Summative Assignment 1LC Data Structures and Algorithms due date 5 March 2025, 12pmYou are given an n × m matrix with integer entries that has the following properties:(1) Each row has a unique maximum value,(2) If the maximum value in row i of the matrix is located at column j, then the maximum value in row i+1 of the matrix is located at a column k, where k ≥ j.The goal of this assignment is to find the maximum value in such a matrix.Question 1. Write a function maxIndex that finds the index of the maximum entry of of a row between columns with indices start and end inclusively. The row is given as an array row. What is the time complexity of your solution? Explain your answer.Question 2. A rectangular block of a matrix is given by a row and column of the upper-left corner in startRow and startCol, and row and column of the lower-right corner endRow and endCol, such that startRow ≤ endRow and startCol ≤ endCol. Write a function blockMaxValue that finds the value of the maximum entry of a given block assuming that the block satisfies the properties (1) and (2) above.Hint: Use the divide-and-conquer strategy.Question 3. Write a function matrixMaxValue that finds the maximum value of a matrix that satisfies properties (1) and (2) above, and provide a better upper bound for the time complexity of this function than O(nm). Explain your answer.Hint: The complexity of linear search is O(nm), do better than that!SubmissionSubmission is via Canvas, and it should contain two files:• Java source code named ‘solution.java’ containing a class Solution with the following meth- ods: public class Solution { public static int maxIndex(int[] row, int start, int end); public static int blockMaxValue(int[][] matrix, int startRow, int startCol, int endRow, int endCol); public static int matrixMaxValue(int[][] matrix); }Do not rename the class or the methods, otherwise your solution will fail the test cases.• A text/pdf file containing the explanation of the complexity of your code.1
ENGINEERING 2125, Spring 2024 Engineering Management & Decision Making Written Assignment Guidelines “Dragonfly Network Diagram ” GENERAL INSTRUCTIONS Access and read the Dragonfly case from the HBS coursepack. Submit a .pdf report in which you respond to the writing prompts and the analysis prompts. IMPORTANT NOTE: Use the duration and cost tables provided in this assignment, rather than those provided in the case. WRITING PROMPTS The Dragonfly case follows an unmanned aerial vehicle (UAV) project. For each of the prompts, provide a brief answer (2–3 sentences). Writing Prompt 1: When the project being described in the case is completed, does it result in (a) a new UAV design and prototype, or (b) a plan for a project to create a new UAV design and prototype? Cite evidence from the case that supports your conclusion. Writing Prompt 2: Would you say that Table 1 in the case represents an RBS or a WBS? Why? Writing Prompt 3: With data in Table 1 and Table 4, compute the projected cost per day for each of the activities. Which activity has the highest cost per day? Why do you think this task is particularly expensive per day? (On the basis of the limited information provided, this may be somewhat of a guess!) Writing Prompt 4: What information in the case suggests that this firm, IACo, is well positioned to carry out this project? Writing Prompt 5: What information in the case suggests that this firm is perhaps not especially experienced in performing a project like this? ANALYSIS PROMPTS 1) For the proposed project, draw a network diagram of the sort shown in Figure 7.15 of the Wysocki textbook. Base your diagram on the “activities” shown in Table 1 and the “design structure matrix” shown in Table 2 of the case. (See the “hint” below on how to interpret Table 2.) Ignore the dependency shown between activity A9 and A4 that is shown in Table 2. To draw the network diagram, you can use any method: hand sketch, graphics software, presentation software, project software, etc. 2) Using the substitute duration data provided below for the “Expected Durations (days)” in Table 1, calculate the total number of days required to complete the project. Provide the “task path” through the network diagram that sets the total project duration (the “critical path”). 3) Using the substitute duration data provided below for the “ Best case time estimates per activity” in Table 3, calculate the best case total numbers of days to finish the project. Provide the “task path” through the network diagram that sets the total project duration. 4) Using the substitute duration data provided below for the “Worst case time estimates per activity” in Table 3, calculate the worst case total number of days, to finish the project. Provide the “task path” through the network diagram that sets the total project duration. 5) Using the substitute cost data provided below, and the additional information provided in the paragraph immediately following (top of page 5 of the case), compute the cost to complete the project for the Base Case, Best Case, and Worst Case scenarios. You will provide a different answer for each of the different scenarios. HINT FOR INTERPRETING TABLE 2 Table 2 provides the “predecessor” information for the activities described in Table 1. To determine the predecessors for any given activity, go to the corresponding row (“Dependent Activity”) and read across the row looking for “tick marks” (a vertical bar in a table cell) that indicate a predecessor activity (“Input Providing Activity”). For example, A2 has predecessor A1 A3 has predecessors A1 and A2 A4 has predecessors A1 and A2 A5 has predecessors A1 and A4 SUBSTITUTE DATA Use the data provided here for durations and costs, rather than those provided in the case. Activity Base Case (Days) Table 1 Best Case (Days) Table 2 Worst Case (Days) Table 3 Cost Estimates (ε1000) Table 4 A1 10 8 13 12 A2 4 3 6 5 A3 8 6 16 5 A4 10 5 10 130 A5 12 9 12 15 A6 9 7 14 15 A7 20 16 28 28 A8 13 11 19 19 A9 16 14 21 95 A10 5 3 8 30
Final Exam 600.435 Artificial Intelligence Spring 2017 Intelligent Agents 13 points 1. (8 points) Consider a self-driving caras an intelligent agent. Name two items for each of the PEAS elements. • Performance measures: • Environment: • Actuators: • Sensors: 2. (5 points) Consider a computer program that is playing online poker. Is the environment of this intelligent agent ... • Fully observable? • Deterministic? • Static? • Discrete? In which of these categories is a Go playing program different? Just provide a yes/no answer to each of these questions. Basic Search 10 points In the following grid, we want to find the fastest path from A to G. We may move from every cell to each directly neighboring cell (up, down, left, right). The cell in the center of the grid is unreachable. A b c d e f G h 3. (7 points) Draw the full search tree of breadth-first search that explores this grid. • Note that search may return to already visited cells. • The search graph must include all leaf nodes at maximum depth. 4. (3 points) If we use depth-first search and move • up, whenever possible • right, whenever possible (and up is not possible) • down, whenever possible (and up and right is not possible) • left, otherwise Again, search may return to already visited cells. Will depth-first succeed? If yes, in how many steps? If no, why not? Informed Search 10 points Consider the search space below, where S is the start node and G1 and G2 satisfy the goal test. Arcs are labeled with the cost of traversing them and the heuristic cost to a goal is reported inside nodes (so lower scores are better). 5. (10 points) For A* search, indicate which goal state is reached at what cost and list, in order, all the states popped o『 of the OPEN list. You use a search graph to show your work. Note: When all else is equal, nodes should be removed from OPEN in alphabetical order. Path to goal (cost): States popped off of OPEN: Game Playing 12 points Apply the mini-max algorithm to the partial game tree below, where it is the minimizer’s turn to play and the game does not involve randomness. The values estimated by the static-board evaluator (SBE) are indicated in the leaf nodes (higher scores are better for the maximizer). Process this game tree working left-to-right. 6. (5 points) Write the estimated values of the intermediate nodes inside their circles. 7. (2 points) Indicate the proper move of the minimizer by circling one of the root’s outgoing arcs. 8. (5 points) Circle each leaf node (if any) whose SBE score does not need to be consulted. Briefly explain why: First Order Logic 15 points 9. (5 points) Convert the following sentence into first-order predicate calculus logic: There is a lacrosse player who has played in every game this season. 10. (10 points) Given the following propositional-logic clauses, show E must be true by adding ¬E and using only the resolution inference rule to derive a contradiction. Use the notation presented in class (and in the book) where the resulting clause is connected by lines to the two clauses resolved. • A ∨ ¬C • B ∨ ¬A • B ∨ ¬D • E ∨ ¬A ∨ ¬B • C • ¬D Probabilistic Reasoning 10 points 11. (5 points) Suppose we roll a die 2 times. What is the probability that the sum of the numbers is 3? 12. (5 points) In the general population, 0.1% have the disease X. Doctors developed a test with the following properties: • If a person has the disease, the test detects it 90% of the time, and fails to detect it in 10% of the time. • If a person does not have the disease, the test gives false positive indication 10% of the time, and a correct negative indication 90% of the time. What is the probability that a random person who receives a positive test result, does indeed have the disease (round numbers when appropriate)? Bayesian Networks 15 points Consider the following Bayesian Network, where variables A–D are all Boolean valued (when answering the questions be sure to show your work, if a computation gets too convoluted it’s fine just state terms with numbers): 13. (5 points) What is the probability that all four of these Boolean variables are false? 14. (5 points) What is the probability that A is true, C is true and D is false? 15. (5 points) What is the probability that A is true given that C is true and D is false? Reinforcement Learning 15 points Consider the deterministic reinforcement environment drawn below, where the current state of the Q table is indicated on the arcs. Let γ = 1. Immediate rewards are indicated inside nodes. Once the agent reaches the end state the current episode ends. 16. (5 points) Assuming our RL agent exploits its policy (with learning turned o『), what is the path it will take from start to end? Briefly explain your answer. 17. (5 points) Assuming the RL agent is using one-step Q learning and moves from node start to node b. Report below the changes to the graph above (only display what changes). Assume a learning rate of α = 1. Show your work. 18. (5 points) Show the final state of the Q table after a very large number of training episodes (i.e., show the Q table where the Bellman Equation is satisfied everywhere). No need to show your work nor explain your answer.
