MAST20004 Assignment 1, S1 2024 Question 1 Old McDonald had a farm. On that farm he had 6 pigs, 5 cows, 7 chickens, 5 sheep, and 1 dog. An experiment involves selecting (“uniformly at random”) one animal from the farm and observing what kind of animal it is. (a) The sample space Ω for this experiment has 5 elements. List them. (b) Give the individual probabilities for each of the sample points in Ω. (c) Find the probability of the event, M, that the selected animal is a mammal. (d) Evaluate the probability of selecting a pig or cow, given that we select a mammal. (e) Suppose instead that the experiment involves selecting two animals (uniformly at ran-dom) from the farm and moving them to a new farm. Find the probability that neither of the two animals is a mammal. Question 2 In a game show called “Squad Game”, there are n contestants labelled 1 to n, in a circle. Moving clockwise around the circle, starting with contestant 1, each contestant k who has not yet been eliminated nominates a contestant (which may be themselves) for possible elimination. Contestant k then rolls a fair 6-sided die, and if the result is a 6, then the nominated player is eliminated from the game. Play continues in this way until a fixed number < n of the players have been eliminated. (a) Suppose that we watch this game and observe the labels of the players eliminated in the order in which they were eliminated. Describe a suitable sample space for this experiment. (b) Suppose instead that we watch such a game and observe only the labels of the players eliminated. Describe a suitable sample space for this experiment. (c) Suppose instead that we watch such a game and observe only whether player 1 is elimi-nated. Describe a suitable sample space for this experiment. (d) Suppose that each player nominates a uniformly chosen player among those who have not yet been eliminated (i.e. if j < players have been eliminated then the player whose turn it currently is rolls a fair (n − j)-sided die to determine who they will nominate). Find the probability of each sample point in the three experiments (a)-(c) above. (e) Find the probability that none of the first j nominations result in an elimination. Question 3 Recall the game from question 2. Suppose that = 1, and that each contestant nominates the adjacent contestant who is anticlockwise of themselves. (a) Find the probability that contestant 1 gets a second turn. (b) Find the probability that contestant 1 is eliminated in the first n nominations. (c) Find the probability that contestant 1 is the eliminated player. (d) Which contestant is most likely to survive in this game, and why? Question 4 Recall the game from question 2. Suppose that in each round (independent of the past) con-testant 1 nominates a uniformly chosen player other than themselves, while all other players always nominate contestant 1. (a) If = 1, find the probability that contestant 1 is eliminated. (b) Now suppose that n > = 3. Find the probability that player 1 is eliminated.
CHMS 5201 & 5202: Anal Instrumentation Lab I & II Experiment 9: Crystal Structure Determination of Pharmaceutical Cocrystals and Metal Organic Frameworks (MOFs) by Single XRD Reference 1.Dejan-Kresimir Bucar, Rodger F. Henry, Xiaochun Lou, Richard W. Duerst, Leonard R. MacGillivray, and Geoff G. Z. Zhang, Crystal Growth & Design, 2009, Vol. 9,No. 4, 1932-1943 2.Hideki Hayashi, Adrien P. Côté, Hiroyasu Furukawa, Nature Materials, 2007, 6 (7), 501-506 Objectives • To practice solvothermal synthesis of pharmaceutical co-crystals and metal organic frameworks • To familiarize with single-crystal XRD instruments • To analyze single-crystal XRD data with crystallographic software Introduction X-Ray crystallographic analysis is a powerful method to provide direct information on molecular structures at atomic level. This core technique provides structural details such as molecular arrangements and bond lengths. It can be used not only as a characterization tools for synthetic chemist determining the precise chemical arrangements, but also play an important role in understanding the physical nature and the materials by studying its various weak intermolecular interactions between molecules. Crystal Engineering has grown and developed over the past 50 years, which is related to the understanding of intermolecular interactions in crystal packing and use this knowledge to design new crystalline solids with desired physical and chemical properties. It can be classified into two main areas: Study of (1) Coordination Networks and (2) Molecular Materials. Coordination Networks, commonly known as metal organic frameworks (MOF) or porous coordination network (PCN), consist of ultrahigh porosity and enormous internal surface areas ranges from 1-D to 3-D coordination polymers. By combining two components, metal ion or cluster and organic linker, the structure of the porous materials can be readily engineered in composition, structure and functionality. Their interesting properties include wide range of important applications include gas storages, separation and sensing. Molecular Materials involves the studying of polymorphs and co-crystals. Polymorphs refer to the same type of materials that can form two or more crystal packing while co-crystals refer to two or more types of molecular or ionic compound crystallize in a single crystalline phase in a particular stoichiometric ratio. They both play