CIV2235—STRUCTURAL MATERIALS Week 7 Practice Class Elastic and Plastic Section Properties; and Masonry Materials Part A - Elastic and Plastic Section Properties Question 1 (50 marks) For the Grade 300 box-section made of heavily welded plates shown in Figure 1, do the following: a. Find the position of the centroid (neutral axis, N.A.) of the cross-section. (5 marks) b. What is the second moment of area ("moment of inertia" I) of the section taken about the neutral axis? (5 marks) c. Compute the elastic section modulus Z. (5 marks) d. Find the location of the Equal Area Axis (y) of the section. (7.5 marks) e. Calculate the plastic section modulus S of the section. (7.5 marks) f. What is the shape factor (S/Z) of the section. (5 marks) g. Using the table below determine the yield stress for the top flange, bottom flange and the web. (5 marks) h. Calculate the maximum plastic moment capacity of the section. (10 marks) Figure 1 Part B – Masonry Materials Technology Question 2 (50 marks) Sketch the following. Please label your sketch with the relevant details that distinguish each different masonry element type. a. Solid and cored brick and hollow concrete masonry units illustrating the following features on each unit: face, frog, core, head, face shell, web, core and arris. (10 marks) b. Buttress and Engaged Pier on a masonry wall. On your sketch, label each and explain the function of each. Describe the properties of the wall cross-section altered by each. What is the difference between a buttress and an engaged pier? (15 marks) c. A section through an articulation joint. Using sketches illustrate where are they required and contrast with places they are not required. Reference the appropriate standard and clause(s) for articulation joints. (15 marks) d. A section through a lintel over a window opening supporting a cavity wall. Show the DPC and how any moisture in the cavity is brought back outside the building. (10 Marks)
CEGE0015: Environmental Systems Environmental Disaster Report 2024-25 Assignment Brief and assessment criteria Your overall aim is to use an environmental disaster as a case study to investigate the impact on different environmental systems. In order to do this, you will have to carry out research into the environmental disaster assigned to you and discuss how different systems have been affected as well as looking into possible solutions to the problem. You have been assigned one of the following disasters to investigate: 1. Sanchi oil tanker collision in 2018 2. Fukushima Daiichi nuclear disaster in 2011 3. Ivory Coast toxic waste dump in 2006 4. Haiti earthquake in 2010 5. Brumadinho dam disaster in 2019 6. Australian bushfire season 2019-20 (Black Summer) This assessment makes up 40% of the final module mark for CEGE0015 and will be double marked by the module coordinator and another member of staff. You will write a report of no more than 2500 words (not including title page, figure legends and references) which will need to cover the following: • A description of the environmental disaster outlining how it came about and the various factors that led to it. (25 marks) • An assessment of how different environmental systems were affected by the disaster. (35 marks) • An assessment of how human health was affected by the disaster. (25 marks) • Overall layout should be neat with no spelling or grammatical errors using Harvard, or similar, referencing style. (15 marks). Exceeding the word limit will result in a penalty being applied. Submission The assignment is due on Monday 20th January 2025 at 5 pm. You will submit the assignment anonymously on Moodle through Turnitin in PDF format as a single file.
125.810 Case studies in Corporate Finance and Risk Management Case study 3 Submission instruction: To submit: goto Stream→Assessment→ Case study 3 Submission. This is a group assignment, so only one submission per group is required. Due date: 5pm. Please refer to the attached case report due date document. Suggested questions for the case: Please refer to the attached document Requirements: 1. The report should address the suggested questions for the case but should not be written in the form. of answers to these questions or be confined to these issues alone. Rather, the report should provide a comprehensive review of the situation and address all points deemed important for the analysis. 2. If you find that the information provided in the case is insufficient for your analysis, you may conduct your own research from reliable sources (such as the firm’s annual reports). Additionally, you may make your own assumptions (especially for the calculation sections). Where necessary, you must provide justifications for the assumptions you make. 3. You should analyze the case as though you were at the time it was written. Therefore, if you seek additional information to support your analysis, ensure that no information after that time frame. is included. 4. Detailed calculations and assumptions for the calculation sections should be placed in the Appendix/Table rather than in the main text. You only need to summarize or briefly discuss the results of the calculations in the body of the report. Make sure to refer to the name of the Appendix/Table when discussing the results. If you use Excel for the calculation sections, please submit the Excel file along with the case report. 5. The case report should be no more than ten pages in length (double-spaced, 12-point font with normal margins). Tables, figures, references, and the table of contents are not counted toward the ten-page limit. The report should be thoroughly checked for grammatical and spelling errors. 6. Fifty percent of the grade will be based on the quality of the report, and the other fifty percent will be based on your engagement during the presentation discussion. When the presenting group is presenting, you are expected to ask questions and participate in the discussion. Your engagement will be graded individually, considering not only the quantity of questions and comments but also their quality CASE STUDY REPORT FORMAT GUIDELINE Following is a suggested guideline for preparing your case study reports. Cover Page (Include student names and student IDs) Executive Summary (About 1 page or less. If appropriate - should be written last to focus on key points/findings.) Introduction Current Situation Analysis and pertinent Background including a synopsis of the relevant information from the Case. Body May include, but not limited to, the following: • Analysis of Case • Key Issues/Goals • Recommendations • Assumptions with rationale (If applicable) • Data Analysis (analysis in the Appendix and summary information in the body) • Preferred Alternative with rationale. • Justification/Predicted Outcome.
Digital Leadership Project Objective: Your task is designing and planning a digital transformation project for a hypothetical or real organization (of your choice). The project requires you to develop a strategic plan that includes the problem to be addressed by the digital transformation project, digital tool selection, team roles, and an execution timeline. Requirements: 1. Project Background: Briefly introduce the organization, its current digital status and what is the main challenge that you want to address through a digital transformation. 2. Digital Transformation Strategy: Outline the key objectives, the need for digital change, and the vision for the project. 3. Digital Tools Selection: Identify and justify the selection of digital tools, platforms, or software that would be implemented (e.g., CRM systems, collaborative tools, cloud solutions). 4. Team Roles Assignment: Define key roles and responsibilities within the project team (e.g., project manager, digital lead, technical support). 5. Execution Timeline: Develop a timeline that outlines the stages of the project (planning, implementation, testing, and go-live). You are required to build a website using a free online platform. (e.g., WordPress, Wix, Google Sites) that consists of at least four different sections presenting your digital transformation project to a team of executives and employees that work remotely for your company. The website should address and present the content of your digital transformation project. The design should be functional, user-friendly, and demonstrate your understanding of basic website development principles. Requirements: The website must have a minimum of five sections/pages with the content of your digital transformation project. You can use multimedia elements (images, videos, etc.) where appropriate. Basic SEO principles applied (titles, meta. descriptions). Consistent design, layout, and branding across all pages. Responsive design (compatible with both desktop and mobile). Submission Requirements: 1 page PDF with your name, student number, title of your project, and the link to the published website. A brief report (250-300 words) outlining your design choices, the platform. used, and how the website meets the requirements.