Students of ELEN E6767: Instructions for Final Paper Deadline is 8 pm, Friday, December 13 (hard deadline) (i) The length should be about 5 printed pages, font size 11-13, single-sided. I am flexible about the length of the paper. Students should submit the paper in pdf or Word formats. Please remember to state your name in the first page. (ii) I am viewing students as researchers. Therefore I am giving students plenty of flexibility in the choice of their paper topic. However, I require that the topic be connected in a meaningful way to the course. This means that the Final Paper is required to be related to some concept or model or method in any paper that was discussed or referenced or pointed to in the course. The paper should make clear what the connection is. I am interested in finding out how well the course material has been internalized. (iii) My expectation for the Final Paper is that it is a small research thesis. In this respect the Final Paper is similar to the Mid-Term Paper. However, it is different in that I expect the Final Paper to be more mature, i.e., present a broader and deeper view of issues, which is informed by what you have learnt in the course. It is OK to build on a base provided by the Mid-Term Paper and/or Project, but the Final Paper should be qualitatively different. Be careful since it is too easy to think that the work is different and new when it is not. (iv) The Final Paper should have the following characteristics. First, there is a summary of a literature search. Second, there is an original assertion or a point of view. Third, there is validation of the assertion. The validation can come in diverse forms. It can come from a mathematical model, or a qualitative argument, or it can come from a sequence of linked arguments. Data can play an important role in the validation of the assertion. Fourth, there is a list of numbered references. I require students to specify the external source, if any, of any content in the paper; this is done in the same sentence as the related content by giving the number in the reference list of the source. These references are essential if you have imported ideas or material - always a good thing to do, but ethics demand that it be noted and recorded. (v) I will welcome your thoughts on the incompleteness of models, but then you should also provide input on how this can be fixed. Similarly, I will also welcome ideas on how current models are relevant or not, but then you should specify and explain how the relevancy of the model can be fixed and what researchers have done in this respect. By a model “extension” I do not mean necessarily a mathematical extension; the model extension can be qualitative and conceptual. Relevant data can be a plus. Yet another possible avenue is an application of the methods and models developed in the course to a new, real world problem. The use of ChatGPT and other LLM is not prohibited, but please note the following. My experience with these tools is that they are strong in summaries and coverage, and weak in originality and in presenting a personal point of view. Yet it is the latter properties that I am looking for. Also, I treat papers harshly that do not acknowledge the original source of key ideas and data. All science is based on “standing on the shoulders of giants”, while acknowledging such help. No compromise here. Papers which mainly just summarize course material or any other external source, or largely recycle material presented in the Mid-Term and/or Projects will not be viewed favorably. I am looking for your personal, original insights. Originality, intellectual depth and evidence that the course material has been internalized are what I am primarily looking for. You should feel free to consult Bert Steyaert (bws2122) or Megan Manik (mgm2206) or me regarding choice of topics, etc. All students should upload their papers to Courseworks no later than 8 pm ET, Friday, December 13.
Final Exam 600.335/435 Artificial Intelligence Fall 2015 Intelligent Agents 1. (4 pts) Consider an email spam filter as an intelligent agent. Every 10 seconds the agent runs a script, ’check.py’, that checks the user’s inbox and returns unread messages (or nothing). For all unread mes- sages, another script is then run called, ’filter.py’, which classifies each unread email as either spam or non-spam and then sends that email to the either the inbox or the spam folder, depending on the classifi- cation. Give an example of a sensor, a percept, an effector, and an action for this agent, and then give an example of a function that maps from the agent’s percepts to an action. The function should include two possible mappings that map two different percepts to two different actions (i.e. it should be bijective). 2. (6 pts) A student is developing a robot that can roll a die. The robot grips and releases the die with an arm that can also twist, and uses two cameras that can see the entire rolling area at once for feedback. The goal is to develop methods that can roll the die in a way that increases the chance of landing on a desired side of the die. Please consider the environment that is the robot, die, and rolling area. Is this environment (justify your answers!): • Accessible? • Deterministic? • Episodic? • Static? • Discrete? 3. (2 pts) Now consider the case that the robot from the previous question is being programmed to roll dice in a competitive game. A group of robots are all collected into one area, and each given a die. If a robot rolls a die that brings the sum of rolled values to an even number, that robot is eliminated from the run- ning. These games are played again and again with the robots that haven’t been eliminated until only one winner remains. The rolling area has been expanded to accomodate the other robots, and as a result the robot now has to sweep this larger area in order to see all of the already rolled dice. In addition, the robots don’t have to take turns. The robot should be constantly strategizing over what kind of roll to perform depending on the values of the other dice that have already been rolled. Which of the answers to the previous question have been changed, and why? Basic Search 4. (2 pts) Fill in the nodes of the above tree in the order they would be explored in a depth-first search (assume left to right traversal). 5. (6 pts) Compare depth-first searching to breadth-first searching. When are depth-first searches the supe- rior choice and why? Give a real-world example. 6. (6 pts) Give the time and space complexity of depth-first searching in terms of its branching factor, b, and search depth d. Is this algorithm optimal? Why/why not? 7. (2 pts) Fill in the nodes of the above tree in the order they would be explored in an iterative deepening search (again, assume left to right traversal). 8. (6 pts) Compare iterative deepening searching to breadth-first searching. When are iterative deepening searches the superior choice and why? Give a real-world example. 9. (6 pts) Give the time and space complexity of iterative deepening searching in terms of its branching factor, b, and search depth d. Is this algorithm optimal? Why/why not? Informed Search 10. (4 pts) Given the above pacman search problem, please show the order of node expansion for a uniform- cost search. Assume west, north, east, south order of traversal. 11. (6 pts) Given the above pacman search problem, please show the order of node expansion for an A* search with manhattan distance as the heuristic. Assume west, north, east, south order of traversal. 12. (6 pts) Is the manhattan distance function considered admissible? Why or why not? Why is admissibility important for A* searching? 13. (3 pts) Given two admissible heuristics h1 and h2, circle the following heuristics that are also admissible (note: there maybe more than one!). (a) max(h1, h2) (b) h1 + h2 (c) max(h1, 2 * h2) (d) min(h1, 3 * h2) (e) 2/h1+h2 14. (2 pts) Prove (briefly) maxnext (jxcurrent - xnext j , jycurrent - ynext j) is an admissible heuristic if you are nav- igating the movement of the king piece on a chessboard with obstacles, moving from the bottom left to the top right. Game Playing 15. (6 pts) I am trying to develop a method that will determine the best chess move for any given turn. The way Ido this is by constructing a tree, where each node is a configuration of the board and each branch is a different valid move from the parent node’sconfiguration. I fully expand this tree with all possible valid moves to each possible end state (and mark it as a victory or loss). Ithen simply find the next move that has the greatest possibility of leading to a favorable win state by counting the number of "victory" leaf nodes that each move could lead me towards and going with the move that has the greatest number. There is a problem with this method. Please specify what the writer of this method is misunderstanding, and what modifications to the algorithm he/she could make to avoid this problem. 16. (4 pts) The above tree represents a two-player game where each player takes turns making a move. The first move is made by us, the second is made by the opposing player, and so on. Circle the nodes in this tree that are explored according to a minmax algorithm. 