an important role in pharmaceutical formulation as their solubility, bioavailability, solid-state stability is greatly depending on their structures. In this experiment, the study of pharmaceutical co-crystals and metal Organic Frameworks were demonstrated by two systems via solvothermal synthesis: (1) Synthesis of Caffeine:4-hydroxybeznoic acid co-crystal and (2) Synthesis of zeolitic imidazolate frameworks (ZIF). Caffeine, a worldwide mostly used pharmaceutical model compound, was heavily studied as a psychoactive drug. Researches shows its pharmaceutical performance can be greatly improved via co-crystal phase formation with aromatic carboxylic acids (1) . The interaction always involves one of more heterosynthon hydrogen bond between acid and caffeine imidazole moiety. In this experiment, the system Caffeine:4-hydroxybeznoic acid is chosen as it can form both 1:2 and 2:1 co-crystal with change of condition. Different supermolecular heterosynthons can be examined in the structural isomers and reveal the impact of structural variation. Zeolitic imidazolate frameworks (ZIF) are known as analogues of SiO2. Various porous materials can be readily engineered for composition, structure and functionality with different reaction conditions. In this experiment, two 3-D networks, SOD topology (ZIF-8) and quartz topology, will be synthesized (2) . SOD topology (ZIF-8) consist of large cavities which is useful for solvent/gas absorption application, while the quartz analogue is sensitive to thermal changes and consist of non-linear piezoelectricity. Experimental Procedure A. Preparation of Crystalline Samples Since most part-time students are unable to prepare these, several different materials will be prepared for later analysis that all groups will use. General Solvothermal Synthesis 1. Place all the mixture into Teflon cup (Day 1) 2. Introduce corresponding solvents into the Teflon cup (Day 1) 3. Place Teflon cup in stainless steel container and seal container (Day 1) 4. Place container + Teflon cup into oven at 80/110oC for various durations (Day 1) 5. Allow to cool slowly to room temperature (Day 2) 6. Unscrew stainless steel container (Day 2) 7. Filter the mixture in the Teflon cup using suction filtration (Day 2) 8. Wash with water then ethanol (Day 2) 9. Continue filtering with suction filtration for 5-10 minutes (Day 2) 10. Weigh the crystals and calculate the yield (Day 2) 1. Pharmaceutical co-crystals Caffeine:4-hydroxybenzoic acid (Day 1) Synthesis of Caffeine:4-hydroxybenzoic acid 2:1 co-crystal (Cpd 1) Caffeine (1 mmol, Fw 194.19, 194 mg), 4-hydroxybeznoic acid (0.5 mmol, Fw 138, 75 mg) and 2 mL Acetonitrile were placed into an air-tight vial. The mixture was heated at 80oC for 2 hours. On cooling colorless block obtained by filtration. Synthesis of Caffeine:4-hydroxybenzoic acid 1:2 co-crystal (Cpd 2) Caffeine (0.5 mmol, Fw 194.19, 97 mg), 4-hydroxybeznoic acid (1 mmol, Fw 138, 138 mg) and 2 mL Acetonitrile were placed into an air-tight vial. The mixture was heated at 80 oC for 2 hours. On cooling colorless block obtained by filtration. 2. ZeoliticImidazolate Frameworks: SiO2 analogues (Day 1) Synthesis of Zeolitic imidazolate frameworks with SOD topology (Cpd 3) A mixture of Zinc acetate (0.183 g, 1 mmol), 2-methylimidazole (0.328 g, 4 mmol), potassium hydroxide (0.112 g, 2 mmol) and ethanol (2 mL) was placed into a Teflon-lined autoclave. Then, the autoclave was sealed and heated at 110 ℃ for 1 day. After being cooled to room temperature, colorless crystals were filtered by filtration. Synthesis of Zeolitic imidazolate frameworks with Quartz topology (Cpd 4) A mixture of Cadmium acetate (0.230 g, 1 mmol), 2-ethylimidazole (0.384 g, 4 mmol), potassium hydroxide (0.112 g, 2 mmol) and H2O (2 mL) was placed into a Teflon-lined autoclave. Then, the autoclave was sealed and heated at 110 ℃ for 1 day. After being cooled to room temperature, colorless crystals were filtered by filtration. Single Crystal X-ray Diffraction (Day 2) Crystals will be mounted for quick checking of the suitability of the crystal for analysis and unit cell determination maybe carried out. The choice of ideal crystal – size, habit and singularity will be discussed. Crystals maybe glued to a glass fiber for mounting on the diffractometer, or immersed in paratone liquid before freezing. Each group member will be responsible for one sample. The optical centering of the crystal will be demonstrated and the procedure for cell check carried out. Ideally for each session one crystal will be selected for a full data collection and crystal structure determination. A Crystallographic software OLEX2 (free download http://www.olexsys.org/) will be introduced step by step. We will go through how to process the x-ray data from two examples (Cpd1 and Cpd3) in details. Results 1) Use OLEX2 software to solve the data for Cpd 2, output a figure represent the crystal structure and attached them in your report. Please also submit your .res file. 2) Please comment on the relatively high R value and the large Q-peak presence in Cpd 1 3) Please suggest what will happen if we try out the reaction in a ratio of 1:1 caffeine:4-hydroxybenzoic acid instead. 4) Please use OLEX2 to output a figure represent the packing of Cpd 3 and 4. Briefly comment on their differences 5) Cpd 3 & 4 are well-known materials. Please list out one important application for each Cpd and explain how they work.