JANUARY 2023 ASSESSMENT PERIOD School of Engineering and Materials Science Module code: EMS702U/P Module name: Statistical Thinking and Applied Machine Learning Rubric: You have a maximum of 4 hours to submit your answers from the assessment release time after initial access within a 4-hour window. Therefore, your work should be accessed ON xxth January 2022 and submitted within 3 hours of access. We are expecting you to spend around 2 hours on this assessment. Answer ALL questions Clearly cross out / delete any work that is not to be marked. There are NO supplementary resources with this assessment. I) ASSESSMENT INSTRUCTIONS ● Answers should be written legibly by hand or word-processed. All text, sketches and mathematics should be integrated into a single document, and submitted as a pdf. ● Include a cover page (first page), detailing your student number, and the module code and name. ● If you have a DDS (Disability & Dyslexia Service) cover sheet, include a scanned copy of that as the second page of your submission During the assessment ● Your submission must be your own work, and you must not break any of the rules in theAcademic Misconduct Policy. Do not collude with others. Do not ask anyone else to answer the questions for you. All such activities are expressively forbidden. You must answer questions entirely independently. ● Please be aware that: 1. All submissions will be subject to inspection for plagiarism and use of external tutoring support. Detection of such activity will constitute an assessment offence 2. We will viva a percentage of students after each assessment to discuss the answers they have provided as confirmation this is your own work ● Any assessment offences will be referred to the Academic Registrar for consideration by the Chair of the Academic Misconduct Panel, and may subsequently lead to a severe penalties, including expulsion. ● Please ensure that you read the question rubric (above) carefully, and answer all the questions that you are expected to. Make sure it is clear which answer refers to which question. ● If you answer more questions than specified in the rubric, only the first answers up to the number required will be marked. Ensure you delete anything you do not wish to be marked before submission. ● Any question regarding the assessment should be directed to the QMPlus module forum. We will staff these during UK working hours, so you can always expect an answer within 24 hours but may already find the answer to your question within the forum post. We will only help with queries around clarity of the question paper and will not provide any guidance on the answers. Submitting the assessment ● Submit your answer as a single pdf document using the submission point on the QMPlus module page. Your filename should be the module code plus your student number. Leave yourself plenty of time for the upload. ● In case of difficulties with the upload, please email your submission to: [email protected]c.uk before the submission deadline, putting the module code and your student number in the email subject line. Question 1 A supervised learning approach based on a stochastict learning algorithm is proposed to predict the taxiing time of arrival aircraft for an international hub airport. Due to the stochastic nature of the learning algorithm, the proposed supervised learning approach has been trained 20 times indepently on the same training data and tested on the testing data set in order to evaluate the performance of the proposed approach. The prediction accuracy measured by the Mean Absolute Error (MAE) for the whole testing data set, in minutes, are as follows: 0.8616 2.1604 4.3546 1.9278 3.9610 3.1240 4.4367 1.0391 2.8023 1.7922 5.9080 3.8252 4.3790 1.9418 2.5314 2.7275 4.0984 2.7221 3.7015 0.9482 Assuming that the MAEs are normally distributed with unknown mean, μ , and unkown variance σ2 , (a) Estimate μ and σ 2 ; [6 marks] (b) Find a two-sided 95% confidence interval for the mean, μ; [8 marks] (c) Find a two-sided 95% confidence interval for the variance , σ 2 ; [9 marks] (d) Find a two-sided 95% confidence interval for the standard deviation, σ . [2 marks] Question 2 Consider we have 5 sets of observed data (red points on the coordinate system) (x, y) = [(0.0, 1.2); (0.5, 2.2); (1.0, 3.5); (1.5,7.3); (2.0, 10.8)] Evaluate the regression model as a second-order polynomial function: y = 1 + a1x + a2x 2 by (a) Formulate the regression model into a matrix form. and show the least Squares (LS) representation of a1 and a2 ; [8 marks] (b) Estimating the values of a1, a2 from the observed data by using the LS method; [8 marks] (c) Drawing the regression model on the coordinate system (Show the applied coordinates); [6 marks] (d) Validating the regression results by calculating the Mean Squared Error (MSE); [3 marks] Question 3 Consider we have 2 sets of observed data (x, y ) = [(1.0, 0.6); (2.0, 0.7)] Use gradient decent method to determine the one-dimensional neural network model: with by solving the following problems: (a) Calculate the feed-forward outputs and the cost function value with w1 and w2 being initialized as w1 = 0.5 and w2 = 0.2 ; [4 marks] (b) Calculate the gradient of the cost function with respect to the weight w2 for the first back-propagation step; [8 marks] (c) Calculate the gradient of the cost function with respect to the weight w1 for the first back-propagation step; [9 marks] (d) Calculate the updated weights w1 and w2 for the first back-propagation step under the learning rate λ = 1; [4 marks] where exp (0.1) = 1.105 ; The required cost function is with y (k) being the model prediction. Question 4 (1) Condier three finite fuzzy sets of X defined by three linguistic qualifiers: positive big (PB), positive medium (PM) and positive small (PS) on the finite universe of real numbers [0, 6]: Find the membership function of (a) PB or PM, PM and PS, Not PM, Not (PB or PM) [4 marks] (b) Consider the rule: R: IF (error is PB) THEN (regulator position is reduced to PS). For PB, PS defined above, find the relational matrix R corresponding to this rule. [5 marks] (2) Condider the following fuzzy partitioning of the variables ‘x’ and ‘y’ in a normalised universe of discourse ‘x’ and ‘y’ respectively: Let us assume the following set of 4 Takagi Sugeno-Kang (TSK) fuzzy rules: Rule 1: IF ‘x’ is NB AND ‘y’ is N THEN Z = a1x + b1 + c1y Rule 2: IF ‘x’ is NM AND ‘y’ is N THEN Z = a2x + b2 + c2y Rule 3: IF ‘x’ is Z AND ‘y’ is Z THEN Z = a3x + b3 + c3y Rule 4: IF ‘x’ is PM AND ‘y’ is P THEN Z = a4x + b4 + c4y (a) Is the above rule-base complete? Why? [4 marks] (b) Find the defuzzified values of the output ‘Z’ if the recorded crips values of ‘x’ and ‘y’ are: ● x = −0.75 ANDY = −0.5 ● x = −0.25 ANDY = −0.75 [12 marks]
ULMS766 JANUARY EXAMINATIONS 2022 Marketing Management 1. Discuss the benefits to the organisations customer when the WHOLE organisation and their Suppliers have an understanding of customer needs. Use an organisation of your choice to illustrate your arguments. 2. In the context of changes in society, to what extent do you believe that the marketing mix (4P’) is still relevant today? 3. Discuss the value of market segmentation in helping oganisations to target customers with an appropriate marketing mix so as to clearly position their brand as the brand of choice in the mind of their audience. 4. Using your own experience as a customer for examples, critically evaluate ways in which marketers can work towards ensuring their customers can never suffer from cognative dissonance. 5. To what extent can market research findings be trusted?