17. (6 pts) Put a strikethrough the nodes that would be pruned according to α - β pruning. 18. (4 pts) Tic-Tac-Toe is a game that is considered "trivially solved." Please explain what it means when a game is "solved." What is it about the structure of tic-tac-toe that makes this game trivial to solve? Please be specific. First Order Logic 19. (6 pts) Simplify the following propositional logic statement as much as possible and translate into english (hint- remove implications): (A ⇒ B) ∧ (¬A ⇒ C) 20. (3 pts) Translate the following english sentence into first-order logic: Not all those who wander are lost 21. (4 pts) Is the following statement valid? What about satisfiable? (A ⇒ B) ⇐⇒ (B ⇒ A) 22. (8 pts) Please examine the following statements for this question. Using resolution, prove that statement 1 follows from statement 2. You should either complete the proof (if possible), or continue until the proof breaks down and you cannot continue (if impossible). Show the unifying substitution in each step. If the proof fails,explain where, how, and why it fails. 1 ∃y∀x(x ≥ y) 2 ∀x∃y(x ≥ y) Probabilistic Reasoning 23. (2 pts) Suppose we flip a fair coin 3 times. What is the probability that all three land on heads? 24. (2 pts) Now suppose we again flip a fair coin 3 times,but this time we know that 2 of them have landed on heads. What is the probability that all three land on heads? 25. (4 pts) Given the probability space of 3 coin flips, describe an event that is disjoint from flipping 3 heads in a row and show why. For the next three questions, suppose we have two continuous random variables,X,Y, whose joint density function is defined by: 26. (2 pts) What’s the probability that X ≥ 0 Λ Y ≥ 0? 27. (4 pts) Are these two events independent? Explain why or why not. 28. (4 pts) Are the two events X ≥ a and Y ≥ b, where a, b ∈ R and -1 ≤ a ≤ b ≤ 1, independent for arbitrary a and b? Explain why or why not (e.g. give a counterexample)? Bayesian Networks 29. (3 pts) Draw a Bayesian network that contains 3 variables where the factorization of their joint probability distribution is P (A,B, C) = P (B j A)P (C j A)P (A) 30. (3 pts) Draw a Bayesian network that contains 3 variables where the factorization of their joint probability distribution is P (A,B, C) = P (C j A, B)P (A)P (B) 31. (3 pts) Write the factorization of the joint probability density of the following Bayesian network: 32. (3 pts) Write the factorization of the joint probability density of the following Bayesian network: Markov Decision Processes Use the hidden markov model below for the following two questions. 33. (10 pts) Examine the hidden markov model. Please describe what it is trying to represent, and what in- formation about this system its structure and probabilities tell you. 34. (4 pts) Given that the hidden state is "not hungry" at time t = 1, what is the probability that the hidden state is "slightly hungry" at time t = 3? Reinforcement Learning The above picture illustrates an icy grid that our agent has stochastic movement within. Note that 60% of the time the agent is able to make its desired motion successfully, but 40% of the time the agentslips and accidentally moves an extra space in the same direction. Suppose we want to use reinforcement learning to learn how to best control our agent in such a situation. We will be examining both passive and active reinforcement learning methods. If the goal state is reached, our agent has completed its task successfully. If the agent enters the hole, however, it is considered a failure. 35. (5 pts) In both the passive and active reinforcement learning methods, we will need to define a reward function R(s). Please state the purpose of such a function, and define one for this grid. Remember to factor in both reaching end states and normal movements within the grid. 36. (5 pts) Examine path 1 as shown in the figure. Note that this path results in a failure state. If we update a utility-based passive agent and then a q-learning agent both using this same run, what exactly is updated in each case and how? Please define the purpose of the functions that are modified. No explicit formulas are needed. 37. (6 pts) Say that these agents have just gone through path 2 as shown in the figure. Note that this path results in the goal state. Based on this run, what exactly is updated in each case and how? How are these updates different than the ones based on a failed run? 38. (7 pts) Say that these agents have just gone through path 3 as shown in the figure. Note that this path results in the goal state and has some overlap with path 2. Based on this run, what exactly is updated in each case and how? Please examine the state that path 2 and path 3 have in common and note that they reach the goal after leaving this state with a different number of steps. How does this fact affect the way that the agents’ functions are updated based solely on this one state they have in common?
Math 132A Assignment 5 1. Apply the Quasi-Newton method with the SR1 update formula to locate the minimizer of Start with and B0 = I. At each step calculate the optimal step size. Just determine the first two iterates x(1) and x(2) (unless you want to do more!) 2. Same as Question 1 but now use the BFGS update formula instead. 3. Same as Question 1 but now use the Conjugate-Gradient Method (do three steps this time so you get the actual solution!)
Assignment 1: Financial Analysis You work at a Software as a Service (SaaS) company that has a suite of software programs to help businesses operate more efficiently. The market is rapidly evolving and many of the company’s clients are asking for a new solution that would be a natural addition to the company’s suite of SaaS products. There is a startup that specializes in this product and they are already selling to in your target market. As a result, your organization has three options: 1. Build a technology product in-house 2. Collaborate with the startup 3. Do nothing You are tasked with developing a recommendation for the CEO of your company. Your CEO believes that developing R&D capabilities is critical to the company’s future because of the rapid pace of change in technology and therefore, the short commercial life of products. Research You explore how much it would cost to build a product with your own technology team. You work with the sales team to understand the revenue potential of your product. You develop the following projections: · It would cost $3M to build the solution in-house and it could be ready to launch into the market in one year. · The new solution would target your current user base, rather than attracting new users. Revenue from the new product is forecast to be $1.5M in Year 1, growing by $160K annually. · Ongoing operating costs (after the $3M investment) to service the solution and provide routine enhancements would be 40% of revenues. · The technology landscape is rapidly evolving so the software is expected to have a useful life of 5 years. The CEO of the startup is willing to consider a joint venture and has proposed the following: · An upfront investment of $4M in year 1. · Your company incurs 50% of the operating costs, and retains 60% of the revenues. · Revenues are forecast be $3.2M in Year 1 and increase by $200K annually. · Ongoing operating costs (after the acquisition cost) to service the product and provide routine enhancements would be 50% of revenues. If you do nothing, you estimate that you will lose some of your current customers. However, you believe you can stem the loss if you invest heavily in advertising. You develop the following projections: · You can make an upfront (year 0) investment of $25K in a 5 year advertising contract. · Operating costs are 40% of revenues. · The incremental revenue is forecast to be $100K in Year 1, declining by $25K annually. The CFO of your company has told you to use 8% cost of capital for each scenario. Your Task 1. Calculate the following for each of the 3 options: (25 Points) · The Net Present Value (NPV) of the entire scenario · The five-year ROI on the original investment · The five-year Internal Rate of Return 2. What recommendation would you make to the CEO regarding the best option (Build vs. Joint Venture vs. Do-nothing-advertise)? Explain your rationale using NPV, 5-year ROI, and 5-year IRR as metrics. (25 Points) 3. The head of your IT department believes that there is a 75% chance that the in-house build will be delayed by one year due primarily to the shortage of skilled personnel in a tight labor market. Does this change your recommendation to the CEO? Explain your rationale using NPV, 5-year ROI, and 5-year IRR as metrics. (25 Points) 4. The head of your IT department has determined that hiring contractors will mitigate the risk of a delay, but it would increase the build cost by $400K. What recommendation would you make to the CEO? Explain your rationale using NPV, 5-year ROI, and 5-year IRR as metrics. (25 Points) Assessment Point values for each question are stated above. Each question will be evaluated based on the following criteria: 1. Your understanding of the uses of balance sheet and income statement metrics and ratios. 2. Your ability to derive insights from the data provided. 3. The clarity of your logic, and the accuracy of your answers.