PROJECT 2: PREDICTIVE MODEL DEVELOPMENT You are to develop a classification on data set from your Project 1 using Neural Network Technique and three classification techniques (Decision tree, Neural Network Classification, Naive Bayes, and SVM (use Weka/Rapid Miner/Python) Title of Report: COMPARATIVE STUDY ON XXX CLASSIFICATION Basic Requirement. Your paper format should follow report guide (Springer Writing Format) and Your paper should NOT exceed 15 pages. The rest of details need to be in the APPENDIX Organization of your paper/report. Your paper should contain the following sections: 1. Introduction (1/2 page) Description on your dataset and business goal 2. Related Work (1-2 pages) This section should highlight the related work and any previous work that use the methods (NN.l Decision Tree, Naive Bayes and SVM) on similar data as yours. Some references are required here You MUST provide at least FIVE related work in EACH technique and preferable if the paper used the same data as you use as one of their experimental dataset. 3. Classification Methods (1-2 pages) A review on the classifications methods used. Just brief. 4. Modelling and Measurement Methods. (1-2 pages) Describe the modelling set-up (k-cross validation/leave one out/etc) and measurement metrics(accuracv. precision. recall. confusion matrix AUc. RoO used in vou proiect.l 5. Results and Discussion (3-6 pages) This section should contain the experimental results and analysis of the results. How you compare the classification methods? What measure you use for all three methods. You should present your detail result as appendixes and summarise results in main documents with several tables and graphs. Example of result table (PLEASE REFER APPENDIX TABLE OF RESULT) 5.1 Visualise Your Results How you analyse? How you reach to the best technique? Discuss the conclusion of the result describing the issue of Accuracy, Robustness, Interestingness, Error analysis-are there any patterns in the errors made by the models produced, Speed, Scalability and Interpretability. 5.2 Knowledge Analysis This Section contain the analysis of knowledge. 6. Conclusion and Suggestion (1 page) This section should contain conclusion of your work, limitation of the techniques, data or tool or any other problems. Suggestion for future improvements References You should refer to several (at least 5 data analytics research paper similar to your work, see how the researchers analyze their problems and results and see how they write research paper. HAPPY WORKING
Data science Assignment Due: 5pm EST, 2/21/2025 1. n-gram Given the training data John read a book by Jane John read another book I read a different book (a) Calculate bigrams using maximum likelihood estimates (MLE) and fill out the table. Bigram Probability Bigram Probability P(John | ) P(another | read) P(read | John) P(book | another) P(a | read) P( | book) P(book | a) P(I | ) P(by | book) P(read | I) P(Jane | by) P(different | a) P( | Jane) P(book | different) (b) Calculate the sentence probability of John read a different book using only MLE bigram. (c) Calculate the sentence probability of Jane read a book using only MLE bigram. 2. Evaluation metrics on binary classification Given the following output, Actual Label Predicted Label 0 0 1 1 0 1 0 1 1 1 0 0 1 1 0 1 1 0 0 1 (a) Draw the confusion matrix. (b) Calculate the Accuracy, Precision, Recall, and F1 score. (c) Why might using accuracy as the only metric is not ideal? 3. Evaluation metrics on multiclass classification Given the following confusion matrix of a multi-label classifier Truth A B C D E F A 95 1 13 0 1 0 B 0 1 0 0 0 0 C 10 90 0 1 0 0 D 0 0 0 34 3 7 E 0 1 2 13 26 5 F 0 0 2 14 5 10 Classifier (a) Calculate the precision, recall, and F1 for classes A-F (b) Calculate the micro-average precision, recall, and F1 (c) Calculate the macro-average precision, recall, and F1 4. Text classfication The drug review dataset provides patient reviews on drugs and a positive and negative rating reflecting overall patient satisfaction. The dataset consists of two files: drug review train .csv for training and drug review test .csv for testing. Both files contain plain-text, UTF8-encoded sample set in a tab-separated format with the following columns: • Text • Binary label (0 and 1) (a) Use BernoulliNB to build a naıve Bayes classifier(¨). BernoulliNB true positive false positive false negative precision recall F1-score positive negative (b) Repeat the process in Task (a), but use the SVM (SGDClassifier) model. SGDClassifier true positive false positive false negative precision recall F1-score positive negative (c) Upload the source codes.
