assignment Title Individual Project Level LM-Masters Level Weighting 25% Hand Out Date 14/10/2024 Deadline Date & Time 09/12/2024 12pm Feedback Post Date 16th working day after the deadline date Assignment Format Report Assignment Length 750 words Submission Format Online Individual Module Learning Outcomes: This assignment is designed to assess the following module learning outcomes. Your submission will be marked using the Grading Criteria given in the section below. • Portfolio Analysis and Optimization: Students will be able to perform portfolio optimization using Stata, demonstrating proficiency in the practical application of financial econometrics to construct portfolios that meet specific risk-return criteria, including the Global Minimum Variance Portfolio and the Optimal Risky Portfolio. • Critical Evaluation of Financial Models: Develop the ability to critically analyse and interpret the results from financial models such as the CAPM, assessing their implications in the real-world setting of financial markets and institutions. • Data Analysis Skills: Gain hands-on experience in handling real-world data by downloading, analysing, and interpreting financial data from sources like Yahoo Finance, using tools to compute descriptive statistics, correlations, and regression analyses pertinent to financial markets. • Communication of Financial Analysis: Enhance the ability to clearly articulate financial analysis and recommendations through structured reporting, including the proper presentation of statistical data, graphs, and investment strategies in a professional report format. Project Report 1 Introduction ● Your report should show the optimal allocation of assets (risk-free and risky assets) based on the optimizations. ● Choose five companies of your choice, making up your portfolio. ● This is an individual project so I expect that you work independently. Please do not choose the same portfolios or report very similar comments; any cooperative work will be penalised. ● You must upload TWO files on Canvas: 1. Your report in Word or PDF format. 2. The Stata do file so that I can replicate your results. ● The report should not exceed 750 words; tables and graphs are not included in the word count. Please include the word count at the top of your document. A penalty of 5 ● The report counts for 25% of the final mark. ● The deadline for submitting the project is clearly indicated on Canvas and on the remit. 2 Report Organization ● You should analyze your data using Stata. During Week 6, which is the assessment support week, the lecture will focus on preparing for the project. A ”do file” containing necessary commands, along with a recording of the Week 6 lecture that explains how to execute these commands, will be available on Canvas in the ’Project’ section. ● The report that you submit should be organized as a literate response to the questions, divided in paragraphs that can be understood by someone who didn’t just read the questions. ● Include your basic numerical results and graphs in your paragraphs along with the ap- propriate analysis and interpretation of them. ● Providing solely the calculations is NOT acceptable. A discussion of your findings, comparisons of the results, possible explanations for any differences found, and finally your recommendations for an investor wanting to hold this portfolio are essential. ● Please edit tables and graphs from Stata before inserting them in the document. ● Tables and graphs should be numbered, have a meaningful title, and an explanatory note at the bottom. ● The significance level of the coefficients must be indicated with an asterisk next to the coefficient, according to the significance level: * 10%, ** 5%, *** 1%. 3 Points to Discuss in the Report 1. Download monthly prices, from January 2014 through December 2023, on the market as a whole and on five individual stocks (for different industries) of your choice from Yahoo Finance. Briefly describe the stocks that you have selected. 2. Graph the time series of the prices. 3. Compute the returns using the closing prices: rt = ln (Pt−1/Pt) × 100 4. Compute descriptive statistics (mean, standard deviation, maximum, and minimum) of the returns and report them in a table. 5. Look at the correlation and report the results in a table with the significance levels. 6. Get the frequency histograms of your returns. 7. Estimate and plot the linear relationship between each of your assets’ returns and the market returns. 8. Estimate the CAPM (reporting the results in a table): E(˜(r)j ) = rf + βj [E(˜(r)M ) − rf ] The annual risk-free rate is 2.4%. 9. Compute the following portfolios and report them in a table (one portfolio per column) indicating the weights, the expected return of the portfolio, the standard deviation of the portfolio, and the Sharpe ratio: ● The Global Minimum Variance Portfolio (GMVP), i.e., the portfolio that lies to the far left of the efficient frontier and is made up of a portfolio of risky assets that produces the minimum risk for an investor. ● Compute four portfolios: three choosing appropriate increments of the required re- turn above the GMVP and one with the maximum return. ● The optimal risky portfolio, i.e., the one at tangency between the efficient frontier and the capital market line. 10. Plot the efficient frontier on a return-risk diagram for a long-only constraint, not for long-short (where short selling is permitted). 11. Plot the optimal risky portfolio tangent to the capital market line. Do so for both a long-only constraint .
Assessment Proforma 2024-25 Key Information Module Code CMT310 Module Title Developing Secure Systems and Applications Assessment Title Technical Report Assessment Number 1 Assessment Weighting 50% Assessment Limits This individual assessment consists of THREE tasks to be completed and prepare a final report for the submission on Learning Central. It should be a single report of 2,000 words (maximum, including all except references). There should not be any appendix attached or included in this report. The Assessment Calendar can be found under ‘Assessment & Feedback’ in the COMSC- ORG-SCHOOL organisation on Learning Central. This is the single point of truth for (a) the hand out date and time, (b) the hand in date and time, and (c) the feedback return date for all assessments. Learning Outcomes The learning outcomes for this assessment are as follows: This individual assignment contributes to the assessment of the following Learning Outcomes (LO) 1, 2, 3, 4, 5 and 6 of the unit: 1. Compare and contrast common technical security controls available to prevent, detect and recover from security incidents and to mitigate risk. [T2] 2. Articulate security architectures relating to business needs and commercial product development that can be realised using available tools, products, standards and protocols. [T1, T3] 3. Deliver systems assured to have met their security profile using accepted methods and development processes. [T2] 4. Critically analyse the correctness and properties of secure systems. [T1] 5. Justify the selection of different cryptosystems. [T2] 6. Critically analyse recent cyber security case studies. [T1, T2] Submission Instructions The coversheet can be found under ‘Assessment & Feedback’ in the COMSC-ORG- SCHOOL organisation on Learning Central. All files should be submitted via Learning Central. The submission page can be found under ‘Assessment & Feedback’ in the CMT310 module on Learning Central. Your submission should consist of multiple files: Description Type Name Coversheet Compulsory One PDF (.pdf) file Coversheet.pdf Report Compulsory One PDF (.pdf) or Word file (.doc or .docx) CMT310_[student number].pdf/doc/docx If you are unable to submit your work due to technical difficulties, please submit your work via e-mail to [email protected] notify the module leader. Assessment Description SCENARIO There has been a major incident for the company ACME.LTD. Their main business is a mixture of manufacturing and distribution management for other organizations. ACME.LTD has the following network infrastructure. The following services are running within the network: ● Windows Active Directory ● DHCP ● DNS Servers ● Mail Server (running SMTP & POP3) ● OpenVPN ● MsSQL Databases ● Multiple Samba Servers ● Web Servers The ACME.LTD's databases were compromised via an internal web server. It was accessible via a lost laptop. The lost laptop only required a username and password to access it. However, the password was at least 16 characters long, SecureBoot and full disk encryption were not in use. All workstations and laptops in use are not part of the Windows Active Directory domain. This means all accounts used are local accounts. Additionally, the attackers were able to use the access to the MsSQL services to pivot to the companies OT network and deploy ransomware. This resulted in the complete shutdown of the operations that relied upon the OT systems that had been Windows-based. INSTRUCTIONS This individual assessment consists of THREE tasks as mentioned below. Please carefully consider completing all tasks and prepare your final report. You are expected to submit this report on Learning Central which requires coursework submission as a single report of 2,000 words (maximum, including all except references). There should not be any appendix attached or included in this report. The expected font size is 12 and the font type is ‘Arial’ on all pages. There is no need to add a cover page with your submission but write your student number and name on the top of the first page of the report. You’re expected to back your answers with citations. Note, there is no ±10% word count criteria for this coursework. It is expected that your report (excluding references) must be within the 2,000 words count. Anything written beyond the first 2,000 words would be ignored during marking. Indicative word count against each task is mentioned. However, this is not a strict limit for each task, rather this should be used as a baseline for the expected amount of text/explanation against the maximum marks assigned for each task. Task 1 [T1]: The CEO and CISO of ACME.TLD would like you to review their network architecture as previously presented and identify security issues, potential risks, and insecure properties. [Indicative word count: 500] Task 2 [T2]: Provide recommendations with evidence of the best practices and applicable approaches to secure their network. Also, provide reflect on (i) what could be easily prevented and how, (ii) if not prevented, what could be detected and how, and (iii) if not prevented and detected, what could be recovered from security incidents and how. [Indicative word count: 1,000] Task 3 [T3]: The CEO and CISO are keen to also know what additional technology and tooling could also be used to help future proof the system and justify your choices. [Indicative word count: 500] References References are not counted in the word limit. Use the IEEE format references: https://ieee- dataport.org/sites/default/files/analysis/27/IEEE%20Citation%20Guidelines.pdf. This point will be further discussed in one of the lectures of the module. and-referencing/citing-and-referencing-support HELPING NOTES • Vulnerability: A weakness in any aspect of a system that makes an exploit possible. • Threat: A potential cause of an unwanted incident that may result in harm to a system. • Attack: An attempt to destroy, expose, alter, disable, steal or gain unauthorized access to or make unauthorized use of an asset. • Risk: An intersection of assets, threats and vulnerabilities. • System or system model: A system that attackers target for attacks. • Network Architecture: It is defined as the physical and logical design of the software, hardware, protocols, and media of the transmission of data. • Security Architecture: The NCSC define