Java Lab 3 Due: Friday, September 8, 11:00 AM EDT In this lab, you will practice with simple input and output and if/switch statements. 1. Create a project named Lab3. Download the file IOStuff.java from Canvas and copy it to the src directory. Run the program; enter some sample data when prompted. Then run the program again. See if you can break the program by entering bad values – which input lines can you break? 2. Change the line that reads in the age to the following: create a String variable named ageString; read the age into it using nextLine( ) instead of using age and nextInt( ); convert the String to an int using Integer.parseInt( ) and store it in age. Do something similar with the line that enters the salary: create a String variable named salaryString; read it using nextLine( ) instead of nextDouble( ); convert the String to a double using Double.parseDouble( ). Can you break this by entering bad values? 3. Surround the code for #2 with this: try { } catch (NumberFormatException e ) { System.out.println(e); age = 0; salary = 0.0; } Can you break this? 4. Comment out the println statements that display the output by highlighting those lines, then use the IntelliJ menu choice Code->Comment with Block Comment Add one printf( ) statement to display the labels (column headers) and another printf( ) statement to display the data as a (very short) table, with nicely spaced columns, something like this: 5. First, comment out the code that prompts the user to enter employee data at the keyboard, down through and including the answer to #3. Keep the table headers of #4 but comment out the line that prints the employee data (you'll be doing it over below). Now download the file "employee.csv" from Canvas. Copy it into the project directory (the root Lab3 directory, *not* the src folder). Open it in IntelliJ to see the format – it's just the same data as above as a Comma Separated Value file. Adapt the code from the notes (reading input from a file) to read the file. You'll have to split the data (see the String notes) and convert the age and salary fields to numeric; don't use a try-catch block for these, although a real program should – just use parseInt( ) and parseDouble( ) and hope for the best. Then display a formatted table something like this (adapt the code from #4, too): 6. Now add a new column to the output called "Category". Create a String variable named category and initialize it to null. If the employee’s salary is either negative or greater than 150000, reset category to "Error". If the employee's salary is at least 0 but less than 35000, re-set category to "Low"; if it's at least 35000 but less than 70000, set category to "Medium"; and if it's at least 70000, set it to "High". Display category in the last column. Your output should look something like this: Deliverable: Add your name and Andrew id to the comment at the top of the file. Upload the file to Canvas.
UESTC4003 - Control Computer Lab Exercise 1 MATLAB for Control Engineering 1.0 Objectives The primary aim of this class is to: • Setting up Control problems in MATLAB • Evaluate complex function via vectors • Create and analyse Root Locus plots • Create and analyse frequency domain plots 2.0 Basic MATLAB commands and their use 2.1 Use of MATLAB environment for elementary calculations 2.1.1 Fundamental expressions Enter the following simple expression >> a = 2+3 Always remember to press return key after typing. The result is assigned to variable “a”, i.e. a = 5. Then try your own arithmetic expressions using the operators “ + ” , “ - ”, “ * ”, “ / ”, “ ^ ”. Use MATLAB to evaluate → x = 4/6 − √[2(3 + 8)] 2.1.2 Reassign Variables Type who command to see a list of defined variables in alphabetical order, and type one of these variables and press return to see its current value. Now reassign the value of that variable to a different value. 2.1.3 Predefined variables MATLAB has several predefined variables. i, j – complex number. Try → (3-5j)+(4+2i) pi – stands for π . Try → pi Inf – stands for ∞ . Try → 5/0 NaN – stands for not a number. Try → 0/0 2.1.4 Built-in functions MATLAB contains number of built-in functions to perform computations. Test below functions, where relevant use MATLAB help to understand formatting of each function (see section 2.3 regarding MATLAB help). abs, sqrt, round, exp, log, sin, real 2.2 Edit previous commands Use the cursor keys to edit (left/right arrow keys) and find previously entered commands (up/down arrow keys). Find “x” and change “6” to “a” . 2.3 MATLAB help Entering “help” at the MATLAB prompt displays a list of MATLAB help topics. Type >> help control to see the table of contents of control system toolbox help. 2.4 Creating Script files Script. file contains series of MATLAB commands, where it is easier to use script. files rather than enter commands at the MATLAB prompt. Each script file should have a name and an extension of “ .m” . Script file can be executed by entering the name of the file in the MATLAB environment. Create a script. file to estimate total surface area of a cylinder and save as “area_cylinder.m” . Don’t forget to define radius and height at the start. 2.5 General commands clear all All variables are removed from workspace clear a b c a, b and c variables are removed clc Clears command window c kills the current command execution quit quit MATLAB 2.6 Vectors and Matrices A row vector with 4 elements can be defined as below. >> A = [12 42 3 -27] When element-by-element involve some mathematical operations, the operator is preceded by a dot, .e.g. A.*2 Convert 5oC, 10oC, 15oC, … , 50oC into Fahrenheit scale, convert all cases at once. If elements are entered with a semicolon between the elements, then it becomes a column vector. >> A = [12; 42; 3; -27] In matrices elements in the same row separated with a space and different rows with a semicolon. B= [2 3 -6; 4 8 1; -3 2 0] Tests with the functions ones, zeros and eye, and create 5 x 5 matrix with all its elements equal to 2.6. 3.0 Setting up control problems in MATLAB Consider the system described by: Define two row vectors to represent numerator polynomial and denominator polynomial of the function G(s). Use “num” to represents the numerator polynomial and “den” to represent denominator polynomial. Use conv function to multiply polynomials where relevant (hint: use help conv to see how conv works). Now use printsys command to print transfer function as a ratio of the two polynomials. Alternatively, tf function in MATLAB can be used to create the same transfer function. Plot the unit-step response of G(s). Consider the below control system and assume K=1 and H(s)=1. Find the closed-loop transfer function with use of feedback function in MATLAB. Also find the closed-loop transfer function when Now plot the step response for each case of H(s) when K =500. Write G(s) manually in terms of its partial-fractions. Then use residue function in MATLAB to check the answers. Now consider the below system. Find the closed-loop transfer function (assuming K =1) using MATLAB and hence find the poles and zeros of the system. Now recalculate the poles and zeros of the system, when a) K = 7.5 b) K = 13 c) K = 25 4.0 Evaluate complex function via vectors (Exercise 1A) Consider the below system. 4.1 Define point s and G(s) in MATLAB, where s = −3 + j4. 4.2 Now evaluate magnitude M andangle θ for G(s) at points. Make sure to state the angle in degrees. 4.3 Repeat part 4.2 using manual techniques and verify the answers. 5.0 Root Locus analysis (Exercise 1B) 5.1 Sketch the root locus for the system that has the forward transfer function: 5.2 Consider unity feedback system that has the below forward transfer function: a) Define G(s) in MATLAB and sketch the Root Locus. b) Find the imaginary-axis crossings by clicking on the appropriate points on the plotted root locus and also the corresponding K (hint: check help rlocfind). c) Plot 0.45 damping ratio line and find where the Root Locus crosses. Also find the corresponding K (hint: check help sgrid). d) Find the range of gain, K, for which the system is stable. e) Now plot both 5.1 and 5.2(a) in the same diagram. References Gene F. Franklin, J. Da Powell, Abbas Emami-Naeini, Feedback Control of Dynamic Systems, 7th Edition. Katsuhiko Ogata, Modern Control Engineering, 5th Edition.
ACCT420 Sections 1 and 2, Spring 2025 (Please do the following question by handwriting and submit the assignment on 25 Feb 2025 Individual Assignment 1 – Property tax computation Dickson Lee owns a residential property in North Point which was let to Mr. Xi on the following terms: (i) Lease term: two years from July 2022 to June 2024. (ii) Rent: $22,500 per month payable on the first day of each month (iii) Rental deposit: $45,000 payable on signing the lease agreement. This could be used to offset the rent long overdue by the tenant. (iv) Rates of $3,780 per quarter were payable by the owner. (v) The building management fee of $1,000 per month was payable by the owner during the lease period. Mr. Xi determined to end the lease agreement at the end of June 2023, this was accepted by Lee. Lee repossessed the property and spent $30,000 on renovation. It was let to Mr. Yan on the following terms: (i) Lease term: Four years from 1 October 2023 to 30 September 2027. (ii) One-month rental free period: October 2023. (iii) Rent: $21,600 per month payable on the first day of each month. (iv) Rental deposit: $43,200 payable on signing the lease agreement. This could be used to offset the rent long overdue by the tenant. (v) Lease premium: $18,000 payable on signing the lease agreement by the tenant. (vi) Rates: $3,780 per quarter payable by Yan. (vii) The lease agreement includes the terms that the building management fee and rates payable by Yan. Assume the rate is $3,780 per quarter and management fee $1,000 per month during the lease period. (viii) Yan paid $4,780 by himself for refurbishment of the kitchen with the permission of the owner. The cost was borne by Yan. The owner has a mortgage loan with a commercial bank to finance the acquisition of the North Point property. During the years 2022/23 and 2023/24, he has paid interest amounting to $36,000 and $24,000 respectively. Required: A) Compute Dickson Lee’s property tax liability for the year of assessment 2022/23 and 2023/24. B) Assume Lee is a HK resident, single and has no dependent and no other chargeable income. Assume Lee elects personal assessment, compute his tax charges for each of the years 2022/23 and 2023/24.