FINN2071 Intermediate Financial Economics Undergraduate Programmes 2023/24 SUMMATIVE ASSIGNMENT 1. Please provide a brief explanation of the holiday effect. Additionally, download the returns of three random stocks from WRDS and the pricing factors from French’s data library. Investigate whether the holiday effects influenced the explanatory power of the 4-factor model from Jan-2000 to Dec 2020. Discuss your findings. (60 marks) 2. Investigate the impact of business cycles on your findings in question (1) using NBER recession dates. (40 marks) Overall word limit: 3,000 words SUBMISSION INSTRUCTIONS Your completed assignment must be uploaded to Ultra no later than 12:00 midday on 20th January 2025 The assignment should be submitting using one of the following file types: .doc, docx or .pdf AVI, MP4, MPG, WMV, MOV, QT, ASF, 3GP, WMA, MP3 or M4V A penalty will be applied for work uploaded after 12:00 midday as detailed in the Student Information Hub. You must leave sufficient time to fully complete the upload process before the deadline and check that you have received a receipt. At peak periods, it can take up to 30 minutes for a receipt to be generated. Assignments should be typed, using 1.5 spacing and an easy-to-read 12-point font. Assignments and dissertations/business projects must not exceed the word count indicated in the module handbook/assessment brief. The word count should: Include all the text, including title, preface, introduction, in-text citations, quotations, footnotes and any other items not specifically excluded below. Exclude diagrams, tables (including tables/lists of contents and figures),equations, executive summary/abstract, acknowledgements, declaration, bibliography/list of references and appendices. However, it is not appropriate to use diagrams or tables merely as away of circumventing the word limit. If a student uses a table or figure as a means of presenting his/her own words, then this is included in the word count. Examiners will stop reading once the word limit has been reached, and work beyond this point will not be assessed. Checks of word counts will be carried out on submitted work, including any assignments or dissertations/business projects that appear to be clearly over-length. Checks may take place manually and/or with the aid of the word count provided via an electronic submission. Where a student has intentionally misrepresented their word count, the School may treat this as an offence under Section IV of the General Regulations of the University. Extreme cases may be viewed as dishonest practice under Section IV, 5 (a) (x) of the General Regulations. Very occasionally it may be appropriate to present, in an appendix, material which does not properly belong in the main body of the assessment but which some students wish to provide for the sake of completeness. Any appendices will not have a role in the assessment - examiners are under no obligation to read appendices and they do not form part of the word count. Material that students wish to be assessed should always be included in the main body of the text. Guidance on referencing can be found on Durham University website and in the Student Information Hub. MARKING GUIDELINES Performance in the summative assessment for this module is judged against the following criteria: • Relevance to question(s) • Organisation, structure and presentation • Depth of understanding • Analysis and discussion • Use of sources and referencing • Overall conclusions
Assessment Proforma 2024-25 Module Code CMT117 Module Title Knowledge Representation Assessment Title Problem Sheet 2 Assessment Number 2 of 2 Assessment Weighting 50% Assessment Limits Hand-out: 09th of January 2025 Hand-in: 16th of January 2025, 09:30AM Limits are per task as set in the instructions The Assessment Calendar can be found under ‘Assessment & Feedback’ in the COMSC-ORG-SCHOOL organisation on Learning Central. This is the single point of truth for (a) the handout date and time, (b) the hand in date and time, and (c) the feedback return date for all assessments. Learning Outcomes • Critically evaluate knowledge representation alternatives to solve a given task • Formalize simple problems with a given knowledge representation approach • Discuss the theoretical properties of different knowledge representation formalisms • Explain the basic principles underlying common knowledge representation approaches • Choose an appropriate knowledge representation approach to address the needs of a given application setting • Compare how knowledge representation approaches influence the success of a given task • Explain the nature, strengths and limitations of knowledge representation technique to an audience of non-specialists Submission Instructions The coversheet can be found under ‘Assessment & Feedback’ in the COMSC-ORG-SCHOOL organisation on Learning Central. You are required to answer 2 multi-part questions on “First-order Logic” and “Description Logics”, as described in detail in the attachment. The answers should be submitted as a single pdf file. All submissions must be via Learning Central. Upload the following files: Description Type Name Coversheet Compulsory One PDF (.pdf) file [student number] Coversheet.pdf Answer to all question parts Compulsory One PDF (.pdf) file [studentnumber].pdf If you are unable to submit your work due to technical difficulties, please submit your work via e-mailto [email protected] and notify the module leader. Any deviation from the submission instructions above (including the number and types of files submitted) may result in a reduction in marks for the assessment. All submissions will be compared to each other and checked against other work available on the Internet and elsewhere to identify cases of potential unfair practice. Staff reserve the right to invite students to a meeting to discuss coursework submissions. Assessment Description Answer all parts of Questions 1 and 2 below. The first question is worth 25 marks and the second question is worth 15 marks. The number of marks available for each question part is indicated. Question 1: First-Order Logic 1. Translate the sentences below from English into first-order logic. For each translated sentence provide an explanation of why your first-order sentence captures its En- glish counterpart. Use the signature S consisting of the unary predicate symbol Region and the binary predicate symbols Disjoint, Included and Overlap and the constant symbols dorset, fife, scotland,england. (a) Any two regions are either disjoint, overlapping, or one of them is included in another. [0.5 marks] (b) Every region is included in itself. [0.5 mark] (c) If two regions are disjoint, they are not overlapping. [0.5 marks] (d) If two regions are overlapping, none of them is included in another. [0.5 marks] (e) If one region is included in another, then they are not disjoint. [0.5 marks] (f) If two regions are disjoint, then any region included in the first one is disjoint from the second one. [0.5 marks] (g) Dorset and England are regions, Dorset is included in England. [0.5 marks] (h) Fife and Scotland are regions, Fife is included in Scotland. [0.5 marks] (i) Scotland and England are disjoint. [0.5 marks] 2. Do the sentences from Part 1 above logically entail that Dorset does not overlap with Scotland? Justify your answer by providing a proof using precise semantic argu- ments or by providing a counter-example. [4.5 marks] 3. Does ∃x.(A(x) ∨ B(x)) |= ∃x.A(x) ∨ ∃x.B(x) hold? Justify your answer by providing a proof using semantic arguments or by providing a counter-example. [4 marks] 4. Does ∀x.A(x) → ∀x.B(x) |= ∀x.