security architecture as ‘The practice of designing computer systems to achieve security goals. ’ These security goals are to make initial compromise of the system difficult, limit the impact of any compromise, make disruption of the system difficult, and make detection of a compromise easy. Security architecture must consider all the technology, people and processes relating to a computer system. • Best Practices: These are a standard or set of guidelines that is known to produce good outcomes if followed. • Useful article for help: How to Prevent, Detect, and Respond to Cybersecurity Incidents, https://www.eidebailly.com/insights/articles/2020/5/how-to-prevent-detect-and-respond- to-cybersecurity-incidents Assessment Criteria Task 1 Reviewing network architecture (Available Marks - 15) High Distinction 80%+ Critically analysed security issues with potential risks and their impact; listed and defined security vulnerabilities and threats with rationale and with specific technical details; identified and critically reflected on insecure properties with valid reasons; excellent demonstration of critical thinking, depth analysis, logical arguments, and citations used. Distinction 70-79% Critically analysed security issues with potential risks; listed and defined security vulnerabilities and threats with rationale and with specific technical details; identified and critically reflected on insecure properties; Very good demonstration of critical thinking, depth analysis, logical arguments, and citations used. Merit 60-69% Clearly analysed security issues with potential risks; listed and defined associated security vulnerabilities and threats; clearly identified and reflected on insecure properties; good demonstration of critical thinking, depth analysis, logical arguments, and citations used. Pass 50-59% Some narration on security issues with potential risks; partially explained insecure properties; reasonable demonstration of critical thinking, depth analysis, logical arguments, and citations used. Marginal Fail 40-49% Not sufficiently narrated security issues with potential risks; not adequately explained insecure properties; poor demonstration of critical thinking, depth analysis, logical arguments, and citations used. Fail 0-39% Not sufficiently narrated security issues; not explained insecure properties; very poor demonstration of critical thinking, depth analysis, logical arguments, and citations are not used. Task 2 Provide recommendations and approaches (Available Marks - 20) High Distinction 80%+ Excellent reflection on possible recommendations; appropriate and suitable use of strong and secure security approaches; excellent demonstration of critical thinking, and logical arguments; excellent - quality and useful citations/references Distinction 70-79% Very good reflection on possible recommendations; appropriate use of secure security approaches; very good demonstration of critical thinking, and logical arguments; very good and useful citations/references Merit 60-69% Clearly reflected on possible recommendations; adequate use of suitable strong/secure security approaches; good demonstration of critical thinking, and logical arguments; good citations/references Pass 50-59% Some reflection on recommendations; Partial use of suitable strong/secure security approaches; reasonable demonstration of critical thinking, and logical arguments; some citations/references Marginal Fail 40-49% Not adequate reflection on recommendations; Not use of suitable security approaches; not sufficient demonstration of critical thinking, and logical arguments; limited citations Fail 0-39% No/limited reflection on recommendations; No use of security approaches; no/limited demonstration of critical thinking, and logical arguments; no citations Task 3 Additional technology and tooling (Available Marks - 15) High Distinction 80%+ Shown excellency in understanding and presented correct logical arguments; excellent reflection on employing correct and suitable technology and tools; excellent reflection on future proof of the system with valid arguments; excellent demonstration of critical thinking, logical arguments, and quality and suitable citations used Distinction 70-79% Shown very good understanding and presented correct logical arguments; great reflection on employing correct technology and tools; very good reflection on future proof of the system; great demonstration of critical thinking, logical arguments, and very good citations used Merit 60-69% Shown competency in understanding and presented correct logical arguments; good reflection on employing correct technology and tools; good and sufficient reflection on future proof of the system; good demonstration of critical thinking, logical arguments, and good citations used Pass 50-59% Logical arguments with some errors, or invalid statements; some reflection on appropriate technology and/or tooling to be used; Some/partial reflection on future proof of the system; reasonable demonstration of critical thinking, logical arguments, and some citations used Marginal Fail 40-49% Many factual or technical errors in arguments; inappropriate technology and/or tooling mentioned; Insufficient reflection on future proof of the system; poor demonstration of critical thinking, logical arguments; no/limited citations used Fail 0-39% Many factual or technical errors in arguments; inappropriate technology and/or tooling mentioned; very limited reflection on future proof of the system; very poor demonstration of critical thinking, logical arguments; no citations used
Assessment Brief 2024/2025 Assignment Information Course Code ACCFIN5229_1A Course Title Advances in Machine Learning in Finance Weighting 50% Question release date Submission date: 19th Dec 2024 Grades and Feedback to be released on: Word limit 2000 words (+/- 10%) Refer toword limit policy Action to be taken if word limit is exceeded Use academic judgement to adjust the grade to reflect failure to adhere to the work limit 1. QUESTION/ DESCRIPTION OF ACTIVITY This is an individual essay (50%). The topic of the assignment is: “Data snooping becomes a considerable concern when exploiting an extensive dataset whose number of variables is larger than the number of observations. This issue results in false discoveries, especially when classical statistical inference is used in which investors repeatedly test the same single hypothesis without adapting a rejection region.’’ Required: Critically discuss the above statement taking into account Multiple Hypothesis frameworks available in the literature. Student guidelines: You will need to plan your answer carefully to provide a focused and succinct essay whatever your approach and reasoning ability within the word-limit. You should also demonstrate your ability to explain and apply the concepts, theories or models and to justify any conclusion you may reach based on evidence provided by all relevant course materials or any other properly referenced source you may choose to use. The assignment answers should be written in an academic and logical manner, not a journalistic style. The assignment refers to the related lecture in Multiple Hypothesis Testing and focuses on data-snooping and the frameworks available to address the relevant statistical bias. You should research MH testing in finance and critically assess the importance of data-snooping test. Evidence of wider reading and critical thinking are strongly encouraged. The word limit is 2000 words. You should not exceed it. 2. ADDITIONAL INFORMATION FOR GROUP ASSIGNMENTS Not relevant for this assignment 3. ASSESSMENT RUBRIC/ CRITERIA Overall, successful individual essays should: • Provide a clear, well-structured and well-written essay according to the academic expectations of the School and based on the required task. • Demonstrate the student’s ability to analyse, elaborate and critically discuss the relevant literature and provide meaningful and coherent arguments regarding the MHT framework and its utility and importance in financial applications • Demonstrate deep knowledge on how MHT evolves around methods such as FWER and FDR and how their application have transformed financial research in machine learning. A holistic rubric provides a list of assessment criteria together with broad description of the characteristics that would be expected for each level of performance.
Coursework 3In this coursework, you are required to write several Python functions, by filling in code into thetemplate below. Submit your Python script via upload on Blackboard before the deadline onFriday, 13th December 2024 at 1pm.I suggest aiming to submit at least one hour earlier, to avoid any last-minute computer problems.Working independentlyAs with all assessed work in this unit, you must complete this coursework independently onyour own. You must not take code from elsewhere, even if it is referenced. You are howeverallowed to use the lecture notes and exercise solutions freely, and the lecture notes contain allthat is required to solve the problems. You are not allowed to ask others for help, and inparticular, you are not allowed to send, give, or receive Python code to/from classmates andothers.See the University Guidelines for Academic Malpractice.Instructions1. Copy the code out of the template below and save it as a .py fi le in Spyder (or anotherPython editor of your choice).2. Add your name, university ID number and university email address in thedocstring at top of the file, where indicated3. Write code to replace the pass statements in the functions initial_state,parse_move, validate_move, apply_move, game_won for questions 1 C5. Forquestions 1 C5, do not alter the existing play_game function.4. For questions 6 C8, modify only the parse_move and play_game functions as requested.5. Do not alter the names or parameters of any functions. Do not create any furtherfunctions with the same names. Ensure that these functions can be called with theparameters precisely as specifi ed, and that they return (not print) values of the correctdata types. Coursework 3_un 1/166. Submit your work on Blackboard as a .py Python file. Do not submit a screenshot,PDF, Word document, iPython notebook, or file in any other format.Aspects of this coursework will be marked automatically and failure to follow theproblem description or any of the instructions above will result in loss of marks.To check that your code is working correctly, you may wish to add some of your own testsin a new function C these will not be marked.If you wish, you can also add other functions to the code, and call them from the requiredfunctions. Make sure all code (other than module import statements) is within a function.Do not create any further functions with the same name as any of the required functions.Do not use the input function anywhere in your code. The only use of input allowed ismy existing line of code in the play_game function.Make sure your functions return the value they should return, not just print the valueto screen.As this is part of the course assessment, we will not be able to help you solving theseproblems. If you are unsure what any question is asking for, please ask on the BlackboardDiscussion Forum.If you provided a valid email address in the docstring at the beginning of your .py fi le, youwill receive a feedback report with your score for each function.Template codeThe template that you should use for your coursework is below. Two functions are provided:display_state is a function that displays the state of the game on the screen, usingfancy colours!play_game is a pre-written basic framework for the game. You must not modify thisfunction for questions 1 C5, but it is modifi ed in questions 6 C8."""MATH20621 - Coursework 3Student name: add your nameStudent id: add your id numberStudent mail: [email protected]""" Coursework 3_un 2/16def display_state(s, *, clear=False):"""Display the state sIf 'clear' is set to True, erase previous displayed state"""def colored(r, g, b, text):rb,gb,bb=(r+2)/3,(g+2)/3,(b+2)/3rd,gd,bd=(r)/1.5,(g)/1.5,(b)/1.5return f"33[38;2;{int(rb*255)};{int(gb*255)};{int(bdef inverse(text):rb,gb,bb=.2,.2,.2rd,gd,bd=.8,.8,.8return f"33[38;2;{int(rb*255)};{int(gb*255)};{int(bcolours = [(1.0, 0.349, 0.369),(1.0, 0.573, 0.298),(1.0, 0.792, 0.227),(0.773, 0.792, 0.188),(0.541, 0.788, 0.149),(0.322, 0.651, 0.459),(0.098, 0.509, 0.769),(0.259, 0.404, 0.675),(0.416, 0.298, 0.576)]n_columns = len(s['stacks'])if clear:print(chr(27) + "[2J")print(' ')row = 0numrows = max(len(stack) for stack in s['stacks'])for row in range(numrows-1,-1,-1):for i in range(n_columns):num_in_col = len(s['stacks'][i])if num_in_col > row:val = s['stacks'][i][row]if num_in_col == row+1 and s['blocked'][i]:print(inverse(' '+str(val)+' '),end=' ')else:if s['complete'][i]:print(colored(*colours[val-1],' '),else:print(colored(*colours[val-1],' '+strelse:print(' ',end='') Coursework 3_un 3/16print()print(' A B C D E F')# Q1def initial_state():pass# Q2def parse_move(input_str):pass# Q3def validate_move(state, move):pass# Q4def apply_move(state, move):pass# Q5def game_won(state):pass# For questions 1-5, DO NOT edit the play_game function.