IU000146 Computational Inequalities Unit introduction Building on the Feminist Coding Practices unit, you will explore computational bias in the context of surveillance capitalism, big data and artificial intelligence. Through supervised studio/lab practice, technical workshops, seminars and independent study , you will learn critical and computational approaches to address forms of exploitation, discrimination and bias that are reinforced by machine learning systems and the data they are trained on. You will explore alternative, crowdsourced and open forms of data and their potential in creative ethical technology development. You will develop a technical prototype and provide accompanying reflective documentation. Please read the Learning Materials that accompany this document. This may include project briefs, unit guidelines, glossary, additional reading lists or event and presentation information. This information will be published together on Moodle. Learning outcomes and assessment criteria On completion of this unit you will be able to: LO1 Analyse and evaluate diverse, complex practices, concepts and theories around computational inequality (Enquiry) LO2 Define, create and implement your own dataset (Process) LO3 Create and critically evaluate the implications of a prototype that addresses a computational inequality (Process) LO4 Communicate about the intentions, contexts, sources and arguments surrounding your prototype (Communication) Assessment Criteria Your work in this unit will be marked against the UAL assessment criteria, which are designed to give you clear feedback on your achievement. The full assessment criteria descriptions can be found on the UAL Assessmen twebpage. What you have to produce You will submit a portfolio project as directed by the unit brief. This unit is assessed holistically (100% of the unit). There is no breakdown of grades for the different assessment evidence listed. Instead, the evidence is considered together and the tutors use their academic judgment to arrive at a grade for the unit as a whole. All components of the assessment must be submitted to pass the unit.
FINA2386 Network analysis 1. Network of Interest, Hypotheses, and Project Design This study examines the actor-director collaboration network within Hong Kong action films from 2019 to 2024, using data manually collected from Douban’s international version. Our goal is to explore how collaboration patterns influence actor careers and illuminate the dynamics within Hong Kong's film industry. Relevance of Network Analysis: Hong Kong's film industry, often described as the "entertainment circle," is deeply rooted in East Asian "guanxi (connection)" culture, where social ties, loyalty, and mentor-protégé relationships heavily influence career trajectories. Prominent groups like Jackie Chan’s "Sing Ga Ban" exemplify these tight-knit networks, built on repeated collaborations and strong bonds. Using social network analysis (SNA), we can quantify and reveal key relationships and community structures driving success and continuity within this unique industry network. Motivation: Our motivation stems from Hong Kong's action cinema's reliance on deep social and professional connections, which shape both artistic output and career development. By studying these networks, we aim to uncover the key figures and collaborative dynamics that define success and influence in this cultural and professional space. Hypotheses: ● H1: Directors with high degree centrality (those who collaborate with a large number of actors) play a key role in linking different actor groups within the industry. ● H2: Actors frequently working with influential directors tend to secure more roles and gain greater industry prominence. ● H3: Groups of actors often working with the same directors form. identifiable communities, reflecting consistent film styles or recurring casting patterns. Project Design: We constructed a two-mode network where nodes represent actors and directors, and edges denote their collaborations in films. We then analyzed the projected actor-actor network to examine indirect collaborations (actors linked through shared directors). Key metrics such as centrality and community detection methods were used to identify collaboration patterns and key figures within the network. 2. Data Collection, Cleaning, and Analysis Process Data Collection: Data was manually collected from Douban’s international version, focusing on Hong Kong action films released between 2019 and 2024. The collected data included: ● Movie Titles: Film names. ● Release Years: Year of release. ● Genre Verification: Ensured the films fit the action genre. ● Cast and Directors: Key actors and directors involved. Data Collection Strategy: ● Selection Criteria: Films were selected based on their classification as Hong Kong action films. ● Manual Entry: Data was meticulously recorded in structured tables for consistent analysis, ensuring accuracy through manual verification. Data Cleaning: 1. Duplicate Removal: Removed duplicates and standardized movie titles. 2. Missing Data Handling: Supplemented missing information with additional searches or excluded entries where necessary. 3. Verification: Cross-checked entries using Douban’s detailed information to ensure data accuracy and consistent genre classification. Visualization——Network Graphs: We used visualization tools to create network graphs illustrating the actor-director network and the projected actor-actor network. These graphs provided a visual representation of the collaboration dynamics, highlighting key nodes (actors and directors) and the density of their connections. Network Construction: 1. Two-Mode Network Construction: In order to capture the direct partnership between actors and directors and the structural characteristics of the film industry, we chose the two-mode network. We constructed a bipartite network where nodes represented both actors and directors, and edges indicated their collaborations in Hong Kong action films. Each edge connecting an actor to a director represented their joint involvement in a particular film. This two-mode network served as the foundation for our analysis. 2. Projection to Actor-Actor Network: The two-mode network is projected to a one-mode actor-actor network, where nodes represent actors and edges connect actors who have worked with the same director. The weights of the edges represent the number of collaborative movies between the two actors, reflecting the frequency of collaboration between the two actors, and through the performance of the actor centrality one can understand the actor's status in the Hong Kong action movie and television industry. 3. Centrality Metrics: ○ Degree Centrality: Identified the most connected actors and directors based on collaboration frequency. ○ Betweenness Centrality: Highlighted actors who served as bridges between different groups. ○ Eigenvector Centrality: Measured influence by connecting to other prominent actors. 4. Community Detection: Used algorithms to identify clusters of frequently collaborating actors, reflecting potential preferences or industry trends. 3. Results, Conclusions, and Potential Problems with the Analysis Results: ● H1 Validation: Certain directors displayed high degree centrality, connecting various actor groups and serving as key industry hubs. ● H2 Support: Actors with high centrality, often working with influential directors, demonstrated greater industry presence and opportunities. ● Community Structures: Community detection revealed clusters of actors consistently appearing together under certain directors, highlighting potential patterns in casting and collaboration preferences. Conclusions: Our analysis of Hong Kong action films from 2019 to 2024, using data from Douban’s international version, demonstrated how directors shape collaboration dynamics and influence actor trajectories. Directors often serve as pivotal figures, while actors' prominence correlates with their network connections through these collaborations. Potential Problems: 1. Manual Data Collection Limitations: Manual collection posed a risk of human error and incomplete data. Future work could involve automated scraping (if allowed). 2. Data Completeness: The Douban dataset might not cover all relevant Hong Kong action films, creating potential gaps. 3. Genre Classification: Genre boundaries can be fluid, which may lead to inclusion of films with mixed genres. 4. Role Significance: All collaborations were treated equally, though different roles (lead vs. supporting) may carry varying levels of influence. 4. Contributions of Each Group Member ● [Member A]: Responsible for collecting data from Douban’s international version and verifying data accuracy. ● [Member B]: Focused on network construction, projections, and centrality calculations. ● [Member C]: Conducted hypothesis testing, results interpretation, and created visualizations. ● [Member D]: Handled report writing, ensuring a clear structure, and contributed to identifying challenges and future directions. Expanded Analysis Process Network Construction: 1. Two-Mode Network Creation: We constructed a bipartite network where nodes represented both actors and directors, and edges indicated their collaborations in Hong Kong action films. Each edge connecting an actor to a director represented their joint involvement in a particular film. This two-mode network served as the foundation for our analysis. 