Course Syllabus Course Title: Calculus I Term and Year: Spring 2025 Term 1 Course and Section Number: MA 134 OL1 OL Course Description: Topics include limits, continuity, differentiation, applications, definition of the integral, and fundamental theorem of integral calculus. Learning Outcomes: Upon completion of this course, the student should be able to: 1. Evaluate limits graphically, algebraically, using the Squeeze Theorem, and with l’Hopital’s rule. 2. Evaluate derivatives using the limit definition of a derivative, basic rules of differentiation, implicit differentiation, logarithmic differentiation, and the Fundamental Theorem of Calculus. 3. Apply limits and derivatives to curve sketching, optimization problems, and related rates. 4. Write simple arguments using the Intermediate Value Theorem, the Extreme Value Theorem, and the Mean Value Theorem. 5. Evaluate integrals using Riemann Sums, the Fundamental Theorem of Calculus, and basic techniques of integration. Prerequisites: None Required Text: This course utilizes OER (Open Educational Resources) materials at no cost to learners. All required reading is available in the online course room. Other Materials: Calculator Course Requirements: Learners are expected engage with all learning materials, participate in activities, and submit all assessments. Attendance/Participation: All students are expected to log in to their courses regularly throughout the week to receive instruction, materials, and updates from the instructor. It is your responsibility to check in and submit your assignments, complete your discussion board postings, and finish quizzes and exams by the due dates. If you do not participate in the course, you will be counted absent. Simply logging in is not enough; you must submit/complete an assignment, post to a discussion board, or other similar assignment tasks to avoid being counted absent. Instructors are required to submit attendance the Monday following each week of class. This attendance is reported to the Financial Aid Department and may result in the loss of any financial aid refund you are expecting if you have not been participating in your courses. In addition, you will be administratively dropped from the course if you are reported absent a total of three weeks. Please note that completing the following actions will not count as present for this class: • Logging into Moodle • Reviewing Course Materials • Any email or phone contact with the instructor • Submitting work early. One assignment must be submitted during each week to be counted present. Late Policy: Late work will not be accepted. However, in exceptional situations and with proper official documentation (doctor’s note, police report, etc.) an extension may be granted. Grading/Evaluation: The undergraduate course grading scale is as follows: 90-100% A 75-79% C+ 60-64% D 85-89% B+ 70-74% C 59% and below F 80-84% B 65-69% D+ The overall course grade will be calculated using the following weights: Discussion Forums (8): 5% Drag-and-Drop Key Terms Reviews (8): 5% Lecture Assignments (15): 10% Homework Assignments (15): 20% Quizzes (9): 20% Exams (3): 40%
Synthesis Exercise 1 LINC11 Winter 2025 January 22, 2025 Individual Submission due: Wednesday January 29, 23:59 on Quercus Group Submission due: Monday February 17, 23:59 on Quercus The following exercises must be completed by uploading a PDF document onto Quercus. Below you will find a set of data, some background information, and a set of discussion questions about this data and the topic. Considering the data, answer the questions posed to you in prose format, using diagrams and structure drawing where necessary for you discussion, or where explicitly asked by the questions. You first attempt at this problem is to be completed individually. The problems in the exercise should be chal- lenging, but possible to work out. For your individual attempt, please use only the data provided below. Your answers will be marked for effort and completion. Your second attempt will be completed in a small group with others. The second attempt should be a more in-depth discussion, taking into account the solutions that each in- dividual group member supplied. This group submission will be marked for depth of discussion and accuracy of the solutions and discussion provided. 1 Persian Ditransitives Persian is what’s known as a Differential Object Marking (DOM) language. This means that instead of a universally-appearing accusative case on direct objects (for example), objects are marked when they need to be differentiated from other material in the clause. The marker in Persian is the morpheme -rā,1 which appears after the noun phrase, e.g. ketāb ‘book’ becomes ketāb-rā . The cause and conditions under which this marking occurs are rela- tively complex; we won’t be solving that problem. In this exercise, we will begin by examining object marking in Ditransitives, with the intention of seeing what it can tell us about the vP in the language. The first important observation is that, like many other languages, there are two word orders for ditransitives. Persian is an SOV language, so we either see IO-DO-V or DO-IO-V orders: (1) a. Maryam barāye mā she’r mi-xun-e Maryam for 1pl poem ipfv-read-3sg ‘Maryam will read poetry to us.’ b. Maryam she’r-rā barāye mā mi-xun-e Maryam poem-ra for 1pl ipfv-read-3sg ‘Maryam will read a poem to us.’ c. ‘Ali be Sahar gol dād ‘Ali to Sahar flower give.pst.3sg ‘Ali gave flowers to Sahar.’ d. ‘Ali gol-rā be Sahar ‘Ali flower-ra to Sahar dād give.pst.3sg ‘Ali gave a flower to Sahar.’ Pay close attention to the distribution of -rā in these contexts. What do you observe? Now, formulate an hypothesis for the structure of the Persian ditransitive construction. To help with this, consider one more set of data: (2) a. har sag-i-rā be sāheb-aš dād-am every dog-indef-ra to owner-3sg.poss give.pst-1sg ‘I gave every dog to its owner.’ b. be sāheb-aš har sag-i-rā dād-am to owner-3sg.poss every dog-indef-ra give.pst-1sg ‘I gave every dog to its owner.’ c. *be sāheb-aš har sag dād-am to owner-3sg.poss every dog give.pst-1sg Draw your hypothesized structure for Persian ditransitive predicates. You should illustrate your hypothesis with at least two tree structures illustrating the sentences above. Now, discuss how you drew the conclusions that you came to. Is this structure different than the ones proposed for English by Larson and Jackendoff? In what ways? Use structural illustrations and examples from Larson and/or Jackendoff in your comparison between Persian and English.
FIT3173 Software Security Assignment-2 (SSB 2025) Total Marks 100 Due on Jan 31st, 2025, Friday midnight, 11:55:00 pm 1 Overview The primary learning objective of this assignment is to provide you with firsthand experience in exploiting SQL Injection, Cross-site Scripting and Cross-site Request Forgery vulnerabilities. Additionally, it aims to deepen your understanding of these vulnerabilities. This assessment does not require a specific virtual machine (VM) and can be executed on any operating system. You can utilize the same setup as the Lab06 and Lab07. 2 Submission For this assignment, you need to submit two files using a single submission link on Moodle: • A PDF file with relevant screenshots, and • a single video file containing the recording of you carrying out all tasks. Typeset your report into .pdf format (make sure it can be opened with Adobe Reader) and name it as the format: [Your Name]-[Student ID]-FIT3173-Assignment.pdf. All payloads, if required, should be embedded in your report. In addition, if a demonstration video is required, you should record your screen demonstration with your voice explanation. You can use this free tool to make the video:https://monash-panopto.aarnet.edu.au/ ; other tools, such as Zoom, are also fine. Important notes and penalties: • A part of the submitted video (at a corner) must clearly show your face at all times. Penalties may apply when that’s not the case. • Video demonstration should be a live exploitation of the vulnerabilities. • Late submissions incur a 5-point deduction per day. For example, if you submit 2 days and 1 hour late, that incurs 15-point deduction. Submissions more than 7 days late will receive a zero mark. • If you require extension or special consideration, refer to https://www.monash.edu/students/ admin/assessments/extensions-special-consideration. No teaching team mem-ber is allowed to give you extension or special consideration, so please do not reach out to a teaching team member about this. Follow the guidelines in the aforementioned link. • The maximum allowed duration for the recorded video is 15 mins in total. Therefore, only the first 15:00 mins of your submitted video will be marked. Any exceeding video components will be ignored. • If your device does not have a camera (or for whatever reason you can’t use your device), you can borrow a device from Monash Connect or Library. It’s your responsibility to plan ahead for this. Monash Connect or Library not having available devices for loan at a particular point in time is not a valid excuse. • You can create multiple video parts at different times, and combine and submit a single video at the end. Make sure that the final video is clear and understandable. • You can do (online) research in advance, take notes and make use of them during your video recording. You may also prepare exploit scripts in advance. But you cannot simply copy-paste commands to carry out the tasks without any explanations. Explanations (of what the code does) while completing the tasks are particularly important. • Zero tolerance on plagiarism and academic integrity violations: If you are found cheating, penalties will apply, e.g., a zero grade for the unit. The demonstration video is also used to detect/avoid plagia- rism. University policies can be found at https://www.monash.edu/students/academic/ policies/academic-integrity. 3 Web Application Vulnerabilities Q1: Complete three labs from PortSwigger Labs, one from SQL Injection, one from Cross-Site Scripting, and one from Cross-Site Request Forgery section. Please select labs designated as PRAC-TITIONER or EXPERT; APPRENTICE labs will not be accepted. You are permitted to utilize the solutions and demonstrations available on the PortSwigger website for assistance. However, please do not copy walkthroughs from the PortSwigger website. You will approach the labs as a penetration tester, simulating a real-world scenario where you exploit each target as if you were doing it for the first time. Your solution should include the logical steps that lead to the exploitation, which may not be covered in the walkthroughs on the PortSwigger website. [60 Marks] Record a video and write a report to answer the following questions for each lab. At the beginning of each lab recording, please state your name, student ID, and the name of the lab you are solving; no marks can be awarded without this information. 1. How did you identify the vulnerability? (5 Marks) 2. Which payload was chosen for exploitation and why? (5 Marks) 3. What an attacker could achieve using the vulnerability? (5 Marks) 4. How the vulnerability can be mitigated? (theoretically, no demonstration is required) (5 Marks) The video submission must demonstrate solving the lab, addressing the questions outlined above. In case time runs short during the video, you may use the report to address any unanswered ques-tions, making references to relevant sections of the video. However, it is important that the video includes, at a minimum, a demonstration of the lab. The report does not need to be in detail, it should briefly address the mentioned questions, i.e. it can contain one or two-line answer for each question, payloads, important screenshots (if necessary) and the video link(s). The marks mentioned above are for the videos and report combined. The word limit for each sub-question is 200 words, i.e. maximum 800 words are allowed for Q1 per lab. Q2: Download the Q2.html file from Moodle. Assume you are browsing monash.edu, and it is hypothetically vulnerable to various web attacks (although it is not). While navigating monash.edu, assume you open another tab in the same browser, and visit attacker.com (as-suming attacker convinced you to do that). You click the Submit button on the attacker.com webpage, which contains Q2.html, initiating attacks on monash.edu. Examine Q2.html (you can open the file in the browser and intercept the request in BurpSuite if desired) and respond to the following questions. No video is required for this question. The word limit for each sub-question is 200 words, i.e. maximum 600 words are allowed for Q2. [20 Marks] 1. Which vulnerability/vulnerabilities attacker.com is trying to exploit on monash.edu? (please explain the scenario outlining how this exploitation could occur) (10 Marks) 2. If successful, what is the consequence of the attack(s)? (5 Marks) 3. What mitigation(s) would you suggest for monash.edu to counter attack(s) launched by attacker.com? (5 Marks) Note: The parameter values in the HTML file are URL encoded. Q3: Assume you visit monash.edu and it tries to talk to lms.monash.edu, the browser issues an OPTIONS method to lms.monash.edu and gets a response, below is the HTTP request and its response: OPTIONS /doc HTTP/1.1 Host: lms.monash.edu User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.14; rv:71.0) Accept: text/html,application/xhtml+xml,application/xml Accept-Language: en-us,en;q=0.5 Accept-Encoding: gzip,deflate Connection: keep-alive Origin: monash.edu Access-Control-Request-Method: POST Access-Control-Request-Headers: x-requested-with HTTP/1.1 204 No Content Date: Mon, 01 Dec 2008 01:15:39 GMT Server: Apache/2 Access-Control-Allow-Origin: * Access-Control-Allow-Methods: POST, GET, OPTIONS Access-Control-Allow-Headers: x-requested-with Access-Control-Allow-Credentials: true Access-Control-Max-Age: 86400 Vary: Accept-Encoding, Origin Keep-Alive: timeout=2, max=100 Connection: Keep-Alive Explain the Cross-Origin Resource Sharing (CORS) HTTP headers in the above HTTP request and response. Please avoid listing each header with an explanation; instead, gather the key information and present it in a concise paragraph. Would browser change future requests based on the above HTTP response? No video is required for this question. The word limit for Q3 is 300 words. [10 Marks] 4 Report Completion and Quality of Presentation [10 Marks] Marks are allocated to the quality and clarity of presentation in the report and the video.