# For the tasks in questions 1-5 initial_state, parse_move,# validate_move, apply_move, and game_won must work with the# the unmodified play_game function.# For questions 6, 7 and 8, you should modify the play_game# functiondef play_game():# When we start the game,board = initial_state()try:while True:# Display the current game statedisplay_state(board, clear=False)# Read input from the user.# (Do not alter this line, even in questions 6, 7move_str = input()# Parse the text typed by the user and convert itmove = parse_move(move_str) Coursework 3_un 4/16# If the move was valid...if validate_move(board, move):apply_move(board, move) # ... alter the board# If we've won, end the gameif game_won(board):breakexcept KeyboardInterrupt: # If the user presses Ctrl-C, qpassplay_game()SolitaireThis coursework implements a game of solitaire.Solitaire, or Patience, is a name given to a single-player game, usually a game played with a packof cards. The game originated in Europe in around 1800, and numerous variations have beendeveloped since.During this coursework, you will write Python program to simulate a particular variation ofsolitaire.The game works as follows. When played as a tabletop game, we start with a standard 52-carddeck, but only the numerical cards 1, 2, 3, 4, 5, 6, 7, 8, 9 are dealt C the court cards (King, QueenJack) are omitted. The suit of the cards (?, ?, ?, ?) plays no role in the game. As a result, thereare 36 cards in play, four copies of each of the numbers 1 C9. We ll refer to the ace as 1, ratherthan A.These cards are shuffl ed and dealt into six stacks, which we ll name A through F, each containingsix cards, for example: Coursework 3_un 5/16A B C D E FThe aim of the game is to rearrange these cards into four complete stacks of nine ordered cards.Here, ordered means in decreasing order going down the stack, i.e.A B C D E FA B C D E FThis is one of several moves we could have made: we could alternatively have moved the 2 instack C onto the 3 in stack A, the 3 in stack A onto the 4 in stack B, or the 7 in stack D onto the8 of stack F.Moving multiple cardsWe can move more than one of the top cards on a stack at once, so long as those cards areordered. For example, after our first move there is a pair of ordered cards at the top of stack C:A B C D E FJust as before, we can move this pair to another stack if the top number in the destination stackis one larger than the bottom card of those to be moved. In our game, the only stack that satisfi esthis is stack A, and we can move two cards from stack C to stack A: Coursework 3_un 7/16A B C D E FYou may have noticed that there is another pair of ordered cards, an 8 and a 9, at the top of stackF. We cannot move this pair currently, as there is nowhere for it to go. The rule for moving a 9card (or multiple cards with 9 the largest) is that a 9 can only move into an empty stack.We can however move the new stack of 1, 2, 3 formed on stack A onto stack B:A B C D E FOur available moves are now a bit limited. While we can move the single 8 on stack F onto stackE, that doesn t really help us progress, as it only reveals a 9. Perhaps the best move is to move the7 on stack D onto stack A: Coursework 3_un 8/162A B C D E FAt this point, there are still moves we can make, but it s diffi cult to see how we can make enoughcards to completely re-order them into four complete stacks as required. However, we areallowed to break the rules established above, in a very specifi c way.Blocking movesA single card at the top of a stack can be moved onto another stack, even if it is not one less than thenumber at the top of the destination stack. However, in that case, the moved card blocks thedestination stack, here indicated with a black card. No further cards can be added to thedestination stack. The only way to unblock the stack is to move the single blocking card at thetop of the stack onto a valid location C that is, onto an empty stack, or onto a stack whose topcard is one greater than the card being moved. So, we can move the 1 from stack C to stack E.A B C D E FThis blocks stack E, really opens things up. We move the 3 from stack D to stack C, then the 2from stack D to stack C. Then, we can then move the blocking 1 from stack E onto the 2 on thetop of stack C, thus unblocking stack E, as follows: Coursework 3_un 9/161A B C D E FWe ll now take a succession of steps:Move the top four cards from stack B (1, 2, 3, 4) onto stack DMove the top five cards from stack D (1, 2, 3, 4, 5) onto stack BMove the top six cards from stack B (1, 2, 3, 4, 5, 6) onto stack AMove the top card from stack D, now a 1, onto stack F. This blocks stack F.Move the top card from stack D, now a 8, onto stack E.Our stacks now look likeA B C D E FMaking a complete stack Coursework 3_u 10/16We ve now got a complete run of numbers of 1, 2, 3, 4, 5, 6, 7, 8, 9 at the top of stack A. Movingthese to the empty stack D gives us the first complete stack that we are looking for. Here thismade by moving all nine cards to an empty stack all at once, but a complete stack can be formedover more than one move too.For example, we could have made the complete stack by first moving the top two cards of stack E (9 and8) to stack D, then the top seven cards of stack A to stack D.A B C D E FEither way, once a complete stack is formed, the cards on it are turned over, and no cards canadded to or removed from that stack for the rest of the game. (This is indicated here by thenumbers begin removed from the cards.)We now continue the game in the same way, except without being able to use stack D. Can yousee how in seven further moves, we could both complete a second stack and both unblock stackF?In terms of strategy, it is not always best to complete stacks as soon as possible, as this prevents that stackfrom being used again, constraining what is possible in future moves.Python implementationThe game state Coursework 3_u 11/16The most important part of this game is the representation of the game state that is, whichcards are in which stacks, which stacks are blocked, and which are complete.We represent the board state with a Python dictionary, (say, s), with three keys:1. s['blocked'] is a list of six boolean values, corresponding to the six columns A CF.The value is True if the column is blocked by a card, or False if it is not.2. s['complete'] is a list of six boolean values, corresponding to the six columns A CF.The value is True if the column is completed, or False if it is not.3. s['stacks'] is a list of six lists of integers, corresponding to the cards in each stack.The lists are ordered from bottom up, so for example s['stacks'][1][0] is the bottomcard on stack B.So, for example, the game state1A B C D E Fcould be created in Python with the statement:s = { 'blocked': [False, False, False, False, False, True],'complete': [False, False, False, True, False, False],'stacks': [[2, 7, 4],[1, 6, 5, 3],[7, 8, 9, 4, 3, 2, 1],[9, 8, 7, 6, 5, 4, 3, 2, 1],[5, 7, 6, 4, 9, 8],[1, 3, 5, 2, 9, 8, 1]]}For any one column, blocked and complete should never both be True. An empty stack isindicated by the corresponding element of s['stacks'] being an empty list []. Note that eventhough the cards in the completed stack are not shown, the representation in 'stacks' shows Coursework 3_u 12/16the nine cards in order. Thus, at all times, the lists in 'stacks' should together contain 36elements, four of each of the digits.Describing a moveThe other key part of the game is the idea of a move. A move is uniquely described by the sourcestack, the destination stack, and the number of cards to be moved. We ll represent a move as atuple of three integers,m = (source_stack, destination_stack, number_of_cards)The stacks are numbered from the left, starting with zero, so stack A is 0, stack B is 1 and so on.For example, the move (0, 4, 3) represents a move of three cards from stack A onto stack E,while a move (1, 2, 1) represents a move of one card from stack B onto stack C.The assessed questions:This coursework is worth 70 marks, and 70% of the course overall.30 marks are allocated for the correctness of the functions defi ned in problems 1 C5. Thesewill be tested in an automated way, as in the previous courseworks.15 additional marks are allocated based on number of perfect scores for core problems 1 C5.15 marks are allocated for problems 6 C8.10 marks are for the quality of all the code.Question 1: The initial state (6 marks)Write a function initial_state that returns a dict corresponding to the game state at thestart of a new game. The cards should be randomly shuffl ed, allocated six to each stack, and nostack should be marked complete or blocked.Question 2: Interpreting user input (6 marks) Coursework 3_u 13/16At each turn, the user is asked for their input. The user should enter a string of two letters andan integer, e.g. AB3 to indicate that they wish to move three cards from stack A onto stack B, orFD1 to indicate that they wish to move one card from stack F onto stack D.Write a function parse_move(input_str) which takes a string of this form and converts itinto a move tuple of the format described above, which it then returns. For exampleparse_move('AB3') should return the tuple (0, 1, 3).If the number of cards is omitted, make it default to one, so that parse_move('FD') returns(5, 3, 1), corresponding to the move of a single card from stack F to stack D.In this problem 2, you can assume that the string given by the user is a valid one, namely twoletters in the range A CF, optionally followed by a positive integer.Question 3: Determining if a move is valid (10 marks)Write a function validate_move(state, move) which returns True if the move move canbe applied to a game in state state. You can assume that the parameter state is a dictionarycorresponding to a valid game state (as described above), and that the parameter move is a tupleof three integers.This function should contain all the logic for checking whether a move is valid, including all therules described above on moving single cards, multiple cards, card ordering, blocking moves,blocked stacks and completed stacks. The function should also check for nonsensical moves(moving more cards than there are in the stack, moving to a nonexistent stack, etc.)Question 4: Moving the cards (6 marks)Write a function apply_move(state, move) which takes a valid game state state and amove tuple move. This function does not return anything, but modifi es in-place the stateparameter passed to it to apply the move described by the move tuple. You can assume thatstate is a valid game state and that move is a