BUSA8000 Assesssment 1 - COVID-19 impact on digital learning 1. Data Cleaning and Wrangling Describe and show the data cleaning, transformation and wrangling process’ that you have completed. Ensure you provide justification of any data handling and transformation you have completed. you can embed your transfomration code and provide justification like this: example: This code creates x and y variable, i have created these variable so that i can add these to my data frame. and compute the required measures. Please remember to use comments in your code. x
Project 3: Composition Proposal Presentation + Discussion · Due 8 May by 1:30 · Points 100 · Submitting a file upload · Available 4 Mar at 0:00 - 3 Jun at 23:59 Weight: 10% Due date: Week 9 during class, Wednesday, 1:30-4:30pm (AEST) Assessment format: Spoken presentation including sound excerpt. Slides and/or audiovisual aids submitted via Canvas before the end of class. Grading Criteria Rubric: Project 3 Grading Criteria Rubric PROJECT DESCRIPTION Present to the class your proposal for a creative composition, to be completed in Project 4. In a five-minute presentation, describe your intentions for a sound composition that will explore, represent, communicate or share your experiences of your chosen sonic environment investigated over Projects 1 and 2. Your composition should be based on your listening experiences, which were documented in Project 2. Your presentation must include at least a 30-second excerpt of your drafts or recordings to be included in the composition. After each presentation, there will be an open-ended discussion where you will be required to answer questions and receive feedback on your proposal from your lecturers and peers. In your presentation, include the following details: · Working title of your composition · Chosen location · A brief summary of your sonic investigation, with a focus on your key findings (in other words, what are you hoping to share or communicate with listeners?) · Progress to date, including at least a 30-second excerpt of your drafts or recordings to be included in the composition · Anticipated production timeline in the lead-up to final submission in Week 12 · At least one question: a request for guidance or feedback, or a question that is driving your work DELIVERABLES · A five-minute presentation, followed by an open-ended discussion, during class hours in Week 9 · Slides and/or audiovisual aids to be uploaded via Canvas before the end of class LEARNING OUTCOMES Upon successful completion of this assessment, you will be able to: · Critically discuss and present design ideas and findings about a soundscape using aural, oral and visual methods
Introduction to Biomaterials Engineering CBE 4421a Problem #1 1. a. Determine the probability of failure of a hip joint arthroplasty after 15 and 30 years, assuming the following (t is in years). b. Which factor is the most important for the longevity of the arthroplasty? Infection fi = 0.05e–t Loosening flo = 0.01e+0.15t Fracture ffr = 0.01e+0.01t Wear fw = 0.01e+0.1t Surgical error fsu = 0.001 Pain fpn = 0.005 2. A polyethylene is made of the following weight distribution. a. Calculate Mn, Mw, and Mw/Mn (polydispersity). b. Plot Wi versus Mi and also Mw and Mn in the same plot. c. Why is Mw always greater than Mn? Wi (grams) 10 20 30 30 20 Mi (kg/mol) 10 20 30 40 50 3. (a) Show that the atomic packing factor for HCP is 0.74. 3. (b) Titanium (Ti) has an HCP crystal structure and a density of 4.51 g/cm3. (i) What is the volume of its unit cell in cubic meters? (ii) If the c/a ratio is 1.58, compute the values of c and a.
AS.440.601 Microeconomic Theory Assignment 1 1.(5 pts) Suppose that f(x,y)=xy. Find the maximum value for f if x and y are constrained to sum to 1. Solve this problem in two ways: by substitution and by using the Lagrange multiplier method. 2.(5 pts) The dual problem to the one described in question 1 is Minimize x+y subject to xy=0.25. Solve this problem using the Lagrangian technique. Then compare the value you get for the Lagrange multiplier with the value you got in question 1. Explain the relationship between the two solutions. 3.(10 pts) Each day Paul, who is in third grade, eats lunch at school. He likes only Twinkies (t) and soda (s), and these provide him a utility of utility=U(t,s)=√ts. a. (5 pts) If Twinkies cost $0.10 each and soda costs $0.25 per cup, how should Paul spend the $1 his mother gives him to maximize his utility? b. (5 pts) If the school tries to discourage Twinkie consumption by increasing the price to $0.40, by how much will Paul’s mother have to increase his lunch allowance to provide him with the same level of utility he received in part (a)? 4.(20 pts) Two of the simplest utility functions are: 1. (10 pts) Fixed proportions: U (x, y) = min[x, y]. 2. (10 pts) Perfect substitutes: U (x, y) = x + y. For each of these utility functions, compute the following: Demand functions for x and y Indirect utility function Expenditure function 5.(30 pts) Consider the Cobb–Douglas utility function U(x, y) = x α y 1-α , where 0≤α≤1. This problem illustrates a few more attributes of that function. a. (10 pts) Calculate the indirect utility function for this Cobb–Douglas case. b. (10 pts) Calculate the expenditure function for this case. c. (10 pts) Show explicitly how the compensation required to offset the effect of an increase in the price of x is related to the size of the exponent α . 6.(30 pts). Suppose the utility function for goods x and y is given by utility = U (x, y) = xy + y. a. (10 pts) Calculate the uncompensated (Marshallian) demand functions for x and y, and describe how the demand curves for x and y are shifted by changes in I or the price of the other good. b. (10 pts) Calculate the expenditure function for x and y. c. (10 pts) Use the expenditure function calculated in part (b) to compute the compensated demand functions for goods x and y. Describe how the compensated demand curves for x and y are shifted by changes in income or by changes in the price of the other good.