2. Projection to Actor-Actor Network: The two-mode network was projected into a one-mode actor-actor network, where nodes represented actors, and edges connected actors who had worked with the same director. The edge weight denoted the number of films in which two actors collaborated under the same director, providing insight into the strength of these collaborative ties. Centrality Metrics: 1. Degree Centrality: This metric was used to identify the most well-connected actors and directors in the network. High degree centrality for a director indicated their collaboration with numerous actors, suggesting their pivotal role in connecting different groups within the industry. For actors, high degree centrality reflected frequent collaborations, potentially increasing their visibility and opportunities. 2. Betweenness Centrality: We computed betweenness centrality to identify actors who acted as bridges between different clusters of actors through their shared work with various directors. High betweenness indicated that these actors might play a crucial role in connecting disparate groups within the network, suggesting greater influence or flexibility in their career trajectories. 3. Eigenvector Centrality: This metric evaluated an actor's influence based on their connections to other highly central actors. An actor with high eigenvector centrality was not only well-connected but was also connected to other influential figures in the industry, potentially enhancing their reputation and prominence. Community Detection: Using modularity-based community detection algorithms, we identified clusters of actors who frequently collaborated under the direction of the same or similar directors. These communities provided insight into recurring collaborative patterns and suggested how certain directors tended to work with specific groups of actors, possibly due to stylistic preferences, casting choices, or industry practices. 1. Findings from the Analysis: 1. Influential Directors: Certain directors demonstrated high degree centrality, indicating their central role in bringing together diverse groups of actors. These directors often acted as hubs, facilitating collaborations and influencing actor communities. 2. Prominent Actors: Actors who frequently collaborated with these influential directors showed high network centrality, supporting our hypothesis that strong director relationships correlate with greater career opportunities and visibility. 3. Community Patterns: The detected communities revealed that some actor groups were repeatedly cast by the same directors, suggesting the existence of preferred collaborations that may shape genre-specific subgroups within the Hong Kong film industry. Challenges and Limitations in the Analysis: 1. Edge Weights and Role Significance: In our network, all collaborations were treated equally, which may overlook the varying significance of roles (e.g., lead roles vs. minor roles). Future analyses could introduce weighted edges to better capture these distinctions. 2. Incomplete Data: The accuracy of our findings depended heavily on IMDb data. Incomplete or missing data, particularly for less well-known films, may have affected the comprehensiveness of our analysis. 3. Genre Classification Issues: Some films listed as "action" may have blended genres, potentially introducing noise into the dataset. Future studies could involve more granular genre classifications to ensure a focused analysis.
Module title PSYC2510/PSYC3410: Advanced Developmental Psychology Module level Level 2 for PSYC2510 Students, Level 3 for PSYC3410 Students Assignment titl Choose one of the following options. Each asks you to prepare a short summary of the best evidence (an Evidence Brief) in relation to a topic linked to a lecture (or set of lectures) within the module. Option 1: Produce an executive summary of the best evidence on classroom play interventions for children in order to advise Reception teachers on how to support their pupils’ social development. Option 2: Produce an executive summary of the best evidence on classroom-based interventions in order to advise Primary Schools on the best approaches to promoting motor skill development in typically developing children. Option 3: Produce an executive summary of the best evidence on the role of exercise on cognitive function in older adults with Dementia in order to advise the Care Quality Commission on the best approach to the provision of exercise in care homes. Assignment type and description You are asked to write a short report for very busy professionals who want an overview of up-to-date evidence on a specific issue quickly. They are interested in the key findings from the best evidence but will also want to know if they should be cautious in acting on particular evidence just yet. The report should be highly focused on the set question, providing a summary of what you think is the best evidence, written as concisely and coherently as you can. For further details, please see the Assignment Guidance section later in this document. Word limit and guidance The report should be no longer than 1000 words. We strongly advise students to get close to this limit (as producing a report well under 1000 words would be self-penalising). The word count does not include the title nor the reference list at the end of the document. As is usual, the word count does include citations within the body of your report (so choose only the best!). Weighting For PSYC2510 students this assignment is worth 70% of the module mark. For PSYC3410 students it is worth 50%. Deadline The deadline is midday on
Java Lab 4 Fall 2021 Due: Saturday September 11, 10:10 AM EDT In this lab, you will practice with simple input and output. 1. Create a project named Lab4. Download the file IfSwitch.java from Canvas and copy it to the src directory. Take a look at the code. 2. Run the program; enter the requested data. You'll be entering your height in feet and inches (you can make up the data). 3. Compute the height in all inches – there are 12 inches in a foot. Display that. 4. Prompt the user to enter their gender; for simplicity – but clearly not for correctness, sorry – tell the user to enter M for male and F for female. If the user fails to enter M or F, set the gender to F. Display it. 5. Write if statements for the following: if the person's height is less than 64 inches and they registered as female, print: you're shorter than average, but if they're 64 or more and female, print: you're taller than average. Do the same for males, where the average height is 69 inches. (CDC averages for Americans) 6. Now add code between #4 and #5 that does a quick error check: if less than 21 inches or greater than 107 inches (shortest and tallest ever, according to Guinness World Records), display an error message. Then set the height to the average for the user's gender. 7. Add a new section that does the same thing as #5, but uses a switch statement to classify according to gender (with a default case with an error message), and uses if statements inside the two gender cases to do the comparisons to the averages. 8. Prompt the user to enter an integer test score. Then compute the letter grade based on the scale 90-100 is an A, and so on down to 0-59 is an F. If the score is out of range, print an error message. Next, use a switch statement based on the letter grade to display the GPA value of the letter grade: an A is 4.0, B is 3.0, and so on. Deliverable: Add your name and Andrew id to the comment at the top of the file. Upload the file to Canvas.
UESTC4003 - Control Computer Lab Exercise 2 MATLAB for Control Engineering 1.0 Objectives The primary aim of this class is to use MATLAB to: • Create and analyse Bode Diagrams and Nyquist plots • Analyse Dynamic models 2.0 Bode Diagrams and Nyquist plots 2.1 Consider the system that has the forward transfer function: a) Draw the Bode log-magnitude and phase plots for this system. b) Also obtain the Nyquist plot for the same system. c) Find the real-axis crossings by clicking on the appropriate points on the plotted Nyquist plot. d) Plot the Nichols chart, with grid, for the same system. 2.2 Consider unity feedback system that has the below forward transfer function: a) Draw the Bode log-magnitude and phase plots for this system. b) Obtain the Nyquist plot for the same system, find gain, and phase margins. Also, find associated frequencies for gain and phase margins. 3.0 Analysis of Dynamic Models (Exercise 2A) Consider a general second-order transfer function: wn is the natural frequency of a second-order system (the frequency of oscillation of the system without damping and ζ is the damping ratio. Without damping, the poles would be on the jω-axis, and the response would be anundamped sinusoid. Second- order response as a function of damping ratio can be shown as in Figure 1 below: Figure 1 3.1 Calculate the eigenvalues, natural frequencies and damping factors of the continuous transfer function model: 3.2 Define below MIMO transfer function model in MATLAB and find DC gain. 3.3 Now generate the Bode plot for the system defined in part 3.2. 3.4 Builds a second-order transfer function, G(s), with damping factor 0.35 and natural frequency 3.4 rad/sec. 3.5 Computes a closed-loop model as shown in Figure 2,using H(s) from part 3.1 and G(s) from part 3.4. Figure 2 3.6 Plot the closed-loop poles and zeros, with grid, of the model shown in Figure 2. Also, plot the impulse response of the system. 3.7 Plot also the response of the system when the input is given by: 4.0 Analysis of Electrical Networks (Exercise 2B) Figure 3 Consider the electrical network shown in Figure 3. Note, Laplace domain values for each inductor, resistor and capacitor are shown in the figure. 4.1 Write down the mesh equations for the network. 4.2 Using MATLAB, find all of the mesh currents in the network. 4.3 Hence obtain the transfer function, I3(s)/V(s), where I3(s) is the current through the capacitor. References Gene F. Franklin, J. Da Powell, Abbas Emami-Naeini, Feedback Control of Dynamic Systems, 7th Edition. Katsuhiko Ogata, Modern Control Engineering, 5th Edition. Norman S. Nise, Control Systems Engineering, 7th Edition.