valid move that could be played in this state. Thisfunction should update the blocked and complete fi elds of the game state state, as well asthe stacks.Question 5: Detecting a win (2 marks) Coursework 3_u 14/16Write a function game_won(state) which takes a valid game state and returns True if thegame has been won (that is, all cards are in a completed stack) and False otherwise.The gameIn the template, you are given the pre-written function play_game() which uses the fivefunctions you have just written (initial_state, parse_move, validate_move,apply_move, game_won) to simulate a game. You can now run the play_game function toplay a working game. In questions 1 C5, you must not change the existing play_game()function.The preceding five questions are the core questions of this coursework, together worth 30marks. To recognise functions that are completely correct, a bonus is awarded for each questionin questions 1 C5 that scores full marks:1 bonus mark is awarded if one core question scores full marks3 bonus marks are awarded if two core questions score full marks6 bonus marks are awarded if three core questions score full marks10 bonus marks are awarded if four core questions score full marks15 bonus marks are awarded if five core questions score full marks(Because an important part of the game is that all of these functions work together correctly, thebonus grows more quickly as more questions score full marks.)There are then three additional questions (6 C8, worth 15 marks in total), and an assessment ofcode quality (10 marks), whose marks simply add to the 45 available in questions 1 C5 to make atotal of 70.Question 6: Handling errors (4 marks)Modify your parse_move function from problem 2 so that raises a ValueError exception if:The string is not of the form of: two letters, optionally followed by a positive integer, orThe two starting letters are outside the range A CFModify the play_game function to catch this exception and immediately re-prompt the user foranother move. Coursework 3_u 15/16As a result of these modifi cations, your game should now not crash, regardless of what string theuser types in.Question 7: Resetting the game (2 marks)Modify the parse_move function so that if the input string is R (or r), the function returns theinteger 0, rather than the move tuple.Modify the play_game function so that if parse_move returns this integer 0, then the game isreset (that is, initial_state is called and the game begins again).Question 8: Undoing moves (9 marks)Modify the parse_move function so that if the input string is U (or u), the function returns theinteger -1, rather than the move tuple.Modify the play_game function so that if parse_move returns this integer -1, then the mostrecent move is undone. By repeatedly typing U or u), the user should be able to undo as manymoves of the game as they like, until they end up back at the initial state. Ensure that the usercannot undo beyond this pointCode quality (10 marks)Ten marks are available for the quality of the code that you write in questions 1 C8. This will beassessed by humans reading your code, and will be based on:Clarity and precision of docstrings for each functionClarity of comments elsewhere in the code that describe what the code is doingCode quality. Code should be clear, effi cient and easy to maintain. Marks may be lost for C Very ineffi cient algorithms C Code that is unnecessarily verbose or diffi cult to understand C Unnecessary duplication of codeCode quality marks do not contribute to the bonus marks for questions 1 C5. Coursework 3_u 16/16
Coursework1 IntroductionThis coursework exercise asks you to write code to create an MDP-solver to work in the Pacmanenvironment that we used for the practical exercises.Read all these instructions before starting.This exercise will be assessed.2 Getting startedYou should download the file pacman-cw.zip from KEATS. This contains a familiar set of files thatimplement Pacman, and version 6 of api.py which defines the observability of the environment thatyou will have to deal with, and the same non-deterministic motion model that the practicals used.Version 6 of api.py, further extends what Pacman can know about the world. In addition toknowing the location of all the objects in the world (walls, food, capsules, ghosts), Pacman can nowsee what state the ghosts are in, and so can decide whether they have to be avoided or not.3 What you need to do3.1 Write codeThis coursework requires you to write code to control Pacman and win games using an MDP-solver.For each move, you will need to have the model of Pacman’s world, which consists of all the elementsof a Markov Decision Process, namely:• A finite set of states S;• A finite set of actions A;• A state-transition function P(s0|s, a);• A reward function R;• A discount factor γ ∈ [0, 1];Following this you can then compute the action to take, either via Value Iteration, Policy Iteration orModified Policy Iteration. It is expected that you will correctly implement such a solver and optimizethe choice of the parameters. There is a (rather familiar) skeleton piece of code to take as yourstarting point in the file mdpAgents.py. This code defines the class MDPAgent.There are two main aims for your code:1 Mallmann-Trenn / McBurney / 6ccs3ain-cw(a) Win hard in smallGrid(b) Win hard in mediumClassicTo win games, Pacman has to be able to eat all the food. In this coursework, for these objectives,“winning” just means getting the environment to report a win. Score is irrelevant.3.1.1 Getting Excellence pointsThere is a difference between winning a lot and winning well. This is why completing aim (a) and(b) from previous section allows you to collect up to 80 points in the Coursework. The remaining20 points are obtained by having a high Excellence Score Difference in the mediumClassic layout,a metric that directly comes from having a high average winning score. This can be done throughdifferent strategies, for example through chasing eatable ghosts.A couple of things to be noted. Let W be the set of games won, i.e., |W| ∈ [0, 25]. For any wongame i ∈ W define sw(i) to be the score obtained in game/run i.• ∆Se in the marksheet is the Excellence Score Difference. You can use the following formulato calculate it when you test your code and compare the result against the values in Table 3∆Se =Xi∈W(sw(i) − 1500) (1)Losses count as 0 score and are not considered. If ∆Se < 0, we set it to 0 (you cannot havea negative excellence score difference).• Because smallGrid does not have room for score improvement, we will only look at themediumClassic layout• You can still get excellence points if your code performs poorly in the number of wins; markingpoints are assigned independently in the two sections• Note however that marking points are assigned such that it is not convenient for you to directlyaim for a higher average winning score without securing previous sections’s aims (a) and (b)first• We will use the same runs in mediumClassic to derive the marks for Table 2 and Table 3.3.2 Things to bear in mindSome things that you may find helpful:(a) We will evaluate whether your code can win games in smallGrid by running:python pacman.py -q -n 25 -p MDPAgent -l smallGrid-l is shorthand for -layout. -p is shorthand for -pacman. -q runs the game without theinterface (making it faster).(b) We will evaluate whether your code can win games in mediumClassic by running:python pacman.py -q -n 25 -p MDPAgent -l mediumClassicThe -n 25 runs 25 games in a row.2 Mallmann-Trenn / McBurney / 6ccs3ain-cw(c) The time limit for evlauation is 25 minute for mediumClassic and 5 minutes for small grid.It will run on a high performance computer with 26 cores and 192 Gb of RAM. The timeconstraints are chosen after repeated practical experience and reflect a fair time bound.(d) When using the -n option to run multiple games, the same agent (the same instance ofMDPAgent.py) is run in all the games.That means you might need to change the values of some of the state variables that controlPacman’s behaviour in between games. You can do that using the final() function.(e) There is no requirement to use any of the methods described in the practicals, though youcan use these if you wish.(f) If you wish to use the map code I provided in MapAgent, you may do this, but you need toinclude comments that explain what you used and where it came from (just as you would forany code that you make use of but don’t write yourself).(g) You can only use libraries that are part of a the standard Python 2.7 distribution. This ensuresthat (a) everyone has access to the same libraries (since only the standard distribution isavailable on the lab machines) and (b) we don’t have trouble running your code due to somelibrary incompatibilities.(h) You should comment your code and have a consistent style all over the file.3.3 LimitationsThere are some limitations on what you can submit.(a) Your code must be in Python 2.7. Code written in a language other than Python will not bemarked.Code written in Python 3.X is unlikely to run with the clean copy of pacman-cw that we willtest it against. If is doesn’t run, you will lose marks.Code using libraries that are not in the standard Python 2.7 distribution will not run (inparticular, NumPy is not allowed). If you choose to use such libraries and your code does notrun as a result, you will lose marks.(b) Your code must only interact with the Pacman environment by making calls through functions in Version 6 of api.py. Code that finds other ways to access information about theenvironment will lose marks.The idea here is to have everyone solve the same task, and have that task explore issues withnon-deterministic actions.(c) You are not allowed to modify any of the files in pacman-cw.zip except mdpAgents.py.Similar to the previous point, the idea is that everyone solves the same problem — you can’tchange the problem by modifying the base code that runs the Pacman environment. Therefore,you are not allowed to modify the api.py file.(d) You are not allowed to copy, without credit, code that you might get from other students orfind lying around on the Internet. We will be checking.This is the usual plagiarism statement. When you submit work to be marked, you should onlyseek to get credit for work you have done yourself. When the work you are submitting is code,3 Mallmann-Trenn / McBurney / 6ccs3ain-cwyou can use code that other people wrote, but you have to say clearly that the other personwrote it — you do that by putting in a comment that says who wrote it. That way we canadjust your mark to take account of the work that you didn’t do.(e) Your code must be based on solving the Pacman environment as an MDP. If you don’t submita program that contains a recognisable MDP solver, you will lose marks.(f) The only MDP solvers we will allow are the ones presented in the lecture, i.e., Value iteration,Policy iteration and Modified policy iteration. In particular, Q-Learning is unacceptable.