Overview This individual assessment task requires you to search, prepare and participate in a series of job ready activities. You will be finding a suitable job advertisement in the business environment, setting up your digital ePortfolio using the software Folio by Portfolium and participating in a job interview task. The purpose of this assessment is for you to obtain an understanding of what opportunities there will be available for you when you graduate, to see how your degree can help you work in areas both within and outside what you may think of as 'business', and to prepare you for the job hunting process. In this course you will undertake an assessment journey which comprises of three parts. This assessment task is your first part of your three part assessment journey. Learning Outcomes The targeted Course Learning Outcomes (CLOs) for this assessment are: CLO1: Explain how modern businesses operate and how they respond to different challenges CLO4: Apply digital literacy to navigate practical situations in modern business environment CLO5: Investigate and use an ePortfolio to digitally document achievements, capability development and evidence of learning outcomes for the purpose of future careers and employability. Marking Criteria This assessment will measure your ability to: Explore the range of professional and enterprise opportunities related to business. Articulate and explain your knowledge on how modern businesses operate and how they respond to different challenges. Demonstrate employability, 21st century skills and enterprise capabilities. Your Task Job interviews can be a complicated process, so your task will prepare you for that experience. You will start by finding a suitable job and then writing a small brief (250-350 words) about why you are interested in this job. Most job applications require this, so try to be honest and succinct. After that, you will sit for your job interview. Working individually, you will be required to complete three (3) key tasks: Find a job advertisement following a set of search guidelines and suitability. In 250-350 words tell us what interested you about this job. You are required to search and find a suitable job advertisement within the business environment using, LinkedIn, reputable online job search portals (ie Seek) or the RMIT career centre job boards and career resources. Your job advertisement needs to follow the below guidelines: Full time. Suitable skill level for a university business graduate. Anywhere in the world and in any industry. Write 250-350 words on why you chose this job. Please note that you will not be reaching out to this organisation directly or applying for this job advertisement. This is a simulated activity only. Create a digital ePortfolio tailored to your profile. This video shows you how to access it: An e-portfolio is a collection of digital artefacts curated by an individual student to showcase their coursework, projects, work-integrated learning and other relevant activities and achievements. Your ePortfolio is a working document throughout your studies here at RMIT. You will continuously add to your ePortfolio as part of your studies within the Bachelor of Business. This assessment task requires you to create your account on Portfolium and start the initial set up and input the below key elements within your ePortfolio. Your task will be to complete the following in your ePortfolio: Register your account. Complete your profile including uploading your photo, banner and tagline. Complete the overview. Input your education. The following tasks are optional, you don't need them for the assignment, but may choose to add them if you want to: Add your skills (if any). Provide a brief introduction (optional). Add your accomplishments (optional, if any). Add in any work experience (optional, if any). Add in any volunteer opportunities (optional, if any). Add in any clubs, affiliations & programs (optional, if any). You may choose to have two completed certifications - LinkedIn learning or Credentials (note creds need to be added in via Badgr using the same email address) (optional, if any). Add connections (it could be with your peers) (optional). Connect with social media platforms: LinkedIn, Facebook, Twitter, Instagram, Github (optional). Create a project (optional, if any). All your learning resources on how to open and create an ePortfolio are found in your Canvas shell under the module: Course Toolkit Participate in a job interview task and respond to (3) three pre-determined questions. A guide to using the STAR technique to answer interview questions can be found here. The questions that you must answer are: Question 1 Question 1: (200 word answer) What motivates you to work hard and achieve your goals? Can you give an example of a time when you were incentivised to work harder than usual? Question 2 Question 2: (200 word answer) Can you describe a situation where you had to make a difficult decision between two options? How did you weigh the costs and benefits of each option? Question 3 Question 3: (200 word answer) Thinking about your prospective employer, what are two key factors likely to impact the business environment within which your prospective employer operates in the next 5 years? If your prospective employer is a private business organisation, explain how each of these two factors impact the market demand/supply for the goods and/or service the business produces. Alternatively, if your prospective employer is a public sector organisation or not-for-profit organisation then explain how the two factors you have identified are likely to influence the work undertaken by the organisation. Submission Details This assessment requires you to submit one (1) document electronically in Canvas. All you need to do is open a Word document and add four headings. Under the first heading (call it Job), copy and paste a screenshot of the job advertisement. Under the screenshot, tell us in 250-350 words what interested you about this job. Under the second heading (call it ePortfolio), copy and paste a screenshot of your ePortfolio's first page. Under the third heading (call it Interview), provide your answers to the interview questions. Under the fourth heading, write down any reference that you may have used excluding lecture notes. Then, just convert this file to a PDF and upload it on to Canvas as a submission. Questions? If you have any questions, check the discussion board to see if they have already been answered. If not post your question or alternatively ask your teacher in your lectorials.
Design and Conduct of Observational Epidemiological Studies 3 Credits P8438 COURSE DESCRIPTION As a basic science of public health, epidemiology is responsible for identifying causes of disease that can guide the development of rational public health policies. The accuracy of the information provided by epidemiologic studies is therefore of central concern. Epidemiologic methods are the tools we use to make valid causal arguments. This course builds upon the methods introduced in the Core (or P6031 and P6400). The primary objective is to provide students with the basic tools necessary to conceptualize the design of, and interpret the results from, observational epidemiologic studies. COURSE LEARNING OBJECTIVES By the time you complete this course, you should be able to: • Articulate the relationship between association and causation • Apply causal concepts to the design and interpretation of epidemiologic studies • Calculate and interpret basic measures of association • Develop testable research hypotheses from a causal theory • Recognize and explain the effects of non-exchangeability • Distinguish among the sources of non-exchangeability • Choose study designs appropriate for specific research questions • Identify sources of, and methods to avoid, invalidity in epidemiologic research • Relate these sources of invalidity to the definition of a cause • Estimate the likely direction and magnitude of non-exchangeability in specific studies • Test research hypotheses using stratification, standardization, and logistic regression • Interpret logistic regression output to address causal questions • Define all the terms presented in the weekly glossaries • Critically evaluate the limitations of current epidemiologic methods • Work efficiently and productively in a team setting. ADVANCED PREPARATION The prerequisites for this course are either the Quantitative Methods Core (P6031) or both Introduction to Biostatistics (P6103/4) and Principles of Epidemiology (P6400). Students entering this course are assumed to be able to: • Calculate basic measures of association between exposures and disease • Interpret data in 2 by 2 tables • Identify major epidemiologic study designs • Define confounding, selection bias and information bias (aka measurement error). COURSE REQUIREMENTS Class Norms A goal of this class is to work in teams to have open and robust discussions of the course material. Each team will discuss and develop team norms, which we will synthesize into class norms, to help create an environment where vigorous intellectual arguments can take place. AI Policy Academic integrity is a core value at Mailman. For this reason, the use of generative artificial intelligence (AI) sites, (for example, but not only, Chat GPT) to complete an assignment or exam is not permitted unless the course instructor has provided clear written instruction about the use of generative AI. Use of generative AI to complete an assignment or exam without written instruction from the course instructor will be regarded as the same as receiving unauthorized assistance from another person and can be reported as an academic integrity violation. Required Course Materials Savitz, David A. and Wellenius, Gregory A. (2016) Interpreting Epidemiologic Evidence: Connecting Research to Applications. The textbook is available for purchase at the bookstore. The complete text is also available online through Columbia’s library, at this link: https://academic.oup.com/book/8266