(A(x) → B(x)) hold? Justify your answer by provid- ing a proof using semantic arguments or by providing a counter-example. [4 marks] 5. We want to prove that the following argument is true: If all quakers are reformists and if there is a protestant that is also a quaker, then there must be a protestant who is also a reformist. Define a set of FOL sentences X and a sentence G capturing this argument and show that X |= G using semantic arguments. Justify why X and G properly capture the argument. Hint: You can define the FOL sentences in X and the sentence G using the unary predicate symbols: Quaker, Reformist and Protestant. [8 marks] Question 2: Description Logics 1. Describe an application scenario in which there exist three advantages and one dis- advantage of using the description logic ALC , rather than propositional logic as a language for Knowledge Representation. You need to justify why they are advantages and disadvantages in the context of the proposed application scenario. This does not mean copy-paste from the lecture’s material. [4 marks] 2. Write down the following (a) A satisfiable ALC-TBox T such that all the atomic concepts occurring in T are unsatisfiable w.r.t. T. Write down a model of T. [1 mark] (b) A satisfiable ALC-knowledge base such that all its models contain at least two domain individuals. Justify your answer. [1 mark ] (c) An unsatisfiable ALC-knowledge base whoseTBox is empty. Justify your answer. [1 mark] (d) An unsatisfiable ALC-TBox. Justify your answer. [1 mark] 3. For a chosen application scenario define an EL KB (T, A) capturing relevant termino- logical and assertional knowledge. The EL TBox T must contain at least five GCIs and the ABox A at least five assertions. Explain what each GCI and assertion is modeling. Define the used vocabulary: concept, role and individual names. [7 marks]
MSc AI for the Creative Industries ASSESSMENT AI and its Application in Creative Practice TFT00112M-A ASSESSMENT HAND IN: Thursday 23rd January 2025, noon (12:00) AIM Through this assessment you are expected to demonstrate the ability to analyse the impact of AI - bee it a tool, a technique, or artificial intelligence in general - upon an area or a project of creative practice. Discussing and analysing technical details about the AI technology employed would matter, but they are not necessary. The focus of this assignment is on your understanding of the disruptions generated by AI to creative processes, evaluating what AI is good at, what it is not (yet), and how it could be included in human-AI co-creative processes. The perspective in working on and writing up this assignment could be that of a consultant commissioned to do an analysis of a tool, a practical project, a research project or of an area of practice which either has already been impacted upon by AI or may foreseeably be so. The same brief applies to both Master level and Undergraduate Year 3 level students. However, the assessment differs: masters students are expected to demonstrate a deeper and more comprehensive understanding of the chosen area of study. BRIEF There are a few ways of approaching this assignment and you are free to choose one which best suits your interest. BRIEF: Option #1: exploration through practice Choose: • one area of creative practice (e.g. painting, photography, film, TV, advertisement, writing, games, virtual theatre, etc.) • one or more AI tools (e.g. ChatGPT, DALL-E, Stable Difusion, Runway ML, Move.AI, Descript, etc.) that could be used in this area • and one project (or creative goal) and explore the capabilities of the AI tool(s) through your own practice, in a manner like what has been presented by some of you in our earlier seminars. In your report you should: • describe the area and the project (artistic goal) • give an overview of the AI tool/system you decided to experiment with by: o summarising the technical details, focusing on the method of controlling its output (prompts, etc.), but capturing also installation aspects, if the tool is not readily available through a web browser o you can also provide a brief account of the position of the tool on the market, (reviews by experts regarding its performance, projects in which it was used, business details, competitors, etc.) • describe the iterative development process o capture the main steps o for each step, discuss the performance of the tool, capturing both strengths and weaknesses • upon the completion of the process – be it successful or unsuccessful – perform. a critical analysis of the development process o generalise (some of) the project-specific conclusions (captured above) to the whole application area o compare with other tools, using expert reviews o make predictions with regards to the future use of this or similar tools in the application area you chose • draw final conclusions with regards to the impact of AI in general on the application area of your choice. SUBMISSION You will have to submit two files • a pdf report including the aspects described above; the report should contain pictures, diagrams, screenshots which have been essential to your development to help you back up the argument; outputs that aren’t critical to the argument should be inserted in appendices, clearly labelled and also submit (see below) – there can be referred to from the main text, where needed; the word limit is o maximum 3500 words - Masters o maximum 2500 words - UG Year 3 • key outputs (e.g. pictures, videos, music files, etc. – see above) and/or screen recordings to illustrate the workflows involved in their production; these should be appropriately labelled and described (e.g. in a read.me file) All the files should be zipped together and submitted as one file only. You will receive instruction on where and how to upload it on our systems from the school’s Administrative Office. BRIEF: Option #2: exploration through literature review Choose an area of creative practice and analyse AI’s impact on it to date and make informed predictions about the future. As sources you can use academic or industry research papers, white papers coming from industry, expert analyses published online or in print, analyses performed by technical journalists, and even marketing material used by various companies, as long as it is appropriately interpreted. Please refer to the marking section below to understand which aspects you should/could include in your analysis. SUBMISSION You will submit one pdf report of maximum 3500 words - Masters or maximum 2500 words - UG Year 3. You will receive instruction on where and how to upload it on our systems from the school’s Administrative Office. USE OF EXTERNAL SOURCES AND ACADEMIC MISCONDUCT You can use any external sources in your analysis as long as you correctly reference them. ETHICS You should observe the principles of ethical research and development adhered to by the University. MARKING Option #1 (the last two columns represent the proportion of the criterion in the final mark for masters and undergraduate, respectively): Criterion Masters UG Problem statement, context, novelty of the project (contextualisation to related work), overview of the AI tools used in the development (identify opportunities and generate new concepts and designs for applications of AI in creative media and performance) 15% 15% Creative workflow – iterative development (richness of features of the tools, extensiveness of their application in the project, iterative development) 45% 55% Depth and comprehensiveness of the critical analysis (justify and critique choices of different parameters, methods and techniques in relation to creative goals, identify strengths and weaknesses of the creative workflow you employed, generalise findings, make informed predictions) 40% 30% Option #2 (the last two columns represent the proportion of the criterion in the final mark for masters and undergraduate, respectively): Criterion Masters UG Problem statement: clarity, novelty, comprehensiveness and contextualisation (identify opportunities and analyse new concepts and workflows involving applications, or possible applications, of AI in creative media and performance) 15% 15% Literature review, appropriateness, comprehensiveness and completeness (number, diversity and relevance of the sources used) 45% 55% Depth and comprehensiveness of the critical analysis (coherence of the argument, novelty of the interpretations, informed generalisations and predictions) 40% 30% This assessment is worth 100% of the overall mark for this module.