(g) Your code must only use the results of the MDP solver to decide what to do. If you submitcode which makes decisions about what to do that uses other information in addition to whatthe MDP-solver generates (like ad-hoc ghost avoiding code, for example), you will lose marks.This is to ensure that your MDP-solver is the thing that can win enough games to pass thefunctionality test.4 What you have to hand inYour submission should consist of a single ZIP file. (KEATS will be configured to only accept asingle file.) This ZIP file must include a single Python .py file (your code).The ZIP file must be named:cw .zipso my ZIP file would be named cw mallmann-trenn frederik.zip.Remember that we are going to evaluate your code by running your code by using variations onpython pacman.py -p MDPAgent(see Section 5 for the exact commands we will use) and we will do this in a vanilla copy of thepacman-cw folder, so the base class for your MDP-solving agent must be called MDPAgent.To streamline the marking of the coursework, you must put all your code in one file, and this filemust be called mdpAgents.py,Do not just include the whole pacman-cw folder. You should only include the one file that includesthe code you have written.Submissions that do not follow these instructions will lose marks. That includes submissions whichare RAR files. RAR is not ZIP.5 How your work will be markedSee cw-marksheet.pdf for more information about the marking.There will be six components of the mark for your work:(a) FunctionalityWe will test your code by running your .py file against a clean copy of pacman-cw.As discussed above, the number of games you win determines the number of marks you get.Since we will check it this way, you may want to reset any internal state in your agent using4 Mallmann-Trenn / McBurney / 6ccs3ain-cwfinal() (see Section 3.2). For the excellence marks, we will look at the winning scores forthe mediumClassic layout.Since we have a lot of coursework to mark, we will limit how long your code has to demonstratethat it can win. We will terminate the run of the 25smallGrid games after 5 minutes, andwill terminate the run of the 25 mediumClassic games after 25 minutes. If your code hasfailed to win enough games within these times, we will mark it as if it lost. Note that we willuse the -q command, which runs Pacman without the interface, to speed things up.(b) Code not written in Python will not be marked.(c) Code that does not run in our test setting will receive 0 marks. Regardless of the reason.(d) We will release the random seed that we use for marking. Say the seed is 42, then you needto do the following to verify our marking is correct:1) fix the random seed to 42 (int, not string type) at line 541 of pacman.py. (not ’42’)2) download a fresh copy of the new api (to avoid using files you modified yourself)3) run python pacman.py -q -f -n 25 -p MDPAgent -l mediumClassic4) you should get the same result as us. If not repeat step 3) again. Should the outcome bedifferent, then you didn’t fix the random seed correctly. Go back to 1)A copy of the marksheet, which shows the distribution of marks across the different elements of thecoursework, will be available from KEATS.5 Mallmann-Trenn / McBurney / 6ccs3ain-cw
N1569 Workshop 3 Calculation Questions 1. What is the expected return of this portfolio? • Asset A: Weight = 40%, expected return = 9% • Asset B: Weight = 40%, expected return = 6% • Asset C: Weight = 20%, expected return = 12% 2. Calculate the covariance between A and B where: • Standard deviation of Asset A = 18% • Standard deviation of Asset B = 12% • Correlation between A and B = 0.7 3. Based on daily data of realised returns, the mean return is 0.02% and the standard deviation of returns is 1.2%. Assuming 250 trading days per year, find the annualized mean return and volatility. 4. What is the risk contribution of Asset A to the portfolio volatility? • Asset A: Weight = 60%, Volatility = 20% • Asset B: Weight = 40%, Volatility = 25% • Correlation between A and B = 0.4 Excel Exercises A. Uni.xls contains historical prices of the Uniswap decentralized exchange token (UNI). (i) Upload the spreadsheet to ChatGPT’s Excel AI and ask it to draw a time series graph of the UNI token prices. (ii) Use the Excel formula bar to calculate the daily log and realised returns, take their difference and plot this as a time series graph in Excel, commenting on the result B. A portfolio contains five stocks, A, B, C and D with holdings worth $4m, $2m, $5m, $4m and $5m respectively. Their volatilities 20%, 30%, 25%, 15% and 10% respectively and their returns all have pairwise correlation 0.5. (i) Enter the holding values as a column in Excel and then produce another 5 ×1 column vector w next to it containing the portfolio weights (ii) Enter their volatilities into a 5 × 5 diagonal matrix D and their correlations into a 5 × 5 matrix C (iii) Use the MMULT function to compute the covariance matrix as V = DCD (iv) Use the MMULT function to compute the portfolio variance as w′ Vw and then take the square root to find the portfolio volatility (v) Explore how this volatility changes with the correlations between the stock returns
QUANTITATIVE METHODS FOR BUSINESS STA60104 GROUP ASSIGNMENT SEPTEMBER 2024 SEMESTER Instructions: 1. This assignment requires students to work in a group of 3 members. All members must come from the same tutorial group. 2. You are required to submit a soft copy of your assignment report in Portable Document Format (PDF) in myTIMeS (Taylor's Integrated Moodle e-Learning System) by 1 December 2024 (only one submission is allowed for each group). The assignment must be submitted to turnitin for the purpose of checking for any indications of plagiarism. Question 1 (17 marks) A store manager would like to increase the number of items purchased by each customer. Currently, the probability distribution of the number of items purchased by each customer is given as follows: Number of items purchased 0 1 2 3 4 5 Probability 0.05 0.10 0.20 0.40 0.15 0.10 The store manager is considering two options: He can offer more discounts to customers who purchased more items (Option 1) or he can reconfigure the layout of his store (Option 2). The respective revised probability distribution for the two options is given as follows: Number of items purchased 0 1 2 3 4 5 Probability (Option 1) 0.05 0.05 0.10 0.20 0.30 0.30 Probability (Option 2) 0.05 0.05 0.15 0.20 0.35 0.20 (a) Discuss whether the two options are able to increase the number of items purchased by each customer. (6 marks) (b) Determine and justify which option is more effective in increasing the number of items that are purchased by each customer. (2 marks) (c) Discuss whether the two options increase the variability in the number of sales. (9 marks) Question 2 (13 marks) The government of a certain country is designing a subsidy policy based on the income of its citizens. Suppose the income of its country’s citizens is normally distributed with a mean of RM5000 and a standard deviation of RM1000. The government is planning to design a subsidy policy whereby the country’s citizens are separated into three groups according to the citizens income, where Group I consists of citizens with the lowest 30% of income, Group II consists of citizens with incomes between Groups I and III, whereas Group III consists of citizens with the highest 20% of income. The following table shows the amount of subsidy per year for the three groups. Group Subsidy per year I 6000 II 3000 III 0 (a) Determine the approximate range of incomes for each of the three groups. (8 marks) (b) Evaluate the mean and standard deviation of the amount of subsidy per year. (5 marks) Question 3 (19 marks) (Note: This question should be attempted using Excel) An owner of two retail outlets would like to compare the monthly revenue of these two outlets. The following table shows the monthly revenue of these outlets for the past 4 years. Month Outlet A Outlet B 1 39227 31777 2 36835 47369 3 40327 16864 4 33974 34079 5 48726 45257 6 45911 13191 7 38482 39313 8 42162 40169 9 36062 21097 10 40803 12851 11 38622 13228 12 36434 20587 13 38339 25075 14 39757 14239 15 40821 35287 16 43374 30809 17 37555 8821 18 39686 25762 19 37891 28634 20 40131 23539 21 44105 41088 22 43908 49523 23 30691 19432 24 47801 38125 25 45246 45411 26 45886 14012 27 48203 16824 28 39413 19058 29 44429 13659 30 41461 17680 (a) Identify the minimum, the first quartile (Q1), the median (Q2), the third quartile (Q3) and the maximum for the monthly revenue for Outlets A and B. Use the function QUARTILE.EXC in Excel to find Q1, Q2 and Q3. (5 marks) (b) Compute the lower and upper outlier limits for the revenue of Outlets A and B. Hence, determine whether there are any outliers. (4 marks) (c) Based on parts (a) and (b), produce the box and whisker plots for the revenue of Outlets A and B. Subsequently, compare the two box and whisker plots in terms of the median, spread and shape. (5 marks) (d) From the box and whisker plots in part (c), is it reasonable to conclude that Outlet A typically shows higher monthly revenue than Outlet B? Explain your answers. (2 marks) (e) The owner speculates that the revenue for Outlet A correlates with the revenue for Outlet B. Discuss whether his speculation is valid. (3 marks) Watch the following videos for the guidelines to construct a box and whisker plot: (i) https://www.youtube.com/watch?v=39lsUsJsc2c (ii) https://www.youtube.com/watch?v=mhaGAaL6Abw Question 4 (21 marks) A retail company with 45 outlets is collecting data on possible variables that might have an impact on the weekly sales of each of its outlets. The attached Excel file contains data on the weekly sales of each of its outlets, together with the corresponding average temperature, fuel price, consumer price index (CPI) and unemployment rate for that particular week. By selecting one of the stores (i.e. any store from Store 1 to 45), answer the following questions. Attach the Excel output together with your solutions. (a) By selecting one of the variables (Temperature, Fuel Price, CPI or Unemployment Rate) as the independent variable, and Weekly Sales as the dependent variable, (i) form. an estimated simple linear regression equation between the selected independent variable and the dependent variable. Subsequently, interpret the slope coefficient of the equation. (2 marks) (ii) determine and interpret the correlation coefficient and the coefficient of determination. (3 marks) (iii) at the 5% significance level, determine whether the data provide sufficient evidence that there is a positive or negative correlation between the selected independent variable and the dependent variable. (2 marks) (b) By selecting all the variables (Temperature, Fuel Price, CPI and Unemployment Rate) as the independent variables, and Weekly Sales as the dependent variable, (i) form an estimated multiple regression equation between the independent and dependent variables. Subsequently, interpret the slope coefficients of the equation. (3 marks) (ii) determine and interpret the coefficient of determination of the multiple regression model. (1 mark) (iii) at the 1% significance level, use the critical value approach to test whether the overall multiple regression model is significant. (3 marks) (iv) at the 5% significant level, identify the independent variables that have a significant effect on Weekly Sales. Justify your answer. (4 marks) (v) construct scatter plots for the significant independent variables identified in part (iv) against the variable Weekly Sales. Comment on the scatter plots. (3 marks) To run regression analysis, you need to install the Data Analysis ToolPak in Microsoft Excel, watch the following video on the guidelines to install Data Analysis ToolPak in Microsoft Excel: https://www.youtube.com/watch?v=_yNxLFagKgw Watch the following videos for question 4(b) on multiple regression analysis: • https://www.youtube.com/watch?v=5nccswDrCUs&list=PLR- lkPxLxukAQ8HFkp10s_jXSfrj0YE8l&index=2 • https://www.youtube.com/watch?v=EfxvaFvVPFU&list=PLR- lkPxLxukAQ8HFkp10s_jXSfrj0YE8l&index=4 • https://www.youtube.com/watch?v=d_baG5cYKjg • https://www.youtube.com/watch?v=jGd2cj4K4Ww • https://www.youtube.com/watch?v=IQo_T7BmO90