CS909/CS429 Data Mining 2025 Assignment 1: Classification Your mission, should you choose to accept it, is to craft a classic machine learning solution for an object recognition task. Picture this: each object is a 28x28 pixel image. You'll get these images as 'flattened' 784-dimensional vectors, each tagged with a label (+1 or -1). Data Sources: Training Data (Xtrain): Rows of images for you to train your model. Training Labels (Ytrain): The label of each image. Test Data (Xtest): More rows of images for you to test your model's savvy. The training data (with labels) and test data (without labels) are available to you at the URL: https://github.com/foxtrotmike/CS909/tree/master/2025/A1 You can load the data with np.loadtxt. �� Submission Guide: Whip up a SINGLE Python Notebook containing all your code and answers. Make sure it includes: 1. A declaration (at the beginning of your submission) of whether you have used any AI tools like ChatGPT for your work and outline in 2 lines the intention behind its use. You are permitted to use such tools as long as you declare them keeping Warwick’s values and academic integrity as a priority. 2. All prediction metrics, presented neatly. 3. The output of every cell executed, so markers can verify your work. 4. A summary table of performance metrics to spotlight the star model. 5. Stick to these libraries: sklearn, numpy, pandas, scipy. If you explore beyond these, include installation code (!pip install xxx). 6. Submit your solution as a single Ipython Notebook through Tabula, complete with comments explaining your code. 7. Also, turn in a prediction file for the test data, formatted as prescribed. �� Important: No recycling old solutions, please! This year's dataset is a whole new game compared to previous years, demanding fresh answers. Question No. 1: (Exploring data) [10% Marks] Start by loading the training and test data. Once you have it ready, let's explore with these questions: i. Dataset Overview a. How many examples of each class are in the training set? And in the test set? b. Does this distribution of positive and negative examples signify any potential issues in terms of design of the machine learning solution and its evaluation? If so, please explain. ii. Visual Data Exploration a. Pick 10 random objects from each class in the training data and display them using plt.matshow. Reshape the flattened 28x28 arrays for this. What patterns or characteristics do you notice? b. Do the same for 10 random objects from the test set. Are there any peculiarities in the data that might challenge your classifier's ability to generalize? iii. Choosing the Right Metric Which performance metric would be best for this task (accuracy, AUC-ROC, AUC-PR, F1, Matthews correlation coefficient, mean squared error etc.)? Define each metric and discuss your reasoning for this choice. iv. Benchmarking a Random Classifier Imagine a classifier that produces a random prediction score in the range [-1,+1] for a given input example. What accuracy would you expect it to achieve on both the training and test datasets? Show this through a coding experiment. v. Understanding AUC Metrics for Random Classifier What would be the AUC-ROC and AUC-PR for a random classifier in this context? Again, support your answer with a code and discuss the consequences. Question No. 2: (Nearest Neighbor Classifier) [10% Marks] Perform. 5-fold stratified cross-validation (https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html) over the training dataset using a k-nearest neighbour (kNN) classifier and answer the following questions: i. Start with a k = 5 nearest neighbour classifier. Define and calculate the accuracy, balanced accuracy, AUC-ROC, AUC-PR, F1 and Matthews Correlation Coefficient for each fold using this classifier? Show code to demonstrate the results. Calculate the average and standard deviation for each metric across all folds and show these in a single table. As the KNN classifier in sklearn does not support decision_function, be sure to understand and use the predict_proba function for AUC-ROC and AUC-PR calculations or plotting. ii. Plot the ROC and PR curves for one fold. What are your observations about the ROC and PR curves? What part of the ROC curve is more important for this problem and why? Question No. 3: [20% Marks] Cross-validation of SVM and RFs Use 5-fold stratified cross-validation over training data to choose an optimal classifier between: SVMs (linear, polynomial kernels and Radial Basis Function Kernels) and Random Forest Classifiers. Be sure to tune the hyperparameters of each classifier type (C and kernel type and kernel hyper-parameters for SVMs, the number of trees, depth of trees etc. for the Random Forests etc). Report the cross validation results (mean and standard deviation of accuracy, balanced accuracy, AUC-ROC and AUC-PR across fold) of your best model. You may look into grid search as well as ways of pre-processing data (https://scikit-learn.org/stable/modules/preprocessing.html ) (e.g., mean-standard deviation or standard scaling or min-max scaling). i. Write your strategy for selecting the optimal classifier. Show code to demonstrate the results for each classifier. ii. Show the comparison of these classifiers in a single consolidated table. iii. Plot the ROC curves of all classifiers on the same axes for easy comparison. iv. Plot the PR curves of all classifier on the same axes for comparison. v. Write your observations about the ROC and PR curves. Question No. 4 [20% Marks] PCA i. Reduce the number of dimensions of the training data using PCA to 2 and plot a scatter plot of the training data showing examples of each class in a different color. What are your observations about the data based on this plot? ii. Reduce the number of dimensions of the training and test data together using PCA to 2 and plot a scatter plot of the training and test data showing examples of each set in a different color (or marker style). What are your observations about the data based on this plot? iii. Plot the scree graph of PCA and find the number of dimensions that explain 95% variance in the training set. iv. Reduce the number of dimensions of the data using PCA and perform. classification. You may want to select different principal components for the classification (not necessarily the first few). What is the (optimal) cross-validation performance of a Kernelized SVM classification with PCA? Remember to perform. hyperparameter optimization! Question No. 5 Optimal Pipeline [20% Marks] Develop an optimal pipeline for classification based on your analysis (Q1-Q4). You are free to use any tools or approaches at your disposal. However, no external data sources may be used. Describe your pipeline and report your outputs over the test data set. (You are required to submit your prediction file together with the assignment in a zip folder). Your prediction file should be a single column file containing the prediction score of the corresponding example in Xtest (be sure to have the same order as the order of the test examples in Xtest!). Your prediction file should be named by your student ID, e.g., u100011.csv. Question No. 6 Another classification problem [20% Marks] Using the data given to you, consider an alternate classification problem in which the label of an example is based on whether it is a part of the training set (label = -1) or the test set (label = +1). Calculate the average and standard deviation of AUC-ROC using 5-fold stratified cross-validation for a classifier that is trained to solve this prediction task. i. What does the value of this AUC-ROC tell you about any differences between training and test sets? Show code for this analysis and clearly explain your conclusions with supporting evidence. ii. How can you use this experiment to identify and eliminate any systematic differences between training and test sets? iii. Add random noise and random rotations to training set examples and then check if the AUC-ROC of this predictor changes. Clearly write and explain your observations.
Tutorial EG501V Computational Fluid Dynamics (AY 2023/24) Tutorial 5. Building a system matrix Two-dimensional fluid flow can be described by means of a stream function φ(x, y) that obeys the following elliptic PDE: Consider the two-dimensional contraction as shown in the figure. The left panel of the figure is a cartoon of the streamlines. The right panel defines the flow geometry and boundary conditions: at the inlet (left) and at the outlet (right); φ = 0 on the entire lower wall; φ = 1 on the upper wall. The figure also defines the discretization. We use dimensionless quantities throughout this problem. Q1 From the discretization (with Δx = 1 and Δy = 0.5 ) of the PDE, and from the boundary conditions determine the 10×10 matrix [A] and the 10-dimensional vector b such that the 10-dimensional vector φ containing φk , k = 1…10 satisfies [A]φ = b . Number the unknowns φk as indicated in the figure. Q2 The fluid velocity in x andy-direction ( ux and uy ) is related to the stream function according to and The solution to = [0.2322 , 0.2049 , 0.4781 , 0.4542 , 0.3513 , 0.3389 , 0.7359 , 0.7233 , 0.6800 , 0.6716]. Given this solution, determine ux in points 3 and 6, and determine uy in points 2 and 5 based on central differences approximations.
ECE 121DA Semiconductor Processing and Device Design – Winter 2024 Homework 1a – Implementing a simple Ion Implantation Schedule with manual calculations. You will use the results of this homework in homework 1b. 1. This exercise is designed to check your ability to do simple hand calculation estimates of an implant profile using the simplest (Gaussian) model that we discussed in class. · Starting substrate is p-type (100) silicon with a background doping of 1015 /cm3 Boron. · Calculate an implant profile with arsenic as the Implant species at an energy of 100 keV and an implant dose of 1014/cm2 · Calculate the modified profile after anneal of 900, 1000 and 1100 C (again using the simple model from class) use an anneal time of 30 min. a. Plot the 4 profiles (preferably on the same graph): as-implanted, anneal 900, anneal 1000 and anneal 1100. Provide an implant profile similar in style. to the one shown above. The bottom of your vertical scale should go no lower than a factor of 10 lower than your background p-type doping. Show that as a straight line. The top of your vertical scale should be the next decade (as shown above) The TSUPREM output will probably be unsatisfactory, you should export the data and use you favorite plotting program. Try to make it look similar to the above with a legend and clearly marked axes. The horizontal axis should only extend as far as it needs to show the pn junction. b. Use the simple formula from lecture to estimate the peak concentration from the tabulated dose and straggle of the implant and compare this to the calculated as-implanted profile in a. c. Estimate the depth of the pn-junction. The pn junction will be where the arsenic doping is the same as the background doping. d. What is the sheet resistance of the n-type layer? Estimate by hand and also have TSUPREM calculate it. e. You will use this data to compare to simulation next week.
CSCI 4041 Algorithms and Data Structures - Spring 2025 Homework 1 - Asymptotic Runtime Due Date: Friday, February 7, 2025 by 11:59pm. Problem H1.1: Theory - Answer the following questions. You should prove these formally using definitions and theorems from the textbook or class (e.g. Definitition of Big-O, Big-Ω, Big-Θ, Asymptotic Limit Theorem, etc...). (a) Prove: ∀a ∈ R, a > 1, loga n = Θ(lgn) (*Practice Problem*) (b) Prove: f = Θ(g) if and only if f = O(g) and f = Ω(g). (*Practice Problem*) (c) Let f(n) = 2n2 + 7n − 1. Show that f = O(n3 ). (*Practice Problem*) (d) Prove: n+1/n3(2+sinn) + 7n + 3 = Θ(g(n)), for some function g(n). Problem H1.2: Runtime - Give Θ(f(n)) complexity for the following examples. Justify your answers. All lines should be assumed to take constant time (including calls to other functions like print( . . .), √3x, log( . . .), etc...). (a) i = 1 while i >> A = [1 . 5, 2 . 5, 3, 1 . 1, 7] >>> print(closest_pairs(A,3)) # Output: [[1 . 5, 1 . 1], [2 . 5, 3], [2 . 5, 1 . 5]] • Example 2 >>> A = [1, 2, 3, 4] >>> print(closest_pairs(A,2)) # Possible Output: [[1, 2], [3, 2]] # Possible Output: [[3, 4], [1, 2]] # Possible Output: [[2, 3], [4, 3]] # Many other permutations are valid . . . (*Practice Problem*) Analyze the runtime of your algorithm. How might you change your algorithm if you only needed to find the closest pair? (i.e. the k=1 case) Can your new k=1 case be more efficient than the original algorithm?