BMAN31911 Innovation and Markets ASSESSMENT: CASE STUDY REPORTS 2024-2025 Course Assessment This course is assessed by individual coursework in the form. of a report of up to 3,000 words (+10% - i.e., an absolute maximum of 3,300 words excluding references, etc.). This document explains the requirements for the report. You will also be required to develop the outline which will then be formatively assessed. The deadline for submission of the reports will be on Friday, 12 December @ 2.00pm. The report must be submitted to Blackboard via Turnitin. Penalties will be applied for late submission (see the end of this document for details). Penalties for later or non-submission of the outline report will be applied to your mark for the final report. Topic Selection and Matching Theories with Evidence This course aims to develop your understanding of how innovations, markets and competition are inter-related. Grounded in Schumpeterian, evolutionary, and institutional economics it examines the ways in which technologies and their associated products, markets, and industries evolve through processes involving entrepreneurship, innovation, competition, imitation, and diffusion. Through your coursework, in the form. of a case study report, you are required to discuss and analyse this interplay. The potential scope for choosing a case and for applying theoretical concepts or ‘lenses’ is wide. You should not to draw on all of the concepts covered by the course. Instead, you should focus on one or (at most) two areas - as defined by the lecture topics - and discuss and analyse an empirical case in relation to that / those areas. A strong report requires: 1. careful selection of the case and an assessment of the evidence available to empirically discuss and analyse the case. 2. the careful matching of one or more theories to the case. One theory is normally sufficient. 3. focusing on analysis rather than description. Outlining who did what, and how, is description. Asking ‘why X acted as it did’ brings analytical thinking – which is what you want to bring to the report. You might also argue X should have done Y, but in doing so you need to explain why. With respect to theories, you are required to draw upon one or more of the theories or conceptual frameworks discussed IN THIS COURSE. Reports which do not draw on theories or concepts from this course – even if utilising theories related to the development and diffusion of innovations and technologies – will receive a failing mark. Outline You are required to prepare an outline of your proposed case study. This will be reviewed but not marked (i.e., formatively assessed), with feedback advising you whether or not you are “on track” and whether and how your report might be improved by a better matching of theory and empirics. We will offer drop-in sessions in the I where you have the opportunity to discuss your outline with a member of the teaching team. It is your responsibility to attend the session and to bring your outline. Your outline should be no more than one page (i.e. one side) of A4 (minimum font size in Arial = 12 (i.e. this size), with minimum 1.15 line spacing (as in this document). Your outline should include the following: 1. Topic area (e.g., entrepreneurship, appropriation strategies, diffusion of innovations, incumbent reactions to the introduction of innovations, etc.) 2. Your research question 3. The theory (or conceptual framework) that you intend to apply (including up to 3 key references you intend to rely on) 4. The empirical case you intend to address – including identifying the main sources of empirical evidence that you intend to draw upon. 1. Topic and case selection The “topics” are those covered in the lectures. Each lecture may cover more than one “topic”. Your task is to match one of these topics to an empirical case to analyse. You are advised very strongly to select a narrow topic-case combination, rather than a broad combination. For example, studying the diffusion of cloud computing across industries is far too broad. Studying it in a particular sector (e.g., construction) is narrower and should be much more insightful. Alternatively, you might as “Why has [Company X] become dominant in cloud computing?”, or ask “Why did [Company Y which was well placed] fail to become a major player in cloud computing?” Unusual and surprising cases are very often better than high profile or obvious cases. For example, students often want to study Apple or Tesla, and those that do often provide banal accounts such as that Apple and Tesla make great products that people want to buy. This is not very insightful. It also suffers from retrospective assessment with a “the team won because they played well” kind of logic. If you are interested in electric vehicles, more interesting is why rivals such as VW or Toyota were slow to respond to Tesla. You must ensure your topic-case combination is original. Do not use those already covered in the lectures and seminars, and of course you must not pass off as your own work studies you find on the internet. The university’s plagiarism software is remarkably good. Don’t take the risk. 2. Research question selection You must then decide upon a ‘research question’ with reference to the topic and case that you select. As indicated above, we strongly advise you to ask “Why” questions. Why questions encourage you to think in an analytical way, rather than just describing what happened. For example, “why were incumbent automakers slow to respond to the opportunity of electric vehicles?” You might want to add the ancillary question “what has been the consequence of this tardiness for competition in the auto-industry?” One way to reduce the scope of your study – which we encourage – is to give it an explicit geographical focus – i.e., confine the analysis to one country or region. Comparative analyses are possible, e.g. “Why was using mobile phone credits as money successful in Kenya but not in South Africa?“ If you are going to do a comparative analysis, then have reason for including the two countries in the comparison – don’t just pick them at random. If you are in any doubt, you are encouraged to discuss your topic-case-question with your seminar leader or member of the teaching teams at the drop-in sessions where you will receive feedback on your report outline/proposal. 3. Empirical Evidence Base A strong or appropriate evidence base is very important, even critical, to a strong report. You might have a really good question that you want to ask, but you might not find sufficient evidence to examine the question. In such a case it is probably better to abandon that question and ask another. So please take some time before you develop your outline report to look into the sources of evidence that you can or might use to undertake the analysis and write your report. There are lots of sources of evidence, including from national and international statistical agencies, from company record, from the internet, etc. Take care that your source(s) is/are credible. If the evidence base is not credible this will result in a weak report. Again, if in doubt please raise your concerns with your seminar leader. 4. The Case Study Report You are expected to thoroughly research your topic. The perspective you choose should be appropriate to the case, but can focus on a specific company, a (small) set of companies, users, or discuss diffusion more generally. You must adopt a critical and analytical stance – this does not mean being negative, it means being independent and objective. If you rely entirely on a company’s promotional material as your sources, you are unlikely to develop an unbiased view of their activities and the reasons for their success. It is vital that you use theories and concepts – FROM THIS COURSE – to interpret the case. This does not mean simply fitting the case to the theory. You should ask yourself whether the theory is appropriate to the case. Some studies, which can be exceptionally good, question why a theory or concept does not apply. For example, why didn’t a set of incumbents suffer from the incumbents’ curse in a particular case?’ Questions like this probe the limits to an established concept or theory. The word limit is 3000 words (+/-10 percent). This limit excludes references (or bibliography). It also excludes diagrams, pictures, graphs, and tables. You are encouraged to include these if your report where they add to the content and quality of the analysis. Do not include them to bulk up your report. A good report is focused – it does not include unnecessary material. Your sources should include academic sources (books and journals). You are required to ensure that your conceptual / theoretical sources are wholly or primarily from within the areas of Schumpeterian, evolutionary, and institutional economics and innovation studies. If in doubt, ask your seminar leader. For the empirical aspects of the study, you can use authoritative internet sources. At the end of your report, you must cite all your sources using the Harvard referencing style. in your list of references. You must also cite your sources in the text of your report (without a citation the reader does not know where the information or idea comes from and can only assume it comes from you. If you make a factual observation without citing the source, then the remark may be considered an unsupported assertion). A report should explain the economic characteristics of the innovation (and if applicable its diffusion (or non-diffusion), and/or analyse the interaction between the innovation and its (intended) market (including competitor reactions), interpreting this utilising theories or concepts discussed in this course. If you have knowledge of other theories and concepts in the field of Schumpeterian, evolutionary and institutional economics or innovation studies that have not been discussed in the lecture that you would like to apply, then please first discuss this with a course coordinator to obtain their agreement that the theory is appropriate. Please email a course coordinator if you wish to discuss this. An explanation of these concepts and their relevance to discussing the topic should be included. You should not over-elaborate on the theory or concepts, however. Assume that your reader (your teachers) knows the theory; we don’t want a lesson in theory, we are interested in how you apply theory to the case. You are encouraged to focus on one or at most two topics – each of the lectures can be considered to cover a “topic”. You should not attempt to include all the concepts/theories covered in the lectures as you cannot analyse the case using all of the theories in a 3000 word report If a particular case is used during seminars, you should not choose that case. The following are examples of topics-cases and research questions that have been effectively analysed by students in recent year (i.e. obtained grades of at least 70%): • Why did Amazon achieve a dominant position in the cloud computing market? • Has Instagram profited from an imitation oriented innovation strategy? • Can E-fuels transition from a niche to a mainstream product in the UK car industry? • Why did Google Glass fail as an innovation; why might it succeed in the future? REPORT WRITING What is a Report? The report is 3000 words in length excluding references, etc. Report structure and style. • A report is always written in the third person (avoiding “I”, “we”, “you”). • A report is concise and to the point – stay on topic. Don’t provide excessive background material that is not directly relevant (if you feel this is absolutely necessary to include, put it in an appendix). • A report is divided into headed, numbered sections (these should include: an introduction – telling the reader what the work is about; theory / concepts – briefly introduce the theory or concepts you will be using and why they are applicable; analysis – discuss your case and the interplay between the theory you are using and the empirical evidence you have gathered (don’t include all the empirical evidence if it is not relevant to the focus of your analysis) – highlight unusual or unexpected findings if there are any; and a conclusion – highlighting the conclusions of the study (rather than providing an overall summary of the report). Conclusions are based on the findings of the investigation Other sections can be added if required. • Complete list of references used, including academic and other sources.