LIN329 – Final paper Please submit your final paper in hard copy at the beginning of lecture on December 3, 2024. Length/formatting restrictions Your paper should fit the following requirements: • 1.5-spaced • Margins of at least 2.54 cm (1 inch) • 12-point font • Eight to twelve pages, including examples, tableaux, and references • The paragraph about how you incorporated feedback from your abstract can be on an additional page Components of the final paper Your final paper should include the following components. They don’t need to be in this order. The requirements for each of these are explained in more detail following the list. • A title • Information on the language • Definition of the phonological phenomenon • Examples from the language • Description of the pattern • Explanation about what is interesting about the pattern • Optimality Theoretic analysis • Discussion • References • A paragraph about how you incorporated feedback from your abstract Title, information on the language, definition of the phenomenon, examples from the language, description of the pattern, explanation about what is interesting, and references: These all have the same requirements as in the abstract. They can be taken directly from your abstract or changed as appropriate based on feedback you received. You can also expand upon any of them, since you have more space in the paper than you did in the abstract. Optimality Theoretic analysis: Your paper must include an Optimality Theory analysis of the data. This should be a full analysis of all the facts that you have presented about the phenomenon in the language. The analysis should include: • A list of constraints, with definitions for all of them • At least one pairwise comparison to illustrate how you came up with one part of the ranking • A ranking of all of the constraints you’re using • At least two full tableaux, illustrating the application of your analysis to two of your examples • Each of the two full tableaux should contain all of your constraints and at least four candidates • There should be enough candidates in your full tableaux to illustrate the relevance of all constraints Discussion: Your paper must include a discussion. This should be around 2-3 paragraphs at a minimum, but can be longer if you have more to say. Some things you could discuss include (but are not limited to): whether your analysis fully accounts for all of the data, any remaining questions, additional points of interest about the analysis, or a conjecture about the applicability of your analysis more broadly in the language and/or for the same phenomenon in other languages. Paragraph about how you incorporated feedback: Write a paragraph explaining how you took the feedback from your abstract into consideration in writing your final paper. This can be on a separate page from the rest of the paper.
CSI09102 Creative Technologies Creative Technology Project Learning Outcomes Covered: 1, 2, 3, 4 Assessment Type: Practical Assessment / Demonstration Overall module assessment 100% For this assessment: 100% Assessment Limits: Presentation, Unity files, videos, reports as detailed. Submission Date: Friday, 06 December 2024 Submission Time: 15:00 GMT Submission Method: Via Moodle • You are advised to keep a copy of your assessment solutions. • Please note regulation Section B5.3.b regards component weighting. • Late submissions will be penalised following the University guidelines as follows: Choose an item. • Extensions to the submission date of up to 10 working days may only be given by the Module Leader for exceptional circumstances by submitting an RE1 form. to them.https://my.napier.ac.uk/-/media/mynapier/section-images/your- studies/documents/academic-issues/re1-form-22_23-updated.ashx • Feedback on submissions will normally be provided within three working weeks from the submission date. The University rules on Academic Integrity will apply to all submissions. The student academic integrity regulationscontain a detailed definition of academic integrity breaches which includes use of commissioned material; knowingly permitting another student to copy all or part of his/her own work You must not share your work with other students - this includes posting any of your work in any repository that is accessible to others (such as GitHub) and applies also after you have completed the course. You must not ask coursework-related questions in online for a (such as Stackoverflow) and you must not use ChatGPT or other generative AI tools - this would constitute academic misconduct as it would be commissioning material. By submitting the report, you are confirming that: • It is your own work except where explicit reference is made to the contribution of others. • It has not been submitted for any module or programme degree at Edinburgh Napier University or any other institution. • It has not been made with the assistance of Artificial Intelligence (AI) tools [except where and how as has been clearly stated]. Creative Technology Project (Phase 1 and 2) Assessment Details: Phase 1 Project Brief For your assessment you will design and implement an Interactive Virtual Exhibit. Your exhibit will become part of the Creative Technologies Online Gallery. Phase 1 is worth 60% of the overall mark. You will implement the exhibit using the Unity 3D game engine, as used in the practical sessions of this module. You will be provided with an optional quick-start template unity project that will provide the bare-minimum necessary to create your exhibit. You can customise the template as much as you want to fit your vision and the design of your exhibit. The concept of a ‘Room’ is used only as an indicative starting point. You can stretch this definition as far as you want. Your exhibit must be suitable for a family audience and therefore must not contain any sexually explicit images, animations, video or sounds. It must also not contain any content that represents bodily harm or people in danger of trauma. The Module leader reserves the right to make the determination of whether the work and content meet these requirements. Timeline & Deadline Dates: The deadline for the Phase 1 submission is: 6th December 2024 at 15:00 Proposal Presentation Mid-way through the trimester you will present a proposal for your exhibition during a practical. Your proposal must be a short 1 slide presentation that outlines your exhibition design. The presentation should last between 1-2 minutes to allow time for feedback. The format of the presentations may be modified. The most up to date specification will be on the Moodle. Please note that the presentation will also be part of your Report submission (Phase 2). Deliverable Submission Requirements Your submission for Phase 1 must consist of the following deliverables: 1. Your virtual exhibit project files a. This should be a single .zip file containing the entire folder of your unity project. b. It is highly recommended that you test that your submission works (by unzipping it and testing that it can be opened as a unity project) prior to submitting. 2. Your ‘built’ virtual exhibit. a. This should be a single .zip file containing a published standalone build of your exhibit. b. This must be submitted as a Windows Build. 3. At least 4 screenshots of your exhibit a. Should try to be as illustrative as possible. b. Annotations to give additional context are encouraged. 4. A video demonstrating your exhibit. a. The video may be between 30 and 60 seconds in length. b. Can be from the perspective of the visitor, or a fly through. c. Must be recorded “in-game” . (Must not be a recording from your phone pointed at your computer screen.) d. It is encouraged to make this a compelling production (e.g., promotional style, soundtrack, voiceover, etc.) 5. A written technical summary of your implementation a. Must include any technical information that is necessary to run and use the exhibit experience. b. Must explain briefly how the exhibit is meant to be experienced (e.g., character controls, and objectives, etc.) c. Especially important to note any unconventional elements in your exhibit. For example, things that might be easy to miss. d. (Important!) A list of all the 3rd party resources you used (code, 3D models, Audio, etc.) with attribution links and licencing details 6. PLEASE NOTE: a. Each submission must follow the following naming scheme: i. Name_Surname_MatriculationNumber_SubmissionType.FileExtensi on ii. Example: “John_Smith_44241_ProjectVideo.zip” b. Moodle has a 2GB upload limit, so you may need to package and upload your deliverables separately. If any one of your submission files is larger than 2GB, then you can upload it to OneDrive and share the link in your technical summary document. Please note only OneDrive links from your university OneDrive account will be accepted. If you do use OneDrive to submit one of your deliverables, please also upload the rest of them as well. Important – you must submit at least one deliverable in order to get graded. The quality of your deliverable assembly and delivery is graded. Use a sensible folder structure and good fine naming practices. Imagine you were submitting this to a paying client. Phase 1 Marking Criteria Marking Specifications: None Substantial work required More work required Satisfactory Good Very good Excellent Exemplary Outstanding Proposal Presentation (E.G. Was a proposal presentation created and presented) 0 1 2 2.5 3 3.5 4 4.5 5 Packaging (E.G. Was the submission packaged up in a professional manner according to the specifications) 0 1 2 2.5 3 3.5 4 4.5 5 Documentation (E.G. Was there a written summary? Was there a video? Was there adequate production quality? E.g., Clear demonstration of experiences, Audio and voiceover, etc. 0 3 4 5 6 7 8 9 10 Concept Design (E.g., Is the design suitable? Does it communicate its message? Does it have a trajectory?) 0 3 4 5 6 7 8 9 10 Experience Design (E.g., How compelling is the experience, does the design and intent come through?) 0 3 4 5 6 7 8 9 10 Interaction Design (E.g., Are there sufficient interactive elements? Do they work?) 0 3 4 5 6 7 8 9 10 Implementation Quality (E.G. Does the experience run? Is it polished? Is it Accessible? User Friendly with good instructions?) 0 3 4 5 6 7 8 9 10 Report Brief – Phase 2 You must submit a written report that details the evolution of the design and implementation of your exhibit from initial idea to final submitted output. Indicatively, your report should about 1500 (+/- 10%). Illustrations and figures are encouraged. Phase 2 is worth 40% of the overall mark. At minimum, the report should cover the following: • Introduction o A short summary of your project • Context o Explain the concept behind your exhibit design, what it’s intent or message is and how it is communicated to your intended audience. This includes your Design Rationale. • Influences and inspiration o You should describe and appropriately cite your influences and inspirations, whether they are other exhibits and experiences, or concepts and stories. • Description o A detailed description of your exhibition (images encouraged) • Technology o A description of the technical aspects of your implementation. Here you can describe the effort you put into making the exhibit. • Reflection and Conclusion o A closing statement where you can critically reflect on your work, identifying any issues or limitations and a discussion of what further work could be done. • Appendix You are expected to author the document in an academic style. This means using an appropriate voice (Do not use 1st person), and appropriate APA-style 7th edition referencing (https://libguides.napier.ac.uk/APA). Timeline & Deadline Dates: The deadline for the CW2 submission is: 13th December 2024 at 15:00 Deliverable Submission Requirements Your submission must consist of the following: 1. Your report file a. In Word or PDF format – other formats will not be accepted. 2. PLEASE NOTE: a. Each submission must follow the following naming scheme: i. Name_Surname_MatriculationNumber_SubmissionType.FileE xtension ii. Example: “John_Smith_44241_ProjectReport.docx” Important – you must submit at least one deliverable in order to get graded. The quality of your deliverable assembly and delivery is graded. Use a sensible folder structure and good fine naming practices. Imagine you were submitting this to a paying client.