Economics 152 Week 3 Practice Questions Winter 2025 1. Briefly describe three empirical studies of timing gaming. 2. Benson’s (2015) study of timing gaming showed that sales managers (who did not engage in sales themselves) were able to manipulate the sales of their subordinates to maximize their own bonuses. Describe how these sales managers accomplished this. 3. Imagine you are a car salesperson with a bonus contract that pays you $2000 in a month if you sell less than eight cars, and $3000 if you sell eight or more. If you expect to sell six this month and ten next month, discuss how you can game this system to raise your average monthly pay from $2500 to $3000. Illustrate using a diagram. (Hint: draw this salesperson’s reward schedule in a diagram like Figure 5.2 or 5.3 in the text, then use the same diagrammatic ‘trick’ to find expected compensation. 4. The following six statements pertain to the multi-task principal-agent problems. Please decide whether they are TRUE or FALSE, and explain your answer: Note: If any part of a statement is false, you should consider the entire statement as being false. a) As long as agents are risk neutral, multi-task principal-agent problems pose no special problems for firms and workers. The optimal contract is simply to set b=1 for every observable task the agent performs, essentially treating each task as a separate ‘mini-job’ . b) Countrywide Financial Corporation attached strong financial incentives to maximizing the dollar amount of mortgage lending done by its originators, with disastrous consequences for the company. We can think of Countrywide’s problem as a multi-task principal-agent problem because its originators’ actions affected not only the total amount of lending that was done, but also its quality, --i.e. the chances the loans would be repaid. As predicted by simple economic models, Countrywide’s originators neglected these other objectives when the quantity of output was so highly rewarded. c) A small business’s website is useful only if (a) it is attractive and well designed, and (b) it is highly ranked in local customers’ search results. Thus, web site design and search engine optimization (SEO) are complementary tasks to the owner of this business. (SEO makes a site more visible to search engines in a company’s target markets.) d) Nicolle works for a small business as its general-purpose tech wizard. She is completely indifferent as to which tech tasks she performs during a work day. If the company attaches financial rewards to some but not all of Nicolle’s tasks, we would expect her to change her focus toward the rewarded tasks dramatically. This is because the tasks are substitutes to Nicolle. e) When agents perform. multiple tasks and only some of those tasks can be incentivized by the principal, some economic theorists have argued that zero financial incentives, (i.e. b=0) is the socially efficient contract. f) In recent years, CEO pay packages have included a larger share of stock options, to incentivize CEOs to pay more attention to a company’s long term viability and profitability. 5. Multi-Task Principal Agent Problems: An Example Suppose you are the manager of a small pizza store, and you are thinking of ways to best allocate tasks and incentivize your two employees. You have 4 tasks that you need your employees to complete each day: Task 1: Arriving early to open the store, so that the first customers can be served right at the advertised opening time. Task 2: Cooking each pizza with care, so it is tasty and attractive for all customers. Task 3: Operating the cash register so it balances to the penny at the end of the day. Task 4: Cleaning up the entire shop (including the kitchen) at the end of the day. Since your schedule only allows you to check in on your employees at the end of the day, you can always observe whether the store has been cleaned and the cash register is balanced (Tasks 3 and 4), but you do not observe whether Tasks 1 and 2 were performed adequately. As the manager, you have two decisions to make: • Job design: How to assign the four tasks to your two employees. (Each employee can only do two tasks) • Incentivization: Whether or not to attach significant financial rewards to adequate task performance. Financial rewards can only be assigned to the observable tasks (tasks 3 and 4) because you can’t see how well the other two tasks were performed. For each of the following statements, please indicate whether it is TRUE or FALSE. You should treat each question individually (i.e. any assumptions in one question do not carry over into another) a) Suppose you’ve decided not to incentivize any of the tasks. Then your profits will not depend on how you assign the tasks to the employees. b) Suppose you’ve decided to institute a high cash penalty (losing half the day’s pay) if the cash register doesn’t balance exactly or the store is dirty at the end of the day (i.e. if tasks 3 and 4 are poorly performed). In this case Milgrom’s theory of multi-task incentives suggests that you should assign the observable tasks (3 and 4) to one worker, and the unobservable tasks (1 and 2) to the other worker. c) Suppose you’ve assigned tasks 3 and 4 to one worker, and tasks 1 and 2 to the other worker. In this case, Milgrom’s theory of multi-task incentives suggests that you can attach strong financial incentives to tasks 3 and 4. d) Suppose you’ve assigned tasks 1 and 3 to one worker, and tasks 2 and 4 to the other worker. In this case, Milgrom’s theory of multi-task incentives suggests that you should attach strong financial incentives to the two observable tasks (3 and 4). That way, at least one aspect of each worker’s job is incentivized, which is better than no incentives at all. e) Suppose that workers are indifferent between tasks 1 and 4: Arriving early to open and staying late to clean requires the same amount of effort and time, and the workers don’t care whether they supply that effort early or late in the day. Suppose also that workers who are in the store during the day prefer to do a mix of cooking and cashiering (tasks 2 and 3), rather than doing only one of these activities (because switching makes the job more interesting). In this case, the multi-task incentive problem will be more severe for a worker who’s assigned to tasks 2 and 3, than for a worker who’s assigned to tasks 1 and 4.
STATS 763 STATISTICS Advanced Regression Methodology FIRST SEMESTER, 2019 1. (24 marks; 4 each) Define or explain briefly in the context of this class (a) facetting (b) ridge regression (c) collider (d) apparent error (e) competing risks (f) sparse matrix 2. (24 marks) You have data from a series of randomised clinical trials (experiments) of related drugs to prevent heart attacks: that is, people are recruited into an experiment and then randomly assigned to one of two treatments. Y is a binary variable indicating heart attack, D is a factor variable with m + 1 levels 0, 1, . . . , m indicating which drug the person received (with D = 0 for no treatment), and G is a factor variable with k levels 1, 2, . . . , k indicating which trial the person was in. You fit two models model1
CSC 485H/2501H: Computational linguistics, Fall 2024 Assignment 3 Overview: Symbolic Machine Translation In this assignment, you will learn how to write phrase structure grammars for some different lin- guistic phenomena in two different languages: English and Chinese. You can use the two grammars to create an interlingual machine translation system by parsing in one, and generating in the other. Don’t panic if you don’t speak Chinese, and also don’t cheer up yet if you can speak the language — it won’t give you much of an advantage over other students. A facility with languages in general will help you, as will the ability to learn and understand the nuances between the grammars of two different languages. In particular, you will start by working on agreement. Then, you will need to analyse the quantifier scoping difference between the two languages. TRALE Instructions The instructions to setup and launch TRALE on teach.cs can be found in: https://www.cs.toronto.edu/~niu/csc485/trale/setup/ 1. Agreement: Determiners, Numbers and Classifiers [10 marks] English expresses subject–verb agreement in person and number. English has two kinds of number: singular and plural. The subject of a clause must agree with its predicate: they should be both singular or both plural. However, the number of a direct object does not need to agree with anything. (1) A programmer annoys a dolphin. (2) Two programmers annoy a dolphin. (3) * Two programmers annoys two dolphins. (4) * A programmer annoy two dolphins. Chinese, on the other hand, does not exhibit subject–verb agreement. As shown in the examples below, most nouns do not inflect at all for plurality. Chinese does, however, have a classifier (CL) part of speech that English does not. Semantically, classifiers are similar to English collective nouns (a bottle of water, a murder of crows), but English collective nouns are only used when describing collectives. With very few exceptions, classifiers are mandatory in complex Chinese noun phrases. Different CLs agree with different classes of nouns that are sorted by mostly semantic criteria. For example, (xuesheng) student is a person and an occupation, so it should be classified by either (ge) or (ming) and cannot be classified by the animal CL (zhi). However, the rules of determining a noun’s class constitute a formal system that must be followed irrespective of semantic similarity judgements. For example, while wolves and sheep are both animals and can both be classified by the animal CL (zhi), (lang) wolf can take another classifier, (pi). (5) yi ge xuesheng one ge-CL student (6) liang ge xuesheng two ge-CL student (7) san ge xuesheng three ge-CL student (8) * san xuesheng three student (9) * san zhi xuesheng three zhi-CL student (10) yi zhi yang one zhi-CL sheep (11) liang zhi yang two zhi-CL sheep (12) san zhi yang three zhi-CL sheep (13) * san pi yang three pi-CL sheep (14) * san ming yang three ming-CL sheep You should be familiar by now with the terminology in the English grammar starter code for this question. The Chinese grammar is fairly similar, but there is a new phrasal category called a classifier phrase (CLP), formed by a number and a classifier. The classifier phrase serves the same role as a determiner does in English. The two grammars below don’t appropriately constrain the NPs generated. You need to design your own rules and features to properly enforce agreement. English Grammar: Chinese Grammar: Rules: Rules: NP → Det N CLP → Num CL NP → Num N NP → CLP N VP → V NP VP → V NP S → NP VP S → NP VP Lexicon: Lexicon: a: Det yi one/a Num one: Num liang two Num two: Num san three Num three: Num student: N xuesheng student N students: N lang wolf N wolf: N yang sheep N wolves: N sheep: N zhuizhu chase V sheep: N kanjian see V see: V sees: V saw: V chase: V chases: V ge CL ming CL zhi CL pi CL chased: V Here is a list of all of the nouns in this question and their acceptable classifiers: • lang wolf: zhi; pi; • yang sheep: zhi • xuesheng student: ge, ming. (a) (6 marks) Implement one grammar for each language pursuant to the specifications above. English: q1_en.pl and Chinese: q1_zh.pl. Neither of your grammars need to handle embedded clauses, e.g., a student caught two wolves see a sheep. Similarly for Chinese, your grammar doesn’t need to parse sentences like example (15): (15) yi ming xuesheng kanjian liang pi lang zhuizhu yi zhi yang A student saw two wolves chase a sheep. For the Chinese grammar, code the lexical entries in pinyin (the Romanized transcriptions of the Chinese characters). (b) (4 marks) Use your grammars to parse and translate the following sentences. Save and submit all the translation results in the .grale format. The results of sentence (16) should be named q1b_en.grale and the results of sentence (17) should be named q1b_zh.grale. (16) Two wolves saw one sheep (17) liang ge xuesheng zhuizhu san zhi yang Operational Instructions • Independently test your grammars in TRALE first, before trying to translate. • Use the function translate to generate a semantic representation of your source sen- tence. If your sentence can be parsed, the function translate should open another gralej interface with all of the translation results. | ?- translate([two,wolves,catch,one,sheep]). • To save the translation results, on the top left of the Gralej window (the window with the INITIAL CATEGORY entry and all of the translated sentences listed),click File >> Save all >> TRALE format. • Don’t forget to close all of the windows or kill both of the Gralej processes after you finish. Each Gralej process will take up one port in the server, and no one can use the server if we run out of ports. 