CS 4293 Topics on Cybersecurity 2021/22 Semester B Environment Setup In this course, most of our hands-on assignments will be adapted from the project collections on the SEED (Developing Instructional Laboratories for Computer SEcurity EDucation) website (http://www.cis.syr.edu/~wedu/seed/index.html). You are strongly suggested to follow the guidelines to setup your own hands-on environment before doing your assignments. The environment includes the virtual machine software, e.g., VirtualBox and Linux, with which you can work on the assignments using your own personal computers. Getting familiar with them is critical. Preliminaries: Above is an illustration figure for cloud hosted virtual machines. In our labs and assignments, we also need to use a Hypervisor Software (VirtualBox) and an OS 1 image which is pre-configured. Within this OS 1 image, we can exploit the vulnerabilities without worrying affecting the hosted OS, achieving sandbox isolation. 1. Prepare the Virtual Machine Software: VirtualBox is recommended for the assignment in this course, which is open-source and completely free. We recommend Version 6.1.16 (please stay away from the newer versions, as they may still have some issues with our VM). If you are working on your own computer, it can be downloaded here. We note that other virtual machine software like VMware Player and Parallels Desktop are also compatible to use. Go to the download page shown as below and choose the appropriate installation package according to your host operating system: For instance, students who prefer to work on their MAC laptops should select VirtualBox for OS X hosts. NOTE: To make Virtualbox work on all platforms, you need to enable x86 virtualization technology. On x86-based Mac, it is enabled by default. On windows, you need to enable Intel VT-x option in the BIOS settings (similarly SVM on AMD platform). 2. Prepare the Pre-built Ubuntu Virtual Hard Disk File (Optional): To use the pre-built Ubuntu image, we basically need to create a guest OS image (Ubuntu 64 bit) that is stored on the hosted computer and load the pre-built virtual hard disk file. In CS Lab Room 2450, the pre-built Ubuntu virtual hard disk files are already prepared. You can find them in the directory “C:VirtualBoxVMCS4293SEEDUbuntu-20.04-64bit”. The virtual hard disk files are ended with the extension “.vdi”. You can also find 32-bit version but you do not need them in this course. If you are working on your own computer, a pre-built Ubuntu 16.04 virtual machine image (SEEDUbuntu 20.04.zip) can be downloaded from Google Drive (4 GB). 3. Install Pre-built Ubuntu 20.04 VM image on VirtualBox: A detailed document from SEED regarding the configuration of the pre-built VM is available at the SEED site (Manual). It gives a more detailed overview of all the installation procedures. Note that some of them might not be necessary for this course (e.g., Step 5.d: Network). Open VirtualBox: 1. Find the shortcut to VirtualBox using CS Lab Menu (if you are in CS Lab Room 2450) or Install VirtualBox Manager using the downloaded installer (if you are working on your own computer). 2. Launch VirtualBox. 3. Launch the Ubuntu image from VirtualBox by clicking “New”: You should see a screen similar to the following: Our prebuilt Ubuntu 20.04 VM is 64-bit, so pick Ubuntu (64-bit). Name the VM, select the type as Linux and then continue: We need to allocate dedicated memory for the VM. 1024 MB should be sufficient, but we recommend 2GB. If your computer has more RAM, you can increase accordingly. The more memory you give to the VM, the better the performance you will get. Click the folder image. On the popup window, use the Add button to select the .vdi file provided in C:VirtualBoxVMCS4293... Note: If you get an error message saying that the UUID already exists, this is because the UUID in the selected vdi file is the same as the one used by an existing VM. You can either remove the other VM or change the UUID in the vdi file. After the previous step, your VM will be created, and you will see it on VirtualBox's VM panel. We need to do some further configuration. Click the Settings option, and we will see the Settings window. Go to the General category, and select the Advanced tab. Select Bidirectional for both items. The first item allows users to copy and paste between the VM and the host computer The second item allows users to transfer files between the VM and the host computer using Drag'n Drop (this feature is not always reliable). The copy-and-paste feature is very useful. If you can't do copy and paste, chances are that you forgot to do this step. You can always do it later by selecting the Devices menu item, and you will see the Shared Clipboard submenu. Note: For some reason, on particular platforms like Windows, some bugs do exist such that you cannot successfully enabled bidirectional support. But you can still use the network within the guest SEEDUbuntu image, e.g., download materials from canvas and transfer files to the host through Whatsapp. Go to the System category, and select the Processor tab. Assign number of CPUs to this VM if you prefer. Although may be sufficient, if the performance seems to be an issue, increase the number. Go to the Display category, and select the Screen tab. If the display does not seem to work properly, try to increase the amount of video memory. In our testing, 28 MB seems to be sufficient. Note 1: Make sure to select VMSVGA, as choosing other graphic controllers may lead to the crash of the VM. Note 2: If your computer's screen resolution is too high, the VM may not be able to match the high resolution. As results, your VM will be very small on your screen. To make it bigger, adjust the Scale Factor in this setting. We can now start the VM. You can also use the Take button to take a snapshot of your VM. This way, if something goes wrong, you can roll back the state of your VM using the saved snapshots. There are many ways to stop the VM. The best way is to use the Save State. This is different from shutting down the VM. It saves the current VM state, so next time when you restart the VM, the state will be recovered. Moreover, the speed is also faster than booting up a VM. Sometimes, we need to copy files between the host machine and the VM. Step A. First you need to create a folder on your local computer (or using an existing folder). We will let the VirtualBox know that this folder should be shared with the VM. Go to the following menus: Once you see a Add Share popup window, select the folder that you want to share, click OK, and you will see that the folder is now made available for sharing. Step B. Inside the VM, we need to mount the shared folder somewhere. Let's mount it to the home directory as a folder Share. We will create a folder called Share in the home directory, and then mount the shared folder VM_Shared to this Share folder using the following command. After that, you can access the shared folder from ~/Share. $ mkdir -p ~/Share $ sudo mount -t vboxsf VM_Shared ~/Share Important Note. Please only use the shared folder to copy files between the VM and the host machine, and never use it as your working folder!!! Working from the shared folder has caused many problems, especially on the permissions of the files created inside the shared folder. For example, if we unzip the Labsetup.zip file inside the shared folder, the permissions of the unzipped files will be different from those on the original files. Some labs and containers are very sensitive to those permissions.