N1577 Principles of Banking Seminar 3. Bank evaluation and performance Students are expected to prepare for the seminar/workshop by attempting the following exercises. Exercise 1 PNC Bank is a typically large depository institution. Which balance sheet accounts would be affected by the following transactions? Indicate at least 2 accounts with each transaction. a. John Lewis opens a money market deposit account with $5,000. The funds are lent in the overnight market for one week. b. Just as a real estate developer pays off a shopping centre loan, a new resident takes out a mortgage on a home. c. The bank hires an investment banker to sell shares of stock to the public. It plans to use the proceeds to finance additional commercial loans. Exercise 2 You know the following information about Miller Bank: Balance sheet in EUR thousand Cash ??? Securities Investments 1500 Net Loans 2500 Net Premises and Equipment 400 Other assets 500 Total Assets 5000 Deposits 3900 Non-Deposit Borrowings ??? Equity 500 Total Liabilities and Equity ??? Income Statement in EUR thousand Interest Income 200 Interest Expenses 70 Non-Interest Income 25 Non-Interest Expenses 55 Provision for Loan Losses 30 Pre-tax Net Operating Income ??? Securities Gains (Losses) 10 Taxes 15 Net Income ??? a. Present the relevant formulas and calculate the values of the missing balance sheet and income statement items. b. Present the relevant formulas and calculate: Return on Asset, Net interest Margin, and Equity Multiplier. Exercise 3 The ratio analysis for the Community National Bank and for its peer banks based on 2007 year- end data is shown below: Ratio Community National Bank Peer Banks ROE 20.68% 9.43% ROA 1.89% 0.93% AU 8.24% 7.59% ER 6.32% 6.33% TAX 0.03% 0.33% Compare and discuss the profitability of Community National Bank with that of its peers based on: financial leverage; average yield on assets (AU); operating expenses (ER); and the level of taxation (TAX). Note that TAX = applicable income tax/total assets. Exercise 4 Fill in the missing items from the income statement shown below (all figures in millions of dollars): Income statement Total interest income Total interest expense Net interest income Provision for loan and lease losses Total noninterest income Fiduciary activities Service charges on deposit accounts Trading account gains and fees Additional noninterest income Total noninterest expense Salaries and employee benefits Premises and equipment expense Additional noninterest expense Pretax net operating income Securities gains (losses) Applicable income taxes Income before extraordinary items Extraordinary gains—net Net income $200 60 100 20 25 30 125 10 20 15 5 3 2
CMPUT 466/566 (Fall 2024) Syllabus Machine Learning Essentials Course Format: The course is offered in person only. When the IT infrastructure allows, the lectures will be broadcasted and recorded. However, the instructor cannot guarantee that IT will work well, in which case pre-recorded videos will be released as a replacement. Exams will be based on in-person lectures. Lecture time and classroom: T, Th 12:30PM - 1:50PM, Sep 3 - Dec 9 ● No course activities during the reading week Online lecture hall: https://meet.google.com/vqo-vkxv-osy Dial-in: (US) +1 337-573-0059 PIN: 584 572 100# Lab session (in-person only): Monday 5–7:50PM ● 5-6PM: TA’s office hours (CCIS L1-140) ● 6-7:50PM: Unattended. TAs will open appointment slots for QA. Instructor/TA office hours: With whom Email Open door By appointment Lili Mou (instructor) lmou Thursday, 3-4PM ATH4-08 as appropriate Zijun Wu zijun4 Monday, 5-6PM CCIS1-160 Tue (2-2:30 PM) In-person: CSC3-26 Online: meeting link Nicolas Rebstock nrebstoc Thu (3-3:30 PM) In-person: CSC3-26 Online: meeting link Haruto Tanaka haruto Thu (10-10:30 AM) Online: meeting link Yu Wang yu35 Mon (9-9:30 AM) Online: meeting link Tian Tian ttian Tue (2:50-3:20 PM) Online: meeting link ● Office hours start from the second week. No office hours on statutory holidays and during the reading week. ● If a student wishes to make an appointment with a TA (10min each slot), they will send an email to the TA before the date. ● Office hour schedule may be changed depending on the need. Students are encouraged to reach out to the instructor if TAs’ answer is not satisfactory. Notes: The instructor and TAs will not answer assignment-related questions before the solution is released. COURSE CONTENT Course Description: Machine learning teaches a machine to learn from previous experience and makes a prediction for (possibly new) data. This course covers standard materials of a “ Machine Learning” course, such as linear regression, linear classification, as well as non-linear models. In the process, we will have a systematic discussion on issues such as training criteria, inference criteria, bias-variance tradeoff, etc. The goal of the course is to build a solid foundation of machine learning; so there would be intensive math derivations in lectures, assignments, and exams. Course Prerequisites: Please fulfill the departmental requirements. The department asks instructors normally not to waive prerequisites. Course Objectives and Expected Learning Outcomes: By the end of this course, the student will understand the foundations of machine learning and gain experience in machine learning applications. Official textbook: Bishop, Pattern Recognition and Machine Learning. The instructor will provide lecture notes, which may also suffice. If not, please use the above textbook. [survey on textbooks] References: link Tentative topic list: Linear regression ● Mean squared error (as heuristics) ● Closed-form. solution ● Gradient descent ● Maximum likelihood estimation ● Maximum a posteriori training ● Bias-variance tradeoff ● Train-validation-test framework Linear classification ● Discriminative model: Logistic regression ● Multi-class softmax ● Maximum a posteriori inference ● Generative model: Naïve Bayes ● Discriminant model: Linear SVM (bonus lecture) Nonlinear models ● Neural networks ● Kernels methods: Non-linear SVMs (bonus lecture)
159.352 Topic 4 — Exercises Cryptography Here we will play with some crytographic functions in Python Hash functions The Python hashlib module implements the various crytographic hash functions. Import this module and start with some byte string, e.g. msg = b ' The secret to everything is 42 ' Get a hash object for the MD5 message digest myhash = hashlib.md5(msg) Print out a string representation of the hexadecimal form of the digest print(myhash.hexdigest()) You can see the actual characters using bytes .fromhex(myhash.hexdigest()) Look at the documentation for hashlib. What is the hash name, the block size, and the digest size? The hashlib module also implements the SHA-2 family for 256 bits, hashlib.sha256(), and 512 bit, hashlib.sha512() . The SHA-3 family is also available, i.e. hashlib.sha3_256() and hashlib.sha3_512() . Repeat the above for these functions. Consider how you would use these hashes for server-side storage of a list of user names and passwords. How would the authentication process work? Password cracking Suppose you have come into knowledge of the following hexadecimal string that represents the hash value of some password: af826ad124dce9ef48772e0bac1dec13 Furthermore, you know the original password is 4 bytes and that the MD5 hash was used. Write a piece of Python code to crack that password. How long does your code take to do this? Asymmetric key cryptography Start with the following string msgtext = b ' Good ' Look at the lectures (and/or relevant sections of Kurose and Ross) on asymmetric key cryp- tography and consider the following values e = 65537 d = 109182490673 n = 5551201688147 What permutation of values will function as the “public” key? What will be the “private” key? In a Python script, encode the message using the public key. Try decoding this using the private key. Do you see the original message? Repeat this by swapping the public and private keys. Do you see the same result? Try a longer message msgtext = b ' Good things come to those who wait. ' Consider a suitable procedure for implementing asymmetric key cryptography on arbitrarily long messages.