2. Quantifier Scope [30 marks] For this assignment, we will consider two quantifiers: the universal quantifier (every, mei) and the existential quantifier (a, yi). In English, both quantifiers behave as singular determiners. (18) A professor stole every cookie. (19) * A professor stole every cookies. (20) * A professors stole every cookie. In Chinese, both of these quantifiers behave more like numerical determiners. In addition, when a universal quantifier modifies an NP that occurs before the verb (such as with a universally quanti- fied subject), the preverbal operator (dou) is required. When a universally quantified NP occurs after the verb, the dou-operator must not appear with it. (21) Every professor stole a cookie. (22) A professor stole every cookie. (23) mei ge jiaoshou dou tou-le yi kuai binggan ∀ ge-CL professor DOU stole ∃ kuai-CL cookie (24) * mei ge jiaoshou tou-le yi kuai binggan ∀ ge-CL professor stole ∃ kuai-CL cookie (25) yi ge jiaoshou tou-le mei kuai binggan ∃ ge-CL professor stole ∀ kuai-CL cookie (26) * yi ge jiaoshou dou tou-le mei kuai binggan ∃ ge-CL professor dou stole ∀ kuai-CL cookie Quantifier Scope Ambiguity In lecture, we talked about different kinds of ambiguity. In many English sentences, no matter what the order of the quantifiers, there is a quantifier scope ambiguity. For example, the sentence every student takes a course has two readings: • (∃ > ∀) Every student takes a course. [The course is Math.] • (∀ > ∃) Every student takes a course. [Some students take Math and some students take History.] The symbol (∃ > ∀) means the existential quantifier outscopes the universal quantifier in a logical form. representation of the sentence. Figure 1: Beta Reduction. What should be the LF of S? We can write the semantics of the two sentences in their logical forms (LF) to distinguish the two readings: • ∃y.(course(y) Λ ∀x.(student(x) ⇒ take(x, y))) • ∀x.(student(x) ⇒ ∃y.(course(y) Λ take(x, y))) English sentences (27, 28) are scopally ambiguous no matter what the linear order of the quan- tifiers is. But in Chinese, a sentence is scopally ambiguous only when the universally quantified NP precedes the existential NP: (29) is ambiguous, but (30) is unambiguous. (27) Every student takes a course Ambiguous: ∃ > ∀, ∀ > ∃ (28) A student takes every course Ambiguous: ∃ > ∀, ∀ > ∃ (29) mei ge xuesheng dou huishangyi zhong kecheng ∀ ge-CL student DOU take ∃ zhong-CL course Ambiguous: ∃ > ∀, ∀ > ∃ (30) yi ge xuesheng huishang mei zhong kecheng ∃ ge-CL student take ∀ zhong-CL course Unambiguous: ∃ > ∀ How can we derive the LF of the two readings? We use a process called beta reduction. Recall the lambda-calculus notation: λx.x2 denotes a function that takes a variable x, and returns the square of its value (x2 ). After substituting the value for the bound variable x, we can reduce the function application in the body of the lambda term to a new expression. For example, applying 2 to λx.x2 will get us: λx.x2 (2) = 22 Figure 2: Beta reduction analysis of the sentence every student takes a course. This process is also known as beta reduction (denoted as β ). Note that beta reduction itself does not tell us that this equals 4. That is obtained by a subsequent process of arithmetic evaluation. But we can use beta reduction even if we don’t evaluate. We can also perform beta reduction on variables for functions. For example, applying in λF.F (2) to λx.x2 will yield: λF.F (2) (λx.x2 ) = (λx.x2 )(2) = 22 Now, let’s look at an example that uses beta reduction to compute the LF of a sentence. For ex- ample, as shown in figure 1, we know that the LF of the NP every student is λP. x.(student(x) P (x)) and the LF of the VP takes a course is λz. y.(course(y) Λ take(z, y)). What is the LF of every student takes a course? λP. x.(student(x) P (x))(λz. y.(course(y) Λ take(z, y))) β x.(student(x) λz. y.(course(y) Λ take(z, y))(x)) β x.(student(x) y.(course(y) Λ take(x, y))) Each step of repeatedly applying beta reduction to every subterm until we reach an irreducible statement is called beta normalisation. Figure 2 shows the complete analysis of the sentence every student takes a course. Familiarize yourself with every part of the analysis. But this only generates one of the two readings – the surface reading ( > ). We will use a technique called quantifier storage to capture the scopal ambiguity and make both readings available. Quantifier Storage If quantifier scoping is a semantic effect, how do we represent it in syntax? When there is no ambiguity, keeping track of the quantifier scope is pretty straightforward. To keep track of and resolve scope ambiguities, we will use a list called a quantifier store. The idea behind QSTORE is that, instead of consuming all of the LF components right away, we can choose to keep them in QSTORE and apply them later. Figure 3: Quantifier Storage. Storing the quantifier at (1), and retrieve it later at (2). Let’s go back to the example, every student takes a course (figure 3). We first store the LF of the NP a course at (1) and replace the LF of the NP with a placeholder λF.F (z). The variable z in this expression is a free occurrence, and it is the same variable as the z in the store and in the LF of the sentence (the free occurrences of z are highlighted in red). We retrieve the logical form from the store at (2). The retrieval process consists of three steps: 1. First, we construct a function λz.LS , where LS is the current LF, and z is the variable paired in the QSTORE entry. In our particular case, this will yield λz. ( x, student(x) take(x, z)). 2. Then, we apply this function to the LF from the QSTORE entry. 3. Finally, we beta normalise. Using beta normalisation, we obtain the second reading of the sentence. λG. y.(course(y) Λ G(y))(λz. ( x, student(x) take(x, z))) β y.(course(y) Λ (λz. ( x.student(x) take(x, z))(y)) β y.(course(y) Λ ( x.student(x) take(x, y))
FN3142 ZA Quantitative Finance Question 1 Consider the following MA(2) process: zt = ut + Q1ut-1 + Q2ut-2 , where ut is a zero-mean white noise process with variance σ 2 . (a) Calculate the conditional and unconditional means of zt , that is, Et [zt+1] and E [zt]. [20 marks] (b) Calculate the conditional and unconditional variances of zt , that is, V art [zt+1] and Var [zt]. [30 marks] (c) Derive the autocorrelation function of this process for all lags as functions of the param- eters Q1 and Q2 . [50 marks] Question 2 (a) What are the two main problems in multivariate volatility modelling? Explain them briefly. [25 marks] (b) Describe Bollerslev (1990)’s constant conditional correlation (CCC) model. [25 marks] (c) Describe what tests you can use to test for volatility clustering. [25 marks] (d) What information criteria can be used (as measures of performance) that penalise models for using a larger number of parameters? Describe their link with the log-likelihood function and the number of parameters. [25 marks] Question 3 (a) Describe how one can determine Value-at-Risk (VaR) using models based on the normal distribution, and critically assess such procedure. [60 marks] (b) Consider a position consisting of a $20,000 investment in asset X and a $20,000 in- vestment in asset Y. Assume that returns on these two assets are i.i.d. normal (Gaussian) with mean zero, that the daily volatilities of both assets are 3%, and that the correlation coefficient between their returns is 0.4. What is the 10-day VaR at the Q = 1% critical level for the portfolio? [40 marks] Question 4 (a) What does serial correlation mean? Explain. [10 marks] (b) Suppose you have a fair coin, that is, the probability of seeing a ‘head’ is always equal to one-half, and coin tosses are independent of each other. Let xt take the value -1 or 1 depending on whether the tth coin toss came up heads or tails. Consider now a process yt that is given by yt = xt + xt-1 . Calculate the autocorrelations of the process yt. [30 marks] (c) Describe the three types of market efficiency as defined by Roberts (1967). [30 marks] (d) Does weak-form. market efficiency imply strong-form. market efficiency? What about the reverse? Explain. [10 marks] (e) Under the Efficient Market Hypothesis (EMH), what should be the correlation coefficient between stock returns for two non-overlapping time periods? Can the process yt from part (b) describe a return process under EMH? Explain. [20 marks] FN3142 ZB Quantitative Finance Question 1 Consider a process Yt that resembles an MA(1) process except for a small change: Xt = ut + (-1)t δut-1 , where ut i~.i.d N(0, σu(2)) and 0 < δ < 1 constant. Hint: (-1)t = 1 if t is an even number, and -1 if t is odd. (a) Find Et [Xt+1], Et [Xt+2], and E[Xt]. Pay attention to t being odd or even. [30 marks] (b) Find Var[Xt]. [20 marks] (c) Derive the autocovariance for lags 1 and 2. [30 marks] (d) Explain what covariance stationarity means, and relate it to your findings in parts (a), (b), and (c). [20 marks] Question 2 There are three companies, called A, B, and C, and each has a 4% chance of going bankrupt. The event that one of the three companies will go bankrupt is independent of the event that any other company will go bankrupt. Company A has outstanding bonds, and a bond will have a net return of r = 0% if the corporation does not go bankrupt, but it will have a net return of r = -100%, i.e., losing everything invested, if it goes bankrupt. Suppose an investor buys $1000 worth of bonds of company A, which we will refer to as portfolio P1 . Suppose also that there exists a security whose payof depends on the bankruptcy of companies B and C in a joint fashion. In particular, if neither B nor C go bankrupt, this derivative will have a net return of r = 0%. If exactly one of B or C go bankrupt, it will have a net return of r = -50%, i.e., losing half of the investment. If both B and C go bankrupt, it will have a net return of r = -100%, i.e., losing the whole investment. Suppose an investor buys $1000 worth of this derivative, which is then called portfolio P2 . (a) Calculate the VaR at the α = 10% critical level for portfolios P1 and P2 . [30 marks] (b) Calculate the VaR at the α = 10% critical level for the joint portfolio P1 + P2 . [20 marks] (c) Is VaR sub-additive in this example? Explain why the absence of sub-additivity may be a concern for risk managers. [20 marks] (d) The expected shortfall ESα at the α critical level can be defined as ESα = -Et [RjR < -VaRα +1] , where R is a return or dollar amount. Calculate the expected shortfall at the α = 10% critical level for portfolio P2 . Is this risk measure sub-additive? [30 marks] Question 3 (a) Explain Black’s observation regarding the link between the stock returns and changes in volatility and provide a possible explanation for this efect. [25 marks] (b) Does a simple GARCH(1,1) model capture the leverage efect? Explain. [25 marks] (c) Describe two GARCH-type models that account for the leverage efect. Note: For full marks, write down the processes with equations and explain analytically how they work. [40 marks] (d) For both GARCH-type models you mentioned in part (c), discuss whether they nest the GARCH(1,1) model. [10 marks] Question 4 (a) What does serial correlation mean? Explain. [10 marks] (b) Suppose you have a fair coin, that is, the probability of seeing a ‘head’ is always equal to one-half, and coin tosses are independent of each other. Let xt take the value -1 or 1 depending on whether the tth coin toss came up heads or tails. Consider now a process yt that is given by yt = xt + xt-1 . Calculate the autocorrelations of the process yt. [30 marks] (c) Describe the three types of market efficiency as defined by Roberts (1967). [30 marks] (d) Does weak-form. market efficiency imply strong-form. market efficiency? What about the reverse? Explain. [10 marks] (e) Under the Efficient Market Hypothesis (EMH), what should be the correlation coefficient between stock returns for two non-overlapping time periods? Can the process yt from part (b) describe a return process under EMH? Explain. [20 marks]