Construction Specialisation Assessment task one: class activities Introduction date Every session Due date The task will due before the next session. However, it is recommended to submit the completed task by the end of the class. Assessment weighting (5*5%=25%) Five marks for each activity. There will be ten class activities for ten sessions and they totally make up 25% of the final mark. Aim The aim of these activities is to gain a deeper understanding of the topic. Objectives Upon successful completion of these tasks you will be able to apply a specific management technique or evaluate a management approach in construction. Brief Throughout the semester, you will be asked to undertake activities during the classes. The lecturer will be available to assist you in doing so. You will also have the opportunity to work within your peer group and discuss the solutions. The activities are related to the topics covered in the course. These activities are part of your learning process. Therefore, it is strongly recommended that you go through the process during the lectures with your peers and submit your assessment task by the end of the class. Electronic submission You need to submit your task through Canvas. Paper submission is not acceptable. ASSESSMENT CRITERIA (For each exercise) Criteria Marks You have shown good understanding on the session topic and provided the correct calculation, assessment or reflection (depending on the session exercise) 4 The report is formatted according to the requirements. 1
‘Tech for Good’ MA DCS 7AAVDC29 practical digital research and design project 1. GENERAL INTRODUCTION Your primary seminar task between weeks 3-8 of Semester 2’s core DCS module (7AAVDC29) is devoted to group work (4-5 students per group). During the seminars and in homework sessions you will be researching and developing a concept and business idea for an app or other digital product that is beneficial for society or culture. You should imagine that the app is being developed for a start-up, a government service, a cultural institution, an NGO/charity or an existing company. In Week 8 your group will present your app concept in a short pitch (with slides/mock up prototype) to an investor (your billionaire seminar tutor). This exercise is intended to help you develop your research skills, business thinking and presentation skills for professional life. The presentation of your group’s project is not part of your assessed grade for the module. However, this practical project is linked to a summative assignment that you will submit in week 9 of the module and which counts for 20% of the overall grade. This is described in section 7 below and in more detail in the Assessments section on KEATs. 2. TIMELINE: Week 3: At the end of this seminar, the seminar leader introduces the project exercise, answers questions, and assigns groups. Week 4: You have group homework. Discuss different ideas for a project and come up with 2-3 possible concepts, which one person will email to your seminar tutor. The seminar leader will review your ideas and suggest the strongest one for your group to start developing. Week 5 seminars (double seminar – 2h). You will discuss the lecture and core readings as normal, and afterwards will work on your project concept with your group members. Your group will already know which idea you will begin researching and developing (based on oral/email feedback you will receive from your tutor). You should conduct practical research together during the seminar. After this, you can assign specific roles for responsibility (see page 5 about roles). At the end of this longer seminar, each group will briefly introduce their research design. Week 6: (Reading Week): Time to execute your research design! Take your own observations using the app, link them to reflections from academic literature, study facts and figures about the area of your app. We would encourage you to plan interviews with potential users and/or a survey. Also study the market for which your app will be developed, and look at competitors, and investigate the existing technologies that will make your app work. Groups should meet during reading week to develop their research together and to discuss findings. Week 7 seminar and homework: Most of this seminar will be dedicated to the finalisation of your presentation, prototypes and mock-ups. Your tutor will check on your progress in the seminar, answer any questions you have, and give feedback on your ideas. Week 8 seminar - 2 hours long. This gives us time to discuss the lecture and core readings as normal, followed by the group project presentations - each group will have 8 minutes (maximum!) to present their app. Each group will also have 5 minutes to answer questions from the potential ‘investor’ and the audience. Top projects (date TBC): The groups with the top scoring projects in each seminar will have the opportunity to present their concepts again in a whole class showcase event that will be scheduled after the Spring break. This will be our final whole class event and groups will get feedback from the module conveners and other teaching staff. An overall class project winner will also be announced at this session. 3. DETAILS OF YOUR TASK: Your team’s task is to research and design the concept of your app through undertaking the following steps/activities: - Explore the issue or problem that your product is trying to address through online research: What information/statistics are available, and can you find (current discussions/debates) that show the potential value of your product/service? - Identification of real and currently existing digital technology that your app can be based on. What ideas can you get from existing apps or platforms? - Research on the target group: Who is likely to use your product? Is it one target group only or two or three? How big are the target groups, are they big enough to make a viable market? - Research on the market situation: Is there a gap in the market for your product/service? What is your target group using now? Who are your main competitors? What is your comparative advantage or unique ‘selling point’? - Development of a business plan: how will your service/product be financially viable