ECON0016: Macroeconomic Theory and Policy Term 1, Problem set 3 1. Using the IS – MR – PC model Starting from medium run equilibrium, explain the response of the economy to a deflationary shock i.e. one that shifts the Phillips curve down. In addition to describing the paths of output, inflation and interest rates, at each stage describe carefully what happens in (a) The labour market (b) The goods market The economy starts at medium run equilibrium, corresponding to the black points on the diagram, output at equilibrium ye, the real interest rate is the stabilising rate rs, expected inflation is πT, nominal wages and prices are growing at the target inflation rate πT. Period 1 The shock hits, inflation expectations are reduced to a level π1< πT. πT – π1 is a measure of the size of the shock 1. Since output is at equilibrium wage setters want to keep their real wage constant so, given they expect inflation to be π1, bargain to increase their nominal wage by π1 2. Firms immediately set prices as a markup over wages so prices increase by π1 3. The economy moves to the red point, with y=ye, π= π1, r=rs. 4. The central bank is now off its MR curve. Interest rates are assumed only to affect the economy with a one-period lag, so the CB it needs to forecast where the PC will be in the next period. Actual inflation today is π1 so the CB knows expected inflation in the next period will be π1 so forecast5s that the PC will be unchanged at PC1. 5. To get to the intersection of this forecast PC with the MR curve (the green point) in the next period the CB sets interest rates in this period to r1. Period 2 1. The reduction in interest rates in period 1 increases investment (and will also have an affect on consumption) so moves the economy along the IS0 curve. Aggregate demand and output change to y2 2. Inflation last period was π1 so inflation expectations are unchanged. 3. Output is above equilibrium, so unemployment is lower and workers have more bargaining power. They increase their wages by more than expected inflation to reflect this, a total of π2 >π1. The gap π2 – π1 is a measure of the extra bargaining power workers have as a result of lower unemployment/ 4. Firms immediately set prices as a markup over wages so prices increase by π2 5. The economy moves to the green point, with y=y2, π= π2, r=r1. 6. The central bank knows that expected inflation in the next period will equal actual inflation in the current period so forecasts that the PC will shift to PC2. 7. To get to the intersection of this forecast PC with the MR curve (the blue point) in the next period the CB sets interest rates in this period to r2. Period 3 1. The increase in interest rates in period 2 reduces investment (and will also have an effect on consumption) so moves the economy along the IS0 curve. Aggregate demand and output change to y3 2. Inflation expectations are updated to last period’s inflation π2 and the PC shifts to PC2. 3. Output is still above equilibrium, so unemployment is lower and workers have more bargaining power. They increase their wages by more than expected inflation to reflect this, by a total of π3 >π2 (but note they have less bargaining power than in the previous period so π3 –π2 < π2 – π1 4. Firms immediately set prices as a markup over wages so prices increase by π3 5. The economy moves to the blue point, with y=y3, π= π3, r=r2. 6. And so on…. Imagine monetary policy were passive so interest rates stayed at rs. In this case fiscal policy could be used to shift the IS curve to get exactly the same path of output and inflation. If there is a one-period lag in the effect of fiscal policy on output it would move the IS curve to IS1 in period 1 resulting in output y2 in period 2 then IS2 in period 2 resulting in output y3 in period 3. 2. Policy effectiveness (a) Describe how monetary and fiscal policy affect output. NB don’t draw any diagrams, just explain in words the mechanism by which changes in policy transmit themselves to output. · Monetary policy: interest rate changes affect directly investment and consumption. They may also affect asset prices which will further change consumption and investment. In an open economy, there will be effects on the exchange rate which may affect imports and exports. Hence monetary policy affects total aggregate demand and hence, if the goods market clears, output. · Government spending is a component of aggregate demand so affects AD directly. · Tax rate changes work by affecting consumption and investment and hence aggregate demand. (b) Give two reasons (based on the material covered in the course) why either fiscal policy or monetary policy might have no effect on output. Explain your answer in the context of the model · If wages and prices are flexible, the PC will be vertical so changes in aggregate demand have no effect on output · If households have rational expectations, output can only be moved from equilibrium if the policy change is a surprise to households · If investment and consumption are completely insensitive to interest rates the IS curve will be vertical and monetary policy cannot change output · Note that if there are lags in the transmission mechanism of policy, the policy still has an effect on output; it just might not be the desired one as in the example on the “Destabilising policy” slide of lecture 5.
FIN 532 Investment Theory Problem Set 4 Fall 2024 1 The tangible benefits of rebalancing The point of this exercise is to quantify the benefits of rebalancing. Suppose that you are investing in two stock market indices, AAA and ZZZ. Both risky assets have returns that are normally distributed, with the same mean return (10%) and the same standard deviation (20%). Further, the correlation between the two risky returns is zero. Also, assume the risk-free rate is zero. 1. What is the optimal (i.e. mean-variance efficient) allocation among the two securities? 2. Now, we will examine the performance of two strategies over an investment horizon of 100 years. We will do so across many simulations (say 10,000 though you can choose any number you want). For each simulation, (a) Simulate a vector of returns for the two risky assets, AAA and ZZZ. Use your software’s build in random number generator.For example, in Matlab, you can simulate a normal variable with mean μ and standard deviation σ via x = μ + σ × randn, where randn simulates a standard N (0, 1) variable. (b) Compute the returns to your initial wealth of the buy and hold strategy—that is, the strategy in which you allocated optimally at t = 0 and then did not rebalance. (c) Compute the returns to your initial wealth of the rebalanced strategy—that is, the strategy in which you rebalanced at the end of every year to the ‘optimal’ portfolio weights. (d) Compute the realized Sharpe ratio for each of the two strategies above. In addi-tion, compute the average allocation to the first risky asset AAA in the buy and hold strategy (in the rebalancing strategy it should be constant by construction) 3. Plot a histogram of the realized Sharpe ratio across simulations of the two strategies. What is the average Sharpe ratio of each strategy? Also plot the mean portfolio weight to asset 1 of the buy and hold strategy. Discuss your findings. For the purposes of this exercise, it will be best to use a programming language like MATLAB, Octave, or R. It is possible to do it in Excel, though it will be rather painful. To get you started in Matlab, we have posted some sample code for simulating returns in GettingStartedhw4p1.m.
[CMPUT 466/566, Fall 2024] Machine learning Course Project Description Objectives: 1. [10 marks] The basic goal of the mini-project is for the student to gain first-hand experience in formulating a task as a machine learning problem and have a rigorous practice of applying machine learning algorithms. 2. [5 marks] The second goal (optional to undergrads) is to accomplish a non-trivial machine learning project, such as replicating a recent top-tier machine learning publication (published at ICML, NeurIPS, ICLR, etc.), proposing new models, and empirically analyzing machine learning models in a significant way. Replicating a paper published at an unknown or non-machine learning venue may not constitute a non-trivial project. The 5 marks count as bonus for undergraduates but are included within 100 total marks for graduate students. Example non-trivial project: Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations Note that only one project is expected. A non-trivial project must also satisfy the basic requirements. Team work Collaboration for the course project is possible only if 1) all team members have already had first-hand experience, 2) they intend to do a non-trivial project, and 3) the team must have no more than three members. The team has to apply in NOI before the NOI deadline. The application may be declined if any of the team members does not have adequate machine learning background. If teamwork is approved, the team members (name, ID, and email) and individual contributions must be stated clearly in all submissions. All team members must upload the submissions to their own eClass assignments. In case the submitted team project only satisfies the basic requirements without any non-trivial components, all team members will share a total of 10 marks. Timeline and submissions All due time in this section is in Edmonton time. Every submission has a free extension. A project intended to satisfy the basic requirements only (10 marks) does not need to submit the notice of intent or a proposal. They only need to submit the