Department of Electronic and Electrical Engineering EEE225 Semiconductors for Electronics and Devices Problem Sheet 3 1. The current at room temperature (20°C) in a certain p-n junction is 2 × 10-7A when a large reverse bias is applied. Calculate the current flow for a forward bias of 0.1 V. 2. An ideal silicon p-n junction diode has a reverse saturation current of 30μA at a temperature of l25°C. Find the dynamic (slope) resistance of the diode for a voltage 0.2V applied in (a) the forward direction, and (b) the reverse direction. Comment on the results. 3. A p-n junction diode has an observed saturation current of 1μA at room temperature. Find the junction voltage corresponding to a forward current of (a) 1mA, and (b) l0mA. The diode is constructed with material of conductivity 2000S/m on the p-type side and 500S/m on the n-type side. The diode is 2mm long (with the junction at the centre of this length) and 1 × 0.5mm2 in cross-section. Find the total voltages applied corresponding to currents (a) and (b) above, including the resistive drops in the n- and p-type regions, comment of the results. 4. A certain germanium rectifier has a reverse saturation current of 1μA compared with l0-8A for a similar-sized rectifier made of silicon. Compare the theoretical forward voltages dropped across the two rectifiers when each carries a current of l00mA at room temperature. By how much would an effective bulk resistance of 1Ω increase the forward voltage drop in each case? 5. In a certain metal/n-type semiconductor Schottky barrier diode, the reverse bias saturation current is 3μA. The diode is connected in series with a resistor across a d.c. source of 0.2V, such that the diode is forward biased and 0.1V is dropped across the resistor. Find the value of the resistor. (Assume operation at room temperature = 20°C). 6. A Schottky diode passes a forward current of l0mA when a bias of 0.25V is applied at room temperature (300K). What voltage is required to double this current? Also, estimate the maximum reverse current for reverse bias voltages less than the breakdown voltage. 7. The dynamic (slope) resistance of a Schottky diode at 300K is measured when forward biased with 0.25V. What applied forward voltage would double the slope resistance? 8. A certain device consists of an n-type semiconductor, of work function 1eV, sandwiched between two metal contacts, M1 having work function 2eV and M2 having work function 0.5eV. At a temperature of 290K, the resistance of the bulk semiconductor between the contacts is 2Ω. When a voltage of 1V is applied between M1 and M2, with M1 positive, 350mA flows. What current will flow when the polarity of the voltage is reversed, its magnitude remaining the same? (Ignore the resistance of the metal contacts).
BIO2101 Exercise 6: Cell Fractionation by Differential Centrifugation Purpose: To integrate microscopic observation and quantitative analysis with cell fractionation. Purification of mitochondria by differential sedimentation and monitoring of fractions for specific activity of succinate dehydrogenase. Part A: Preparation of cell fractions Introduction: Eukaryotic cells contain many kinds of subcellular organelles. In order to determine the physical and chemical properties of these organelles, it is necessary to study them in isolation from other cellular components. The most common way to perform. this cell fractionation is to disrupt the cells and separate their components by use of centrifugation. Particles subjected to the acceleration of a centrifugal field will move, or sediment, at a velocity determined by various properties of the particle, including its mass, its density compared to that of the suspending fluid, and its frictional properties. If the suspending fluid forms a gradient of density around the density of the particle itself, the particle will form a band at a position in the centrifuge tube where its density is matched by the density of the fluid. The forces impelling it further down the tube are countered by the increasing resistance of the ever denser fluid. The tendency of diffusion to disperse the particles is countered by the centrifugal field. Cell homogenates treated in this way can be separated in one step into multiple bands of species differing in density. This technique is known as equilibrium density gradient centrifugation. The best separation, however, occurs only after centrifugation for a long time, when equilibrium has become established between the forces concentrating the particles and those leading to their dispersion. A less complete, but more rapid, separation can be achieved by a technique called differential sedimentation. The fluid in which the particles are suspended is less dense than any of the particles. Thus instead of forming bands, they pass down the centrifuge tube at a rate determined by the properties described above. When they reach the bottom of the tube, they form a pellet. If particles of several different kinds are present initially, the pellet's composition will change over the period of the centrifugation, initially consisting of the most rapidly sedimenting particles, with the slower ones being added later. Thus a separation can be effected by stopping the centrifugation periodically, removing the pellet, and continuing to centrifuge the supernatant. This procedure has been standardized into a scheme for fractionating cells, in which the various pellets have been identified according to the predominant organelle they contain: the nuclear fraction, the mitochondrial (or plastid) fraction, the microsomal fraction, and the soluble fraction. It is important to remember, though, that each fraction is contaminated by elements of the other fractions, unless further purified. Also the soluble fraction contains material, e.g. ribosomes, which could be pelleted if the centrifugal forces were further increased. Thus, in a rigorous application, it is necessary to confirm by independent means the purity of a fraction. This can be done by microscopic analysis or by assaying for an enzyme activity characteristically associated with a particular organelle. The rate at which a particle moves is also a property of the centrifugal field, usually expressed as a multiple of g, the acceleration due to gravity. Thus the relative centrifugal force, R.C.F. = 1.118 x 10-5 (rpm)2r, where rpm is the revolutions per minute of the rotor and r is the distance (in cm) of the particle from the axis of rotation. A convenient way to determine the R.C.F. for a given rotor at a given speed is by use of a nomogram. The radius used is usually the mean between the radius to the top of the centrifuge tube and the radius to the bottom. Since distance = rate x time, a given particle may be sedimented the length of the centrifuge tube (i.e., pelleted), by subjecting it to high speeds for short times or to lower speeds for longer times. Other factors influence the choice of conditions, such as diffusion properties of the particle of interest compared to other particles in the homogenate, or other time-dependent factors. Various kinds of centrifuge are available to carry out this kind of work. Clinical centrifuges, both refrigerated and not, seldom run faster than 5,000 rpm; they can readily sediment cells, nuclei, or chloroplasts, but smaller particles require impractically long times. High-speed centrifuges go as fast as 18,000 rpm; ultracentrifuges can manage 60,000 - 75,000 rpm. Both these types of instrument are usually refrigerated and may also be operated in a vacuum, to reduce heat generated by friction with air molecules. The rotors, too, are designed of titanium and strong alloys to withstand the strain of the centrifugal forces and to conserve angular momentum. Procedure: The exercise today will use differential sedimentation to prepare three fractions from a homogenate of rat liver: a "nuclear" fraction, a "mitochondrial" fraction, and a "soluble" fraction. Note: Soluble fraction would still include cell components such as microsomes and ribosomes, which could be sedimented in stronger centrifugal fields. 1. Add about 2 g fresh rat liver into a 50 ml centrifuge tube containing 5 ml ice-cold buffered sucrose. Put the tube on ice. Finely mince the tissue with scissors. *Buffered sucrose: 0.25 M sucrose in 10 mM phosphate buffer, pH 7.0. 2. Transfer the tissue pieces with the ice-cold sucrose to a homogenizer vessel. Add additional 5 ml ice-cold buffered sucrose to rinse the tube and pour out all remaining tissues to the vessel again. 3. Homogenize the minced liver tissue for about 12 seconds (until no lumps remain). Then add additional 5 ml ice-cold buffered sucrose to rinse the homogenizer vessel. Keep cold throughout. (Step 1-3 will be done by TAs.) 4. Decant the homogenate through a cell strainer into an ice-cold 50-ml centrifuge tube. Rinse the homogenizer vessel with 5 ml ice-cold buffered sucrose and pour it through the cell strainer into the tube too. This is a 10% (w/v) crude homogenate (why 10%?). Record the volume. 