6CCS3ML1 (Machine Learning) Coursework 1 (Version 1.6) 1 Overview For this coursework, you will have to implement a classifier. You will use this classifier in some code that has to make a decision. The code will be controlling Pacman, in the classic game, and the decision will be about how Pacman chooses to move. Your classifier probably won’t help Pacman to make particularly good decisions (I will be surprised if it helps Pacman win games, my version certainly didn’t), but that is not the point. The point is to write a classifier and use it. No previous experience with Pacman (either in general, or with the specific UC Berkeley AI imple- mentation that we will use) is required. This coursework is worth 10% of the marks for the module. Note: Failure to follow submission instructions will result in a deduction of 10% of the marks you earn for this coursework. 2 Getting started 2.1 Start with Pacman The Pacman code that we will be using for the coursework was developed at UC Berkeley for their AI course. The folk who developed this code then kindly made it available to everyone. The homepage for the Berkeley AI Pacman projects is here: http://ai.berkeley.edu/ Note that we will not be doing any of their projects. Note also that the code only supports Python 3, so that is what we will use.1 You should: (a) Download: pacman-cw1.zip from KEATS. (b) Save that file to your account at KCL (or to your own computer). (c) Unzip the archive. This will create a folder pacman Figure 1: Pacman (d) From the command line (you will need to use the command line in order to use the various options), switch to the folder pacman. (e) Now type: python3 pacman.py This will open up a window that looks like that in Figure 1 (f) The default mode is for keyboard control, so you should be able to play this game out using the arrow keys. Playing Pacman is not the object here — don’t worry if there is an issue with controlling Pacman using the keys, that can happen on some platforms — but you will need to run this code to do the coursework. So, if the code causes an error, get help. When you are tired of running Pacman, move on to the next section. 2.2 Code to control Pacman Now we work towards controlling Pacman by writing code. The file sampleAgents .py contains several simple pieces of code for controlling Pacman. You can see one of these run by executing: python3 pacman.py --pacman RandomAgent This is not a good player (it is just picking from the available actions at random), but it shows you a couple of things. First, you execute an agent that you write by using the --pacman option, followed by the name of a Python class. The Pacman code looks for this class in files called: Agents .py and, when it finds the class, will compile the relevant class. If the class isn’t in an appropriately named file, you will get the error: Traceback (most recent call last): File "pacman.py", line 679, in args = readCommand( sys .argv[1:] ) # Get game components based on input File "pacman.py", line 541, in readCommand pacmanType = loadAgent(options.pacman, noKeyboard) File "pacman.py", line 608, in loadAgent raise Exception(‘The agent ’ + pacman + ‘ is not specified in any *Agents .py . ’) Now open your favourite editor and look at sampleAgents .py. If you look at RandomAgent you will see that all it does is to define a function getAction(). This function is the only thing that is required to control Pacman. The function is called by the game every time that it needs to know what Pacman does — at every “tick” of the game clock — and what it needs to return is an action. That means returning expressions that the rest of the code can interpret to tell Pacman what to do. In the basic Pacman game, getAction() returns commands like: Directions .STOP which tells Pacman to not move, or: Directions .WEST which tells Pacman to move towards the left side of its grid (North is up the grid). However, for your coursework, you have to pass this direction to the function api.makeMove() first, just as the classes in sampleAgents .py do. sampleAgents .py contains a second agent, RandomishAgent. Try running it. RandomishAgent picks a random action and then keeps doing that as long as it can. 2.3 Towards a classifier For this coursework you’ll work from some skeleton code that is in the folder pacman-cw1. The file to look for is classifier.py which is used in classifierAgents .py. You will ONLY need to modify classifier.py and no other file. Two things to note about this: (a) The skeleton in classifier.py defines a class Classifier, and classifierAgents .py defines a class ClassifierAgent. When we mark your coursework, we will do so by running the ClassifierAgent class. If this doesn’t exist (or, similarly, if class Classifier doesn’t exist, because you decided to rename things), we will mark your code as if it doesn’t work. So make life easy for yourself, and use the classes and functions provided as the basis for your code. Again, you will ONLY need to modify / use the skeleton in classifier.py and no other file. We cannot accept code after the deadline has passed even iferrors are of accidental nature. (b) The ClassifierAgent class provides some simple data handling. It reads data from a file called good-moves .txt and turns it into arrays target and data which are similar to the ones you have used with scikit-learn. When we test your code, it will have to be able to read data in the same format as good-moves.txt, from a file called good-moves .txt. If it doesn’t, we will mark your code as not working. So make life easy for yourself and stick to the (admittedly, but intentionally, limited) data format that we have provided. To run the code in classifierAgents .py, you use: python3 pacman.py --pacman ClassifierAgent Note the difference in capitalisation between file name and class name. Now open your editor and take a look at the code for ClassifierAgent. There are six functions in it: (a) init () The constructor. Run when an instance of the class is created. Because the game doesn’t exist at this point, it is of limited use. (b) loadData() This is a simple utility. The data in good-moves .txt is stored as a string. We need it as an array of integers. This does the conversion. (c) registerInitialState() This function gets run once the game has started up. Unlike init() , because the game has started, there is game state information available. Thus it is possible for Pacman to “look” at the world around it. Right now this is the only function that is doing any real work. It opens the file good-moves .txt, and extracts the data from it, where data is parsed into the arrays data and target. These ar- rays are accessible from any function. (They are data members of the class ClassifierAgent.) (d) final() This function is run at the end of a game, when Pacman has either won or lost. (e) convertNumberToMove() Another simple utility. The data in good-moves .txt encodes moves that Pacman made in the past using integers. What you need to do is to produce moves of the form. Directions .NORTH since that is the format which the game engine requires. This function converts from one to the other in a way that respects the original conversion from moves to integers. (f) getAction() This function is called by the game engine every time step. What it returns controls what Pacman does. Right now it just returns Directions.EAST or a random move (see predict() in classifier.py). (The function also does some other stuff, but we will get to that later). 3 What you have to do (and what you aren’t allowed to do) 3.1 Write some code Your task in this coursework is to write a classifier using classifier.py which uses the data in good-moves .txt to control Pacman. By “control Pacman” we mean “select an action and return it in the function getAction (the code is already set up for you this way). However, because this is a module on machine learning, not a module on game programming, we are quite prescriptive about how you go about doing this: (a) Your code is only allowed limited access to information about the state of the game. What you are allowed to access is the information provided by: api.getFeatureVector(state) This returns a feature vector in the form of an array of 1s and 0s like this: [1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] This records details of whether there are walls, food, and ghosts in the squares around Pacman. You don’t need to know what each number means (though if you want to know, look in api.py). What you do need to know is that if your code uses any other information about the game to decide what to do, you won’t get any marks for the coursework. (b) Your code should use a classifier to make a decision, based on the information in features, to decide what to do. Thus the classifier should be trained using the information in self .data and self .target, and should predict an action when passed the data in features (again, this is already set up). (c) You are allowed to use a classifier from an external library such as scikit-learn. However, if you use a classifier from an external library, you will not get as many marks as if you write a classifier yourself. (For details on exactly how we will mark your code, see the marksheet on KEATS. Also ensure you read the coursework’s FAQs on KEATS.) (d) If you do code your own classifier, it does not have to be complicated. It could be as simple as a 1-nearest neighbour classifier. However, the more sophisticated the classifier, the more marks you will get. (Again, for details you should see the marksheet and FAQs on KEATS.) (e) To get full marks, your code has to run until either Pacman wins a game, or until Pacman gets eaten by a ghost (and loses a game). In other words, your code should not crash or otherwise fail while we are running it. Losing a game is not failing. In fact, from the point of view of marking, we don’t care if your Pacman wins, loses, gets a high score or a low score. We only care that your code successfully uses a classifier to decide what to do. 3.2 Things to know If you look in good-moves .txt, you will see that each line contains a feature vector like the one above, plus a final digit. (There are no brackets or commas, that is because good-moves .txt holds 3 Ok, that is not quite right. The code in getAction in the skeleton classifierAgent uses legal = api.legalActions(state) to get a set of the legal moves at every step. That is technically information about the game state, and it is both allowed, and sensible, since if you return an illegal action to the game engine, the game crashes. The code is already set up to use this. Using any other information is, however, forbidden. strings not arrays.) The first digits are indeed a feature vector, and the last digit encodes an action. When the data is read in by registerInitialState, the feature vector part is loaded into data, and the “action” is loaded into target such that the ith elements of data and target go together. The data was collected from code that played Pacman. (Indeed, from some code that won games of Pacman.) At each step, the feature vector and move were stored in good-moves .txt. And that is exactly why you can create a classifier from it. If you train a classifier on the good-moves data, then that classifier should be able to predict a sensible move given a new feature vector. Note that while the good-moves data is what we will test your code with (or rather it is one of the things we will test your code with), you may want to create some custom training data. To make that easy, we have provided TraceAgent (in the file traceagents.py). If you run this using: python3 pacman.py --p TraceAgent you will get the same keyboard controlled Pacman as you saw before, BUT one which outputs data on your move and the corresponding feature vector. This data is written to moves .txt. (If a file already exists with that name, it is over-written, so be careful.) 3.3 Limitations There are some limitations on what you can submit. (a) Your code should be in Python 3. Code written in a language other than that will not be marked. Code written in Python 2 is unlikely to run with the clean copy of pacman-cw1 that we will test it against. If it doesn’t run, you will lose marks. The reason for this is that we do not have the resources to deal with code written in multiple languages, and to ensure that we can run code written in Python 2. (b) Your code will be tested in the same environment as we have been using in the lab. That is the standard Anaconda Python 3 distribution, with scikit-learn also installed (and the scikit-learn distribution includes numpy). Code using libraries that are not in this collection may not run when we test it. If you choose to use such libraries and your code does not run when we test it, you will lose marks. The reason for this is that we do not have the resources to deal with setting up arbitrarily complex environments (with the possibility of libraries with arcane interactions) for every submission. (c) Your code must only interact with the Pacman environment by making calls through the version of api .py supplied in pacman-cw1 .zip. Code that finds other ways to access information about the environment will lose marks. The idea here is to have everyone solve the same task. (d) You are not allowed to modify any of the files in pacman-cw1 .zip except classifier.py. Similar to the previous point, the idea is that everyone solves the same problem — you can’t change the problem by modifying the base code that runs the Pacman environment. Also, your code will have to run against a clean version of the code in pacman-cw1 so you’ll just be making trouble for yourself. (e) You are not allowed to copy, without credit, code that you might get from other students or find lying around on the Internet. (This includes the use of code that was distributed as part of the module — if you use code from files other than classifier.py and classifierAgent .py without attribution, we will consider that to be plagiarism.) We will be checking. This is the usual plagiarism statement. When you submit work to be marked, you should only seek to get credit for work you have done yourself. When the work you are submitting is code, you can use code that other people wrote, but you have to say clearly that the other person wrote it — you do that by putting in a comment that says who wrote it. That way we can adjust your mark to take account of the work that you didn’t do. Please add any citations, descriptions, or whatever you want us to know in the python file. Please also ensure you familiarise yourselves with what constitutes plagiarism and collusion and how to avoid them (ensure you read the information on KEATS); e.g. copying large parts of code from others, even with attribution, is not allowed. We need to be able to assess your OWN contribution. (f) Your code must be based on using a classifier on the data in good-moves .txt. If you don’t submit a program that contains a recognisable classifier, you will lose marks. 