final project, and the deadline is 12:30PM, Dec 10 (extended to 12:30PM, Dec 17). A non-trivial project requires significantly more time than a project satisfying basic requirements only, so a significant amount of time has to be set for the project. It must follow the mandatory timeline: ○ Sep 19 (extended to Sep 24): Notice of intent ○ Oct 17 (extended to Oct 22): Proposal ○ Dec 10 (extended to Dec 17): Final report All deadlines are due by 12:30PM (Edmonton time). A student must decide early if to attempt a non-trivial project. If so, the student must send a notice of intent (NOI) on eClass by the deadline, which can be a message, a title, or a short description. The NOI will not be reviewed but is mandatory for a non-trivial project. If several students intend to form. a group, the NOI must also include every team member’s name, email (ccid), and prior experience in machine learning (such as a short bio). The approval of teamwork will be based on the students’ background and the intended topic. ● Any team member may leave the team unilaterally and submit a basic project. The rest of the team may still attempt a non-trivial project. ● If a team is dissolved but there is dispute on who still owns the non-triviality project, then all members of the team fall into the category of basic projects. ○ Example: A team of two students do not wish to work together, but each claims the ownership of the non-trivial project. Then, both students are ineligible for the non-trivial part. For the non-trivial project, the student is supposed to read literature and prepare experimental environments after NOI. By the proposal deadline, the student must submit a pdf proposal to eClass. The instructor will read the proposal and make a comment, especially on how non-trivial the proposal is. Notice that an intended non-trivial project may not get all 15 marks or, if not satisfying the basic requirements, may not even get 10 marks. Basic Requirements [10 marks]: ● Formulating a task into a machine learning problem. The student CANNOT re-use any task in coding assignments (namely, house price and MNIST datasets) as the course project. ● Implementing a training-validation-test infrastructure, with a systematic way of hyperparameter tuning. The meaning of “training,” “validation,” “test,” and “hyperparameter” will be clear very soon. ● Comparing at least three machine learning algorithms. In addition, include trivial baselines (if possible). For example, a majority guess for k-category classification yields 1/k accuracy. The machine learning algorithms must be reasonable for solving the task, and differ in some way (e.g., having different hyperparameters does not count as different machine learning algorithms). General machine learning packages may be used for the course project. However, the student cannot use the codebase specific to the task at hand and run a few scripts like “sh run.sh” . Requirements for a non-trivial project [5 marks]: A non-trivial project could be either replicating a recent, sophisticated machine learning paper, proposing new models, or conducting empirical analysis of machine learning models in a significant way. Typically, a non-trivial project involves a significant amount of literature reading, programming, and experimentation. A student would not expect any additional marks by trying some CNN/RNN models, or applying existing code base to a new task in a straightforward way. If a student seeks non-triviality marks by replicating a recent paper, the student should assume the code base of that paper does not exist. If a student has doubts about how non-trivial the project is, the student may check how much mathematical and algorithmic formulation there is. Final report submission: The submission must contain a PDF report and the code to reproduce the results. (Non-complying file format will result in mark deduction.) The code should be submitted by a zip file through eClass or through a Google Drive link. If using Google Drive link, the student should ● Make the folder “ Readable” by any university member, but keep the link in their custody except for the submission ● Sharing the folder with the instructor does not work, because the project may not be graded by the instructor ● In the event that the link is not kept in the student’s custody (e.g., sharing the link with friends or publishing the link), the student knows that other people may plagiarize the project. If the project is indeed plagiarized by others, the student is liable for Unauthorized Collaboration under SAIP 4c. Knowingly advising, encouraging, aiding or assisting another person, directly or indirectly, to commit any violation under this policy. [Student Academic Integrity Policy Appendix A, 4c] The format of the report is flexible, but generally, the report should contain ● A short introduction, describing the background of the task ● Problem formulation (what is input, what is output, where did you get the dataset, number of samples, etc.) ● Approaches and baselines (what are the hyperparameters of each approach/baseline, how do you tune them)? ● Evaluation metric (what is the measure of success, is it the real goal of the task, or an approximation? If it’s an approximation, why is it a reasonable approximation?) ● Results. (What is the result of the approaches? How is it compared with baselines? How do you interpret the results?) ● The report should NOT contain code snippets or program outputs. Grading criteria: Basic requirements [10 marks]: ● If the submission is not a machine learning problem, then 0 marks. ● Otherwise, the grading starts from 10 points. If one or more of the above requirements are not fulfilled, it will result in mark deduction for one or a few points. ● Presentation enters the mark in a multiplicative way. The factor is 1 be default, if the report is reasonably well written. If the presented content is not readable, then the project will get 0 marks. Non-triviality [5 marks]: Marking will consider literature review, proposed approach, and experimentation. Statement of Expectations for AI Use: AI tools, including but not restricted to generative models and online translation models, are not allowed. Note: AI-flavored writing demonstrates poor presentation skills. For example, the text is oftentimes grandiose but empty. This will result in a devastatingly low mark (including a mark of 0) by merit, regardless of whether there is proof of using AI tools. Tips: 1. The course project only counts 10--15% of the total marks, and obviously, this course focuses more on math derivations than coding. It is more important to formulate a machine learning system in a rigorous way and complete the project in time than do a super fancy project (which may require too much work and has a risk of not being finished in the course timeline). 2. In fact, many students sought minimal efforts to obtain non-triviality marks in the past, which is not possible. In general, not many students got the 5 marks and students should not worry about it. Even for graduate students, getting a 5-mark deduction is not a problem, because the letter grade cutoff will be adjusted accordingly and may be different from undergraduates. 3. Using external general-purpose machine learning packages is allowed but should be acknowledged (e.g., use libsvm to solve the task by a few lines of function call). However, using a code base directly related to your task is not allowed (e.g., download a Git Hub repo and only write a few lines of script like “sh run.sh”). 4. There is no constraint on the number of pages of the course report. However, the length should reflect the substance of the project, and in a normal case, a few pages suffice. An over-lengthed report will not yield a higher mark. On the contrary, it shows poor presentation skills (and may lead to mark deduction). The report must be written in text with results organized in an appropriate form. (such as tables and figures). Python notebook, code snippets, and program output are not considered as a textual report. 5. We will grade the course project in a lenient way. However, we do not accept mark negotiation. The basic requirements are clearly stated above. The instructor will adjudicate the degree of non-triviality based on the same criteria applied to all students.