and sustainable? Will it be subscription based? Or advertising based? Will it be funded by a non-profit organisation? Or is it linked to a transaction or shop? How will you keep this thing going? - Imagine undertaking the walk-through method for two/three real or potential users from your target group: thinking through potential user experiences of different types of (typical) users from your target group. - Develop an understanding of the ethical challenges the technology could face including worst case scenarios = #epicfail; - Develop the design – a logo and a mock-up of a prototype showing the most important screen (landing screen/home screen, for example) and features. - Going public: outline the marketing strategy you will use to introduce the new digital service/product to your target group. How do you reach your users? - Think about the academic literature that we have been reading through the core module, both this semester and last semester. What insights/needs/concerns have informed your approach to app design? (You will not need to talk about the academic literature in your group presentation, but you will need to write about this in the assignment that is linked to project). 4. THE PRESENTATION (PITCHED FOR A POTENTIAL INVESTOR): Max 8 minutes. lively and informative in tone, creative, making good use of visual elements, and delivered to time. Tip: The slides should not be the notes that you are reading out loud. Avoid too much text on a slide – images are more engaging! You want people to listen to you, not to read the slides, which should contain only the most important information. You presentation should: A) Start with delivering a very brief idea of the digital product and service, what it does and why it is beneficial for society. B) Show the research methods you conducted to understand the target group and product. This includes statistics, interviews, observations, survey data. C) Research competitors as well as the target group. D) Explain real and existing technologies that will make your product/service work E) Mock-up of the prototype or design F) Walkthrough description of at least one different use case (potential user experience) from your target group F) Identify the biggest ethical risk, give an example and discuss how you would reduce/remove that risk G) Explain briefly the planned communication and marketing campaign The presentations will be judged by the seminar tutor (and other groups). Projects will be judged on the following criteria: Best researched Most innovative or imaginative Most beneficial for society Most realistic and financially viable Best marketing campaign This judging is not related to the assessed component of the project, which has different criteria (see the assessments section on KEATs). The purpose of the seminar judging is to identify one ‘winning’ group per seminar. Each of those groups will then be showcased in front of the whole course in a separate project finalists workshop that we will hold after the Spring Break as a final course event. Again, this finalists workshop is not related to the separate project assessment. 5. TEAMWORK AND INDIVIDUAL ROLES In the seminar presentations you will be judged as a team, and you should develop your project together. Although each member will have different roles, we expect everyone to contribute to the research on the target user group, market and technology. For the creation of the presentation, each team member can take on a dedicated role to research a specific aspect of your concept. These are some roles you may wish to have assigned to individual group members (you could adapt or combine some of these depending on the size of your group): Chief Business Officer prepares the presentation of the business plan, which could focus on subscriptions, advertising, receiving government support or funding, or by developing a service a bigger company needs. Chief User Research Officer prepares the presentation of the research into the target groups, usage of the product, and statistics and numbers; prepares the imagined walkthrough method on two different users typical for target groups (fictional or real persons). Chief Design Officer prepares the mock-up of the prototype app. Chief Technology & Ethics Officer prepares the presentation of the key technologies or data sets that your product will be based on, explains how the technology functions and looks into the ethical issues relevant for that technology. Develops a plan for ethical standards important for the product. Chief Marketing Researcher presents communication strategy and marketing campaign knowledge gained. 6. THE SKILLS YOU WILL DEVELOP This practical research/design project will encourage you to apply your understanding of issues and theories in the field of digital culture (from the course readings, lectures etc.) to a real world challenge. This process will help you develop the following cognitive skills essential for working in different digital environments and sectors: A) Analysis - Challenging, interpreting and exploring different views and perspectives - Crossing theoretical and practical boundaries - Working with incomplete pictures - Thinking creatively B) Problem solving - Identifying problems and developing hypotheses - Breaking down structures and identifying components - Testing problems and applying processes - Understanding context and applying solutions - Making decisions based on different factors - Managing complexity and ambiguity C) Planning, team organisation & communication - Planning and identifying sources of information and data - Reviewing, synthesising and integrating information and data - Team organisation and roles - Involving others and including others' contributions - Visualization and presentation 7. ASSESSMENT LINKED TO THE RESEARCH PROJECT In week 9, each student will submit one document that includes an individual 500 word reflection on their contribution to the group project, and a link to a Sharepoint upload of their group’s presentation slides. This will be worth 20% of the grade for this module. Please see the assignment document in the Assessments section of KEATs for details of what this should include and the specific criteria that will be used for marking.