4 What you have to hand in Your submission should consist of a single ZIP file. (KEATS will be configured to only accept a single file.) This ZIP file must include a single Python file (your code): classifier.py. The ZIP file must be named: cw1-- .zip Remember that we are going to evaluate your code by running your code by using variations on python3 pacman.py -p ClassifierAgent and we will do this in a vanilla copy of the pacman-cw1 folder, so the base class for your agent must be called ClassifierAgent and use class Classifier and the skeleton provided. To streamline the marking of the coursework, you must put all your code in one file, and this file must be called classifier.py (which we provide). Do not just include the whole pacman-cw1 folder. You should only include the one file that includes the code you have written. Do not modify any of the other files either when developing your code. Submissions that do not follow these instructions will lose marks. 5 How your work will be marked There will be three main components of the mark for your work: (a) Functionality We will test your code in classifier.py by running the classifierAgents .py file against a clean copy of pacman-cw1. As discussed above, for full marks for functionality, your code is required to run when we invoke the command: python3 pacman.py --p ClassifierAgent and run until the game is won or lost. Code that fails to meet these requirements will lose marks. We will also look at your code for evidence of the use of a classifier. Code that does not use a classifier will lose marks. Code that does not implement a classifier (that is, uses one from an external library like scikit-learn) will lose marks. Code that implements more sophisticated classifiers will get more marks. So, my example (above) of using a 1-NN classifier, which is about the simplest possible classifier, would not get as many marks as the implementation of a more sophisticated classifier. (b) Style. There are no particular requirements on the way that your code is structured but you should ensure it follows standard good practice in software development and will be marked accord- ingly. Remember that your code is only allowed to interact with the Pacman environment through api .py (the version in pacman-cw1), and is only allowed to use the environment information provided to the Classifier class. Code that does not follow this rule will lose marks. (c) Documentation All good code is well documented, and your work will be partly assessed by the comments you provide in your code. If we cannot understand from the comments what your code does, then you will lose marks. A copy of the marksheet, which shows the distribution of marks across the different elements of the coursework, is available from KEATS, together with FAQs.
CIT 596 - HW1 - 2025 1. (5 points) For the following functions, how would you express them in terms of the big- O notation. You do not need to provide any explanation. Your answer has to be written in the most compact manner possible. An answer of O(50n + 20) will receive 0 points. In that case we would be expecting an answer of O(n). If not specified, you can assume the base for logarithm is 2. • 100n2 + 25n3 + 10000n2 log n • log n + log n2 + log(log n) • n3 + 3n + n! • 2n+1 + n101 • log5 (n3 ) + 2log2 n • 3log2 (n) + 17n2 2. (10 points) Formally prove that 5nlog2 n + 7n − 5000 is Θ(nlog2 n). 3. (5 points) When we say an algorithm is O(log n) (or even Θ(log n)) we are allowed to be sloppy about the base of the logarithm (we do not write it usually). Why is this OK? We are looking for mathematical reasoning in this question, so please use definitions, formulae, theorems etc. 4. (5 points) We have a simple undirected graph (a graph with a finite number of vertices and at most one edge between any pair of vertices). Let m be the number of edges and n be the number of vertices. Person X tells you that they have an algorithm for accomplishing a certain graph theoretic task that runs in Θ(n2 ). Person Y tells you that they have an algorithm for accomplishing the same task that is running in time Θ(m). Who has the better algorithm in terms of runtime? As the graph gets larger and larger, whose algorithm is likely to be faster. Please provide a brief explanation here. When considering which algorithm is better, you might want to think about different types of graphs - graphs with many edges, graphs with less edges. . .. 5. (5 points) Solve this recurrence by expanding it out. Please express your answer in big-O notation. Please show your work. Note that you will need to know the formula for the sum of the first n natural numbers for this question. 6. (3 points) A recursive algorithm for checking whether or not a string is a palindrome can be described in plain English in the following manner. Base case - If it is single character string or empty string, it is clearly a palindrome. Check whether the first character and the last character are the same. If they are not, return false. If they are the same, then recursively check whether the substring from the 2nd character to the penultimate character (second last) character is a palindrome. Write a recurrence that describes the runtime of this algorithm (this is the T(n) equa- tion). Please explain how you got that recurrence equation. You are not required to solve the recurrence.
CHEM191 ASSIGNMENT 8 Due: Monday 3rd February The assignment is in two parts and worth a total of 30 marks. Part A - A useful organic compound (10 marks) Choose one of the compounds below (or you can choose your own organic molecule but check with Tylah first) and write about its discovery or synthesis and its usefulness to society (focus on one application). You should attempt to find a link between the molecule and its usefulness. You should consult and reference at least 3 different sources and collate the information into a coherent, concise paragraph which includes: • The molecular formula • The structural formula • The functional group/groups in the molecule • A short summary (300 words) of the usefulness (linking the application to the structure) and the discovery or synthesis of the compound. Vitamin C, caffeine, nylon, aspirin, glucose, penicillin, acetic acid, sodium stearate, rubber, DDT, morphine, progestin, polythene, quinine Please note: you are required to write a “summary” so this means that you should collect ideas from various references and combine the ideas in your own words. Your work will be checked for plagiarism so be sure to present your ideas in your own words. The marking rubric for the writing is shown below. 3 points 2 points 1 point Molecular structure Molecular formula, structure and functional groups are correctly documented Most of molecular formula, structure and functional groups are correctly documented Discovery or Comprehensive details Details of the discovery synthesis of the discovery or synthesis are documented or synthesis are sketchy Usefulness Details of a specific use are clearly documented with a link to the molecular structure Details of a specific use are clearly documented Details of a specific use are documented Writing Skills Most of the following: Clarity, Precision, Concision, excellent spelling and grammar AND written in own words Poorly written - needs improvement. References At least three references are listed and consulted Part B - (20 marks) Provide answers to each of the following questions. Q1 Identify the functional groups (i) to (iv) in the following molecule: (2 marks) Q2 Give the systematic names for the following organic molecules: (4 marks) Q3 Write structural formulae for the following organic molecules: a) 3-ethylcyclohexanol b) pentyl propanoate c) 2-bromo-3-methylpentanoic acid d) 3,4-dimethylpentan-2-amine (4 marks) Q4 Draw all the structural formulae of alcohols that have the molecular formula C5 H12O. Classify the isomers as primary, secondary, or tertiary. (3 marks) Q5 Determine which reagents A to C and reactant D that will bring about the given transformations and draw the structural formula of PRODUCT X Q6 Draw the structural formulae for the organic products of the following reactions and determine whether the reaction is addition, substitution, elimination, acid-base or oxidation-reduction (NOTE: there are two products that form. for b)). (4 marks)
Selection, Collection & Reporting of Clinical Trial Outcomes Module Code 32203 Assessment Brief 2024/2025 – Protocol Sections Submission format: Please create a Microsoft Word document using font size 11 or 12. If you are unable to create a Word document, please create a pdf file. The name of your document should use the following format ‘SCRO STUDENT-ID’, so if your student ID was 1234567, your filename would be ‘SCRO 1234567’. Include your Student ID on each page but DO NOT include your name. Word limit: 2500 words Assessment weighting: 100% of module mark Submission deadline: 10th February 2025, 12:00 (midday) Submission is via CANVAS (further details will be provided on CANVAS) Task: To write expanded protocol sections relevant to the selection, collection and reporting of clinical trial outcomes. Topic: Treatment for adolescent idiopathic scoliosis. Scoliosis is an abnormal sideways curvature of the spine, creating an S-shaped or C-shaped spine. Patients are diagnosed with scoliosis if they have one or more angles of lateral curvature (Cobb angle) of greater than 10° (see figure below). Approximately 80% of patients with scoliosis have idiopathic scoliosis, where the cause is unknown. Most idiopathic scoliosis cases develop and are diagnosed amongst adolescents (10–18 years) and until the skeleton stops growing, the curvature may become worse. Scoliosis causes changes in posture, muscle weakness, reduced mobility and in more severe cases, chronic pain and problems with breathing. For many individuals, these symptoms lead to reduced physical function and poor quality of life. It can also have a significant psychological impact, especially amongst adolescents, affecting an individual’s body image, self-esteem and confidence. Conservative treatment options aim to slow or prevent progression and avoid the need for spinal surgery. Treatment options throughout adolescent development, include monitoring (with regular x-rays every six months or yearly, depending on severity), bracing (wearing a rigid plastic brace around the torso) and physiotherapy (low impact strengthening and stretching exercises). Whilst there is some evidence of a benefit of bracing in terms of progression and the need for surgery, bracing requires patients to wear their (individually adapted and curve specific) brace for 23 hours per day, until skeletal maturity and consequently, adherence is often poor reducing any potential beneficial effects. Poor adherence is likely to be due to physical restriction and comfort and negative psychological impact from the brace, particularly related to the school environment. Scoliosis specific exercises are individually adapted and curve specific and delivered by a physiotherapist. Once patients are familiar with the exercises they can also be performed at home. There is some evidence that performing such exercises regularly throughout adolescent development, can improve pain and muscle strength and may help reduce any back pain. It is not yet clear, however, whether scoliosis specific exercises can prevent progression or improve scoliosis, and they are not recommended by all specialists. A research team plan to conduct a definitive randomised controlled trial to determine the effectiveness of regular scoliosis specific exercises compared to bracing in adolescents (10-15), diagnosed with mild to moderate scoliosis. All patients will have an x-ray and meet with a consultant every six months until skeletal maturity, as part of their usual clinical care. Those allocated to the brace will also be seen by an orthotic expert every 3 months for any adjustments that need to be made to the brace. Those allocated to the exercise group will attend weekly physiotherapy appointments for eight weeks, then once a month for a further four months. After the initial six months of treatment, they will attend a physiotherapy appointment every six months to ensure they are still completing the exercises correctly and to re-tailor the exercises if necessary. Patients will be required to perform. 