Homework #8 Spring 2024 1. Molecule 3 was prepared via reaction of molecule 1 with hydrazine and base and forms intermediate 2 during this process. Provide a complete curved arrow mechanism for the most straightforward conversion to 3 from intermediate 2. You can use H2O+ as your acid and - OH as your base. 2. Molecule 5 was prepared via molecule 4. Provide a complete curved arrow mechanism for the most straightforward conversion to 5. Use the reagents given for your mechanism. 3. For the following transformations draw the product in the box below. Draw only one stereoisomer in the box if there is more than one. On the right side check the box(es) that corresponds to the major product obtained from the reaction. 4. Provide two different pairs of starting materials needed to make the amine below via a reductive amination. 5. When the following reactions were performed, the corresponding products given were not obtained. Provide the correct organic molecules that would be isolated at the end of the reactions in the boxes below. 6. Propose a sequence of reactions that efficiently converts the given starting material(s) to the target molecule. Draw the structure of the product formed after each synthetic step. Do not write mechanisms. Note: You do not need to show stereochemistry in your synthesis
COM1005 Machines and Intelligence Weeks 10-11 Lab Brief Robotics & Layered Control Architectures Changelog Ver 1.2 Removed the requirement for the AprilTag to be centered in the FoV. Ver 1.1 Added mark breakdown, a link to code template; updated wording. Ver 1.0 Initial Release. Introduction In this lab, you will explore layered control architectures and their applications in robotics. The specific architecture you’ll implement draws inspiration from some of the well-known works in robotics and AI, such as Grey Walter’s Tortoise and Valentino Braitenberg’s Vehicles, as well as natural behaviours, like run-and-tumble and phototaxis observed in bacteria. Objectives 00001. To be introduced to and implement a layered control architecture on a simulated robot. 00002. To observe and be able to link the behaviours produced by layered control architectures to those observed in nature. 00003. To be able to reason and suggest improvements to the base layered control architecture based on observations about the agent’s behaviour. Before the lab To prepare, review the following lecture slides: · Robotics I, II and III · MiRo Lab Introduction Lab materials You will be using MiRoCODE and the associated phototaxis world to program the layered control architecture. Remember the following requirements for working with MiRoCODE: · Firefox browser · Eduroam + FortiClient VPN Architecture Specification Overview The layered control architecture is implemented on a MiRo robot within MiRoCODE. For simplicity, we’ll refer to the robot running the model as Elowen. Elowen’s behaviour in the environment can be likened to that of a bacterium such as E. coli. It seeks out nutrient clusters and exhibits run-and-tumble behaviour while also mimicking the photosynthetic ability of chloroplasts and demonstrating phototaxis. Elowen operates using a layered control architecture, where sensor data is processed across multiple layers that determine the robot's actions. These layers are interconnected and can either inhibit or stimulate each other. Generally, the lower layers are more reactive but less adaptable, while the higher layers are more flexible but require longer processing times. In this simplified model designed for MiRoCODE, the layers are processed sequentially. In reality, however, these layers would operate concurrently. Programmatically, this means there is a main control loop that polls the sensors and then processes the layers iteratively in a fixed sequence, at some frequency. Layer 1: Metabolism This is the base layer on which all other layers depend for their functioning. At each step, Elowen consumes energy and must actively seek ways to replenish it to remain operational. Elowen begins with an energy level of 100%, which represents the maximum capacity and cannot be exceeded. All energy calculations happen in this layer. When the energy level reaches 0%, the run is stopped. Energy expenditures are: · Base Metabolic Rate (BMR): A fixed energy loss of 0.01% per step, independent of any other activities. · Movement: Energy loss is directly proportional to velocity. For instance, moving at 0.2 m/s results in a 0.2% energy reduction per step. · Dim Lighting: Being in areas with low brightness increases energy expenditure. Energy replenishments are: · Boosters (AprilTags): Each booster restores 25% of Elowen’s energy, but boosters are single-use only. · Bright Lighting: Being in areas with high brightness restores energy. Elowen's energy gain or loss due to lighting depends on the brightness level. The threshold brightness is set at 0.5; levels above this threshold restore energy, while levels below it cause energy depletion. This relationship is modelled as an arctangent function, with the specific values illustrated in the accompanying plot. Layer 2: Safety Elowen operates in an environment containing cliffs and walls, which it must detect and avoid using its sonar and cliff sensors. Upon detecting a cliff or wall, Elowen should immediately stop moving. This layer overrides the ‘run’ behaviour from the layer higher up. The tumble mechanism in the yet higher layer will eventually help Elowen reorient itself to face away from the obstacle. Layer 3: Run (Phototaxis) Elowen’s primary drive is to move forward, with her velocity determined by the overall brightness of her surroundings. This relationship is modelled as a piecewise non-monotonic function, as illustrated in the plot below. Layer 4: Tumble The tumble involves a random change in the robot's facing direction, with the turn angle uniformly distributed between -180° and +180°. Elowen performs, on average, a tumble every 5 seconds (in simulator time) under normal conditions, which gives a tumble rate of λ = 0.2 (tumbles per second). However, to actually implement this layer you will need to know the tumble rate per iteration. The nominal frequency of the Periodic Control Loop Block is 10 Hz, but in reality this can be slower due to the simulation environment and code complexity. Testing reveals that with fully implemented layers the code runs at about 5 iterations per second (5 Hz). However this can be different on different machines and might require adjustment. You can use the Poisson probability distribution to calculate the probability of a tumble per iteration: assuming k = 1 (representing one tumble event) and λ = 0.04 (tumbles per iteration, assuming control loop runs at 5Hz). Given the robot’s size and shape, it is recommended to reverse slightly before initiating a tumble in front of an obstacle. Layer 5: Acquisition (AprilTags) Six AprilTags are scattered throughout the environment, each with a unique ID ranging from 1 to 6. When Elowen comes within 1 meter or less of an AprilTag, a resource acquisition mode should be triggered. In this mode, Elowen must orient herself so that the AprilTag is visible in both cameras. Once properly aligned, Elowen receives an energy boost of 25%. However, each AprilTag is a single-use resource; once it has been ‘consumed’, it no longer provides an energy boost. Task Description Use the provided MiRoCODE template to implement the specified model, then proceed to the Lab Question Sheet to complete the assignment. Submission details This is an individual assignment. Each student must submit their own completed copy of the Lab Question Sheet along with the associated .mirocode file. Use of any generative AI tools in the preparation of the solution to this work is not permitted. Submissions should be made by 3:00 PM on Thursday, 12th December. Marks and feedback will be provided approximately five weeks after the submission deadline, taking into account the holiday period. In Week 12, you will have the chance to test your implementation on the physical MiRo robots. This session is optional and will not contribute to your mark. Mark breakdown Base Model [12p] · [4p] Correct layer implementation as per specification (including math). · [2p] Robust Elowen movement. · [2p] Effective use of the robot’s expressive capabilities (cosmetic joints, LEDs, etc). · [2p] Good coding style. and code organisation (use of variables, functions, etc). · [2p] Sensible trial data Modified Model [8p] · [4p] Description of a beneficial modification · [4p] Evidence, including code. COM1005 Machines and Intelligence Ver 1.2 2024–2025