Course/Programme: MSc International Business Management Level: 7 Module Title: International Business Assignment title: Formal Report (3500 words) Assignment number: 1 of 1 Weighting: Formal Report - 100%; Date given out: 1 October 2024 Submission date: 10 December 2024 Eligible for late submission (3 calendar days, with penalty)? Yes Method of submission: X Online only Online and paper copy Special instructions for submission (if any): Completed assignment is to be submitted to the UoS Brightspace only Date for results and feedback: 24 January 2025 Learning outcomes assessed: On successful completion of this module, the student will be able to: • Demonstrate a critical understanding of international business and its environment within a global context. • Demonstrate a critical awareness of the global environment, including economic, cultural and sustainability factors that influence the development and management of international business. • Evaluative management perspectives of both domestic and international markets. • Critically analyse different management theories (eg: resource-based view; stakeholder theory; and institutional theory) to understand what is behind different firm performances around the world. • Assess and advise on business operations and relationships (with Joint Venture partners, government agencies) in complex international business environment. At postgraduate level you are expected to: • Have a high standard of presentation, structure, layout and design. • Demonstrate appropriate coverage, critical appreciation and evaluation of relevant literature. • Demonstrate a critical understanding of key concepts and the application of theory to practical solutions. • Show evidence of originality of thought and approach, and of creative problem-solving ability. The grade awarded for this piece of work remains provisional until ratified by the Assessment Board. Component number Formof assessment Assessment size Weighting (%) Learning outcomes assessed Core or non- core 1 Formal Report 3,500 words (+/-10%) 100% All Core Assignment brief: • You are to prepare an individual formal research report. • Secondary research is expected for this written assignment. • Draw your references from a broad range of sources. Apply to the brand, product and market selected, evidence of research and depth of thought will benefit this feasibility study. Objective: This assignment is focused on the B2B distribution channel. You are a SME importing and packaging. For this case, you are importing macadamia nuts and oil to Singapore. This assignment aims for students to have a deep appreciation towards International Business. Case: Importing Macadamia Nuts and Oil to Singapore (B2B) You are a SME importing and packaging a range of nuts from Vietnam and USA, for sales on your and various leading e-commerce platforms. Recently, you have received quite a number of inquiries from customers asking for macadamia nuts. You do not carry any macadamia nuts at this point in time, and you are interested to expand your product range to include this. Research shows that macadamia nuts and oil are a high energy food and contain no cholesterol. This written assignment is focused on the feasibility of importing nuts from either a wholesaler or producer directly from Australia to Singapore for packaging under your own local brand, then to retail on your online shop. The packaging for B2B and B2C will be different in terms of packaging size and labelling. You have identified a few producers of macadamia nuts and oils in Australia. You have shortlisted the supplier, WALIZ NUTS PTY LTD, with address 18 Johnston Road, Newrybar NSW 2479, Australia. Website ishttps://waliznuts.com FCA price based on Minimum Order Quantity (MOQ) is: -1kg-vacuum packed processed Macadamia - Price ranges from AUD47 (for halves) to AUD50 per kg (whole, large) - 18litres Processed natural Macadamia Oil: AUD210 (packed in drum) Please refer to its website for more information. https://waliznuts.com/com There is sufficient information on the company’s website to collect the necessary information to meet this assignment’s requirements. Format: You will need to include the following in your report’s discussion: 1. A brief introduction of the chosen Buyer. (5 marks) 2. As an online seller of nuts in Singapore, discuss and provide justification if the products should be pack and retail under your own existing local brand, or otherwise? (5 marks) 3. Assuming that you would like to import at least 100 kgs of macadamia nuts and 5 drums of macadamia oils for a start, propose the payment methods that would be feasible to both your company and the overseas supplier for this first shipment. Explain your answer. Note that your company is an SME. (15 marks) 4. Describe any TWO (2) factors (e.g. Social-cultural environment, Economy, Regulations, Competition and Political) that would be positive for your business in Singapore. (25 marks) 5. Describe any TWO (2) challenges or risks that should also be considered. (25 marks) 6. Assume the terms of delivery is FCA (from New South Wales – NSW), identify the various stages and logistics processes that will be involved in shipping the goods from New South Wales until it reaches your packing facility cum warehouse in Singapore. Propose either seaport or airport (as the case may be) from exporting country to importing country. Use a well-labelled diagram or flow-chart to outline and explain the transport and logistics processes, including customs clearance that may be involved. (20 marks) 7. Conclusion (5 marks) (Total: 100 marks) Additional notes: • Collusion, similarity, or plagiarism will result in high Turnitin scores that will be flagged out by system, with consequent penalties imposed by the University. • Late submission, non-compliance with format, referencing and prescribed page number will result in penalty. • The range of research to support the quality of discussion must meet the standard of a postgraduate student. Format - further guidance: • Assignment Cover Sheet o Module Name o Assessment Title o Word Count o Student ID number • You will include a correctly formatted reference list and in-text citations using UoS Harvard referencing format.Refer to http://libguides.uos.ac.uk/friendly.php?s=academicskills/referencing. • Your work should be presented in a report format, using headings and including a content page and page numbers. • Please choose a clear font (either Arial or Times New Roman) using size 12. • Please ensure your work is 1.5 spacing. • Your total word limit is approximately 3,500 words (+/-10%). Employability Skills By the end of this module, students will have gained competence in the following key areas: employability skills; independent learning; research; critical thinking and analysis; problem solving; interpersonal skills; communication skills.
N1577 Principles of Banking Seminar 4 GAP and DGAP analyses Exercise 1 Consider a bank that accepts a 18-month $30,000 Certificate of Deposit and invests the funds in a $30,000 6-month T-Bill . a) What is the bank’s 6-month GAP? b) Calculate the change in NII if interest rates increase by 1 percentage point. Exercise 2 If rate-sensitive assets equal £100 million and rate-sensitive liabilities equals £80 million, what is the expected change in net interest income if rates increase by 1 percentage point (Assume a parallel shift in the yield curve)? Exercise 3 a) Consider the following bank balance sheet. Calculate NII, Earning Assets, NIM and GAP. Balance Sheet Assets Yield Liabilities Cost Rate sensitive $ 600 8.0% $ 450 4.0% Fixed rate $ 250 11.0% $ 370 6.0% Non earning $ 150 $ 100 $ 920 Equity $ 80 Total $ 1,000 $ 1,000 For the following questions, also calculate NII, Earning Assets, NIM and GAP. Make necessary assumptions where appropriate. b) What if interest rates increase by 1 percentage point? c) What if interest rates decrease by 1 percentage point? d) What if interest rates rise and at the same time the spread falls by 1 percentage point? e) What if the bank proportionately doubles in size? Exercise 4 You are provided with the following balance sheet of a bank: Assets Market Value Rate Duration Liabilities & Equity Market Value Rate Duration Cash £200 0% 0 Time Deposit £1,100 3% 1 Treasury Bond £600 5% 2.86 Certificates of Deposit (CD) £700 5% 3.72 Commercial Loan £1,200 11% 4.70 Equity ??? a. What is the bank’s Economic Value of Equity (EVE)? b. Calculate the Interest Income from Assets. c. Calculate the Interest Expense on Liabilities. d. Calculate the value of the Net Interest Income. e. Calculate the weighted duration of the assets? f. Calculate the weighted duration of the liabilities? g. Calculate the Duration GAP. h. Based on the bank’s duration GAP, provide the possible strategies to reduce interest rate risk. Exercise 5 Conduct duration GAP analysis using the following information: Assets Amount Rate Macaulay’s Duration (years) Cash £23,000 0% 0 Bonds £102,000 7.2% 1.8 Commercial Loans £375,000 11.0% 1.5 Liabilities & Equity Amount Rate Macaulay’s Duration (years) Small time deposits £130,000 3.6% 4.0 Large CDs £70,000 6.3% 1.0 Transaction accounts £250,000 2.8% 3.3 Equity £50,000 a. Calculate the bank’s duration GAP. Is this bank positioned to gain or lose if interest rates rise? What would be the change in EVE if all market interest rates fall by an average of 1.5 percentage points? b. Provide a specific transaction that the bank could implement to immunise its interest rate risk. The transaction may be a new asset funded by a new liability or an asset sale and a simultaneous purchase of another asset.
CMPUT 466 566 Machine learning Problem 1. Let be the linear hypothesis class. Prove that for, any in their linear combination is also in where Problem 2. Consider a two dimensional space ℝ2 . Determine whether the following sets are convex or not. Prove or disprove. Problem 3. Consider the function a) View x1as a variable and x2 as a constant. Determine whether f is convex in x1 and prove it. b) View x2as a variable and x1 as a constant. Determine whether f is convex in x2 and prove it. c) View f: ℝ2 → ℝ as a function of the input vector (x1, x2) . Determine whether f is convex in (x1, x2) and prove it. Hints: For a) and b), treat one variable as a constant, and calculate the second-order derivative of a single-variable function. For c), calculate the Hessian matrix H first and choose a point, say, (0,0). You may use numpy in Python to calculate the eigenvalue import numpy as np from numpy import linalg as LA H = np.array ( [ [11, 12], [21, 22]]) # your values here eigenval, eigenvec = LA.eig(H) Print eigenval. If any number is less than 0, then the function is not convex. Otherwise, it is convex. Eigenvalues may also be calculated manually. The example shows that an element-wise convex function may not be jointly convex.