20 minutes of exercises at home 4-5 times for the duration of the study or until skeletal maturity. As an outcome expert, the team have asked you to help write a protocol for the proposed trial. Reporting structure and allocation of marks: The assessment should be written in the style. of an essay, using the sub-headings below. Please ensure that you write the requested detail in the correct sections. Take note of the allocation of marks below, to guide you as to how much to write in each section. Title: Descriptive title identifying the study design, population, and interventions. Objectives: In addition to listing the trial’s objectives, you should provide a brief, but clear rationale for your objectives and any hypotheses you are testing. If you draw information or evidence from additional resources, you should cite your sources appropriately. However, the information included in this assessment brief should provide you with sufficient rationale. Methods – Population and Interventions: Describe your target population for your research question (detailed clinical inclusion/exclusion criteria are not required). For the intervention and comparator, we do not require any additional information than that included in the information above. Methods - Outcomes: This section should include a full description of the chosen primary, secondary and any other trial outcomes. It is very important to explain the rationale for the choice of each outcome, with justification drawn from the wider literature, and reference to resources such as the COMET Initiative database (make sure that any additional resources are properly referenced). Where several potential outcomes are considered (for example, for the primary outcome), there should be critical discussion around strengths and weaknesses of each, with clear illustration of which elements underpinned the final decision. For each outcome, include a definition (if needed), the analysis metric (e.g., final value, change from baseline, time to event), and the time point(s) for each outcome with justification. The choice of outcome measurement instruments is NOT required in this section. If relevant, you should discuss any potential issues that the chosen outcomes may have on the validity, generalisability, or interpretation of the trial (e.g., composite or surrogate outcomes). Methods - Data Collection Methods: This section should succinctly present plans for assessment and collection of the trial outcome data, including a full description of outcome measurement instruments (e.g., questionnaires, laboratory tests) along with their reliability and validity, if known. Who are the outcome assessors? (E.g., patient-reported, clinician-reported etc.), are they blind to treatment allocation and how will the data be collected? The section should also include any related processes to promote data quality (e.g., duplicate measurements, training of assessors). Again, there should be critical discussion and justification around the chosen methods (with mention of other measurement instrument tools that were considered) and any potential impact of your choices on the conduct, validity, and interpretability of the trial (e.g., subjective outcome measures, burden on patients). Analyses and Reporting: You are not required to include specific analytical methods to be used, but should include, for each outcome, the method of aggregation (that is, how the data will be summarised in each group e.g., mean, proportion), and your estimate of treatment effect(s) to be reported (e.g., difference in means, risk ratio). You should also include a minimal clinically important difference (MCID) for your primary outcome, with justification. If an MCID is not available, you should suggest a sensible estimate and describe how this would be confirmed before the start of the trial. Please also provide information of how results will be reported to ensure transparency and wide dissemination. You should refer to the SPIRIT Statement, SPIRIT-Outcomes and SPIRIT-PRO guidelines of what detail should be included throughout. The assessment will be marked out of a possible 100 marks. Marks will be awarded in the following proportions: Title, objectives, population, interventions 8 marks Trial Outcomes 38 marks Data Collection Methods 32 marks Analyses & Reporting 12 marks Overall Presentation 10 marks (including logical flow, correct use of grammar/spelling and referencing) Maximum marks will be given based on the following criteria: (1) Clear description and definition of the outcomes of the trial (2) Justification and rationale for each chosen outcome, supported by a critique of the wider literature. Consideration of the strengths and weaknesses of each outcome considered/selected. There should be critical discussion and justification around the chosen outcomes drawing upon appropriate literature. (3) Identification of, and justification for appropriate data collection approaches and statistical/reporting considerations. Consideration of approaches that may improve trial conduct, optimize data quality and interpretation of trial findings. There should be critical discussion and justification around the chosen outcome measurement instruments drawing upon appropriate literature.
SUBJECT OUTLINE 23566 Economics for Business 2 Course area UTS: Business Delivery Summer 2024; standard mode; City Credit points 6cp Requisite(s) 23115 Economics for Business These requisites may not apply to students in certain courses. There are also course requisites for this subject. See access conditions. Result type Grade and marks Subject description This subject extends the foundational treatment received in 23115 Economics for Business by analysing the decisions that lie behind the demand and supply curves in markets, and the forces that affect aggregate demand and aggregate supply curves of the economy. The subject examines a number of core issues in economics such as: how firms choose their production levels and how their decisions are affected by the market conditions, how consumers choose between alternative combinations of goods and services, how government conducts fiscal and monetary policy and which forces affect the long-run economic growth of a country. The subject also equips students with the basic quantitative skills needed to examine these questions. Subject learning objectives (SLOs) Upon successful completion of this subject students should be able to: 1. explain a number of important extensions and modifications to the theory of markets, some important implications of macroeconomic policy decisions and the forces that affect long run economic growth 2. critically evaluate a greater range of government policies at both the microeconomic and macroeconomic levels 3. communicate critical evaluations of economic phenomena in writing with greater effectiveness. Contribution to the development of graduate attributes This subject contributes to the Bachelor of Business and Bachelor of Economics by providing students with an enhanced understanding of foundational issues in economics. The students will learn how to model behaviour of consumers and producers using graphical and analytical framework. Economics for Business 2 thus extends the knowledge and skills students have developed in Economics for Business to prepare them for more advanced study in economics, finance and other areas of business that draw heavily on economic principles. This subject contributes to the development of the following graduate attributes: Communication and collaboration Social responsibility and cultural awareness Professional and technical competence This subject also contributes specifically to develop the following Program Learning Objectives for the Bachelor of Business: Demonstrate ability to work independently and with others as a member of a team to achieve an agreed goal (2.2) Make judgements and business decisions consistent with the principles of social responsibility, inclusion and knowledge of Indigenous peoples (3.1) Apply technical and professional skills to operate effectively in business (4.1) This subject also contributes specifically to develop the following Program Learning Objectives for the Bachelor of Economics: Demonstrate ability to work independently and with others as a member of a team to achieve an agreed goal (2.2) Teaching and learning strategies In this subject, students get involved in active learning through the entire semester. The subject is taught through a range of interactive activities, including the following: Online Quizzes: Five computerized online quizzes will be provided during the course and the best three results will be counted towards the final mark (part of Assignment 1). Lecture Slides: Lecture slides will be available several days before each lecture on the learning management system. Please note that a lecture is just a summary of the material covered in the subject and should not be regarded as a sufficient coverage of the subject matter. Additional Notes: Since the subject has a quantitative component, the additional notes, extracts and exercises are supplied to complement the main textbook. These notes are available online. Tutorial questions will refer to the concrete questions and exercises to be studied in these additional notes. Tutorial Exercises: Tutorial questions containing the homework for the next tutorial are available on the learning management system each week. The tutorials will typically have two parts, self-test Multiple Choice Questions and Open Questions. During the first several weeks an extra part on mathematical background will be added. Tutorial questions should be attempted prior to each class, where a tutor will present and discuss the solutions. Solutions to the tutorial questions will be available the week following the respective tutorial class. Collaboration: Tutorials will provide an opportunity to discuss key ideas from this subject with your teacher and peers. Students will also have opportunities to directly interact with each other through the learning management system. Feedback: The learning management website will be used extensively as a vehicle of communication between staff and students, including through the online discussion board. Students will also receive regular feedback through interactive tutorial sessions by completing online quizzes. Content (topics) . Firms' cost structures and its impact on production and pricing decisions; graphical and analytical representation of cost functions; . Market structure and its impact on firms'market strategy; · Labour and capital market, theories of wage determination; · Expenditure Multipliers and the Keynesian Model of National Income; Macroeconomic Fluctuations; . Investment, Saving and the Role of Financial Markets; . Issues in Fiscal Policy. Assessment Assessment task 1: Portfolio (Individual* and Group) Intent: This assessment comprises two parts: 1. individual, computer-based assignments* (weight 15%) 2. group assignment (weight 25%) Objective(s): This addresses subject learning objective(s): 1, 2 and 3 Weight: 40% Task: The individual part of the Assessment consists of five online quizzes. See the Program above for the due dates. The questions will be similar to the tutorial exercises. Three best results of the five quizzes will be counted towards the final mark with a maximum of 5% for each. For the group part of the Assessment, you need to form. a group of 4 students (groups of 5 students may be allowed in some cases). More information on the group assignment will be provided on Canvas. The group assignment must be submitted on the due date. Due: The dates for all assessments are listed the program schedule. Criteria: *Note: Late submission of the assessment task will not be marked and awarded a mark of zero. Further Since each quiz will be open on Canvas for a sufficient period of time, there will be no possibility to information: postpone the deadline for any particular test. Do not leave the quiz to the last minute! The group assignment task will also be available well before the deadline. Do not leave the group formation until the last minute! Plagiarism detection software, including for the use of AI generated text, may be used to ensure the work you submit is your own. Details on UTS policy surrounding plagiarism and copyright are provided later in this subject outline. Assessment task 2: Final Examination (Individual) Objective(s): This addresses subject learning objective(s): 1 and 2 Weight: 60% Task: The final exam covers material from the entire semester. The final exam is a closed book. The questions will be similar to the lecture and tutorial questions that you have practised throughout the semester. The Student Administration Unit (SAU) handles all aspects of exam administration. Information about 'Examinations and Assessments' is on the UTS website at: http://www.sau.uts.edu.au/assessment/exams/central.html If you are unable to attend the centrally-conducted exam it is your responsibility to familiarise yourself with the relevant rules detailed on the above website. Length: 2 hours. Due: UTS Exam Period; see Further information