5. Save 3*1 ml of crude homogenate in three chilled 1.5 ml microfuge tubes, for assay, microscopy, and SDS-page (next experiment), respectively. Label the tube with group number and indicate which fraction it is. Centrifuge the rest of homogenate fraction in the centrifuge tube for 10 min at 600 g at 4oC. 6. After centrifugation of Step 5: (i) Carefully transfer the supernatant to another high speed centrifuge tube (do not disturb the pellet). (ii) For the pellet (which is the nuclei), resuspend in 10 ml buffered sucrose. Centrifuge again for 10 min at 600 g to wash the nuclei. Discard the supernatant. Resuspend the pellet in 5 ml buffered sucrose, this is the nuclear fraction (so its total volume is 5 ml). Save it on ice. 7. Centrifuge again the supernatant from Step 6(i) for 20 min at 20,000 g at 4oC. 8. After centrifugation of Step 7: (i) Carefully transfer he supernatant to another chilled test tube (do not disturb the pellet). Record the volume. This is the soluble fraction. Save it on ice. (ii) For the pellet, resuspend in 5 ml ice-cold sucrose, this is the mitochondrial fraction (so its total volume is 5 ml). Save it on ice. 9. In addition to crude homogenate (saved from Step 5), you now have another three samples: nuclear fraction (Step 6 ii), mitochondrial fraction (Step 8 ii) and soluble fraction (Step 8 i). Save 3*1 ml of crude homogenate in three chilled 1.5 ml microfuge tubes, for assay, microscopy, and SDS-page (next experiment), respectively. Label the tubes with group number and indicate which fraction it is. Note: Submit the two of the saved 1 ml of the four fractions to instructor. They will be saved in refrigerator for the protein determination and the enzyme assay to be done in next week, and SDS-page in next experiment. 10. For the microscopy observation of samples, prepare wet mounts of each and examine by microscopy as follows: • Add 10 ul of the fraction on a clean slide. • Spread the drop with the edge of a second slide to make a smear. • Allow the slide to air dry. • Add 10 ul of stain (methyl green pyronin). Let sit 3 minutes. • Invert the slide and immerse slide in a rack in a pan of SLOW running tap water for about 30 seconds. Avoid flushing the sample smear directly with water! • Add 10 ul glycerin and a coverslip. * Please pipette the glycerin gently since glycerin is sticky • Observe in bright field. Nuclei should stain purple, cytoplasm red or pink, and mitochondria can be seen as small dots (under 400x magnification). Note: Record the presence of cytoplasm and nuclei in crude homogenate and all three fractions, and describe their relative amount by the number of “+” sign. Part B, C and D should be done in next week Part B: Sample Dilution Procedure: 1. Thaw all four saved samples from last week. 2. Dilute your fractions in buffered sucrose to 1 ml (= 1000 μl), with each diluted sample in microfuge tube. 1/25 dilution 40 µl sample + 960 µl sucrose buffer 1/50 dilution 20 µl sample + 980 µl sucrose buffer Part C: Protein Assay Procedure: 1. Prepare the following blank and bovine serum sample (BSA) in 96- microplate wells for protein standard plot construction. We can use Bradford reagent to determine the protein content of bovine serum albumin (BSA) in order to construct a standard plot as reference. Bradford reagent contains a dye, Coomassie blue, which binds to protein. The dye/protein complex produces a blue color whose absorbance is directly proportional to the protein concentration. 1. Bovine serum albumin (BSA) has been prepared in PBS at a concentration of 1.2 mg/ml. 2. Using an aliquot of the 1.2 mg/mL protein standard prepare a range of standards solutions. Add the following volumes of 1.2 mg/mL protein standard and H2O (Table 1) to the appropriate wells to construct a range of standards solutions between 0 and 0.6 mg/mL (e.g. 0, 0.0375, 0.075, 0.15, 0.3, 0.6 and 1.2 mg/mL). Mix the content well by pipetting up & down. A total volume of 50 μL of each standard is sufficient for all the required assays. Table 1 Construction of Protein Standard Plot (BSA is given at concentration of 1.2 mg/ml.) 2. Add 10 μL of standards to the appropriate wells. Then add 200 μl of Bradford reagent to each well. Mix the contents well. Wait for 3-minutes 3. Repeat Step 2 for the following diluted samples (which are already prepared in Part B). Add 10 μL of samples to the appropriate wells Homogenate 1:50 Nuclear 1:25 Mitochondrial 1:25 Soluble 1:25 4. After 3-minutes, measure absorbance at 595 nm with microplate spectrophotometer (BioTek Epoch plate reader). Record the absorbance value on the datasheet. Part D: Succinate Dehydrogenase (SDH) Assay Introduction: This enzyme catalyzes the oxidation of succinate to fumarate in the Krebs cycle. It is a good choice as a marker enzyme for mitochondria because not only is it readily assayed, but also it is the only Krebs cycle enzyme to remain bound to the inner mitochondrial membrane. Thus even if mitochondria lose some their soluble contents through damage during isolation, they remain able to exhibit SDH activity. The chemical reaction is given in Figure 1. Flavine adenine dinucleotide (FAD) is a coenzyme covalently bound to the SDH enzyme. For the enzyme to complete its catalytic cycle, the electrons it receives from succinate are ordinarily passed on down the electron transport chain to oxygen, and the reduced FAD becomes reoxidized, ready to encounter another succinate. This procedure can be altered, however, by providing the assay system with artificial electron acceptors to draw the electrons from reduced FAD. If these acceptors are dyes with characteristic absorbance in the oxidized and reduced forms, the progress of the reaction may be assayed by the change in the amount of color. Figure 1. The chemical reaction catalyzed by the enzyme succinate dehydrogenase (SDH) One very useful artificial electron acceptor is 2, 6-dichlorophenol indophenol (DCPIP) (see Figure 2). It absorbs strongly at 600 nm when oxidized, but becomes colorless in its reduced form. Thus the basic procedure is to mix samples of fractions to be tested (which supposed to contain different level of SDH) with an assay mixture containing, among other things, substrate and DCPIP. The absorbance of the mixture is measured as a function of time, and thus the rate of the enzyme-catalyzed reaction is determined. Figure 2. The structure of dichlorophenol-indophenol (DCPIP) in its oxidized and reduced forms Procedure: For each assay follow this procedure: 1. Centrifuge homogenate and nuclear fraction at 600x g, 5min. 2. Add 10 μL of the following samples to the appropriate wells. Then add 180 μl of pre-warm assay medium to each well. Mix the contents well. Well # Assay medium (μL) Buffered sucrose or Diluted fractions 1 180 10 μL of buffered sucrose (blank) 2 180 10 μL of homogenate supernatant 3 180 10 μL of nuclear fraction supernatant 4 180 10 μL of mitochondrial fraction 5 180 10 μL of soluble fraction 3. Then add 10 μlof Solution 5 to each well. Mix the contents well 4. Measure absorbance at 0 min and 2 min (600 nm) with microplate spectrophotometer (BioTek Epoch plate reader). Record the absorbance value on the datasheet. Data Processing of SDH Assay 1. This calculation assumes that the components in the assay medium remain constant over the assay, valid unless some component of the mixture precipitates. Therefore, the change in absorbance at 600 nm is solely due to the activity of SDH which indirectly lead to the conversion of oxidized DCPIP (absorbs at 600 nm) into reduced DCPIP (invisible) 2. Calculate the specific activity of succinate dehydrogenase in the original homogenate and in each fraction by dividing the activity per ml of that fraction by the protein content protein concentration (mg per ml) of the same fraction. The result, expressed in units of activity per mg of protein (U/mg), should be in some cases greater than the homogenate and in some cases less. It represents the degree to which the particular fraction is enriched for succinate dehydrogenase (for example, mitochondria) or the extent to which the enzyme has been removed from that fraction. Experimental Datasheet of Exercise 6 Part C: Protein Standard Plot BSA Standard Protein concentration (mg/mL) A595 #1 0 #2 0.0375 #3 0.075 #4 0.15 #5 0.3 #6 0.6 #7 1.2 On your laboratory write-up attach your standard curve and calculate the followings: Correlation coefficient, slope, Y-intercept, linear equation of the plot Protein content per fraction Fraction Dilution A595 Protein conc. in diluted sample (mg/ml) Protein conc. in fraction (mg/ml) Volume of fraction (ml) Amount of protein in fraction (mg) Homogenate 1:50 Nuclear 1:25 Mitochondrial 1:25 Soluble 1:25 On laboratory write-up, show the calculation steps accordingly Data Processing of Part C 1. Record absorbance at 595 nm in the column labeled “A595” . 2. Using the known concentrations of the standards, construct a STANDARD PLOT with absorbance (dependent variable, y-axis) versus concentration (independent variable, x-axis). 3. For each diluted sample, find the absorbance on the standard curve and record the corresponding concentration. 4. Multiply this value by the dilution factor to get “protein concentration (mg/ml) of fraction” . Then calculate the “averaged protein concentration of fraction” . 5. Multiply the averaged protein concentration of fraction by the total original volume of the fraction to get the amount of protein (mg) of each fraction. 6. Check the protein recovery: the sum of the protein in the three fractions (nuclear, mitochondrial, soluble) should approximate to the protein in the crude homogenate that was fractionated. Express the recovery in each fraction as a percent of the total protein.