Project 4: Composition Presentation + Supporting Documentation · Due 29 May by 1:30 · Points 100 · Submitting a file upload · Available 4 Mar at 0:00 - 3 Jun at 23:59 Weight: 30% Deliverables - formats & submission dates: · Canvas discussion post (Week 12, Tuesday, 8:00pm) · Composition presentation (Week 12 during class, Wednesday 1:30-4:30pm) · Digital sound file, image file and PDF upload (Week 12, Friday, 8:00pm) Grading Criteria: Project 4 Grading Criteria Rubric ASSESSMENT DESCRIPTION In this final project, you will draw on the work you have produced across the course, through projects 1, 2 and 3, and develop and produce a creative composition about your chosen sonic environment. This project has three deliverables: 1. Creative composition 2. Supporting documentation, including digital sound and image files 3. Canvas discussion post and in-class presentation Deliverable 1: Composition Develop and produce between 3 and 10 minutes of creative composition that communicates your findings or expresses your listening experiences in your chosen sonic environment, based on your findings in Project 2. You must record audio material to use in your composition. These recordings must be original, and must be recorded in your chosen location or represent your interpretation, memory or listening experience of your chosen location. You may use a combination of personal recordings and other audio resources to develop your composition. Your composition must include a minimum of three audio recordings that you have personally captured. Any media sourced externally must be sourced from the RMIT library and credited appropriately. RMIT Pro Sound Effects library: https://library-prosoundeffects-com.ezproxy.lib.rmit.edu.au/#!explorerLinks to an external site. If you are unsure if your chosen sound sources meet the assessment requirements, you must discuss with your tutors before submission. Your full composition must be rendered/exported as a stereo .wav or high-quality .mp3 file and accompanied by supporting documentation (details below). Students who present multichannel/ambisonic compositions in the SIAL Sound Studios in their final presentations in Week 12 will still need to supply a stereo mix of their work. Deliverable 2: Supporting Documentation You must compile your stereo .wav or .mp3 audio file of your composition, a .png image file of your graphic notation, and a PDF of approximately 2,000 words of supporting documentation to be uploaded as a compressed .zip folder via Canvas. Your supporting documentation must include the following: 1. Composition Title Title of your creative composition. 2. Composition Aim/Intention · · Summarise the intention/aim of your composition -- what findings you aim to communicate or listening experience you intend to express through your creative work. · [indicative word count: 200 words] 3. Intended Listener Experience · · Describe in detail the intended listener experience of your composition. What sounds do you intend the listener to focus on? Do you expect the listener to recognise the environment? What emotions do you want the listener to feel? Are there particular thoughts/memories/ideas that particular sounds or events are intended to evoke? · Your description must consider and discuss how a listener's experience of your composition may vary from your own, depending on their own experiences setting and knowledge, to demonstrate your deep understanding of the complexities and subjectivity of sound. · [indicative word count: 200 words] 4. Summary of Media · · Provide a detailed and descriptive list of each sound used in your soundscape composition. · For personally recorded audio materials, detail the location it was captured in and how the recording/sound was used in the composition. · For all other media, reference the source of each material used and provide at least a one-sentence justification for its use. 5. Graphic notation · · A creative graphic notation of your soundscape composition, which may take the form. of a notated timeline, or a spatial graphic map. · The graphic notation must a well-developed iconography (set of icons and symbols) to represent sounds in your composition and their qualities (e.g. sound envelope, duration, sound loudness, movement, fluctuation, feeling). · The notation must be accompanied by a legend that defines what each icon/symbol represents. · Graphic notation must be provided as a .png file of at least 1920px width/1080px height 6. Production Summary · · Provide a detailed and descriptive account of the production of your composition. · You don't need to account for all of your production here; instead, highlight a few significant examples of both fieldwork and production. · What are the moments or instances of fieldwork that informed your creative approach? · What are the key moments or iterations of your editing, drafting and listening that informed or changed your approach to the composition? · [indicative word count: 800 words] 7. Reflection Reflect on your project and respond to the following questions: 00001. 1. How did you arrive at the composition aim/intention that you wanted to share, express, communicate or represent about the sonic environment? Make reference to projects 1, 2 and 3. 2. Why is your composition aim/intention valuable to you and/or other listeners? What factors, experiences and/or events led you to your composition aim/intention? 3. What did you learn about the place during the development of your composition? Make reference to your field work where relevant. 4. How has your relationship to this place changed across your listenings and investigations? Make reference to your place-based work across projects 1 and 2. 5. How successfully do you feel you have expressed your composition aims and intentions? 6. How similar or different is your final work to your composition proposal (Project 3)? Why? · · [indicative word count: 800 words] Deliverable 3: Presentation & Canvas Discussion Post You must choose up to five minutes excerpted from your composition to be presented during class in Week 12. Before your presentation, you must create a discussion post on Canvas titled Student Name - Composition Title. In the Canvas post, include: · Embedded sound file of the composition excerpt to be presented · 100-word explanatory statement LEARNING OUTCOMES Upon successful completion of this assessment, you will be able to: · Apply your listening skills for identifying and analysing the principal components of a soundscape · Critically assess a soundscape in relation to listener needs · Critically discuss and present design ideas and findings about a soundscape using aural, oral and visual methods · Research, construct and implement a sound-design project to represent a soundscape.
ST332 & ST409 Medical Statistics: 2024-2025 Assignment [20%] Deadline: 13:00 Thursday 13th March 2025 Background Colleagues at University of Warwick and University Hospitals Coventry & Warwickshire (UHCW) NHS Trust have undertaken an unmatched case-control study to assess whether vaping (i.e. use of e-cigarettes) is a potential risk factor for Chronic obstructive pulmonary disease (COPD). They as well as collecting information on whether individuals had ever used or were current users of vape/e-cigarette products and whether they had been diagnosed with COPD, they also collected data on various covariates, including; their age, smoking history, socio-economic deprivation and sex. Dataset The R dataset ccvape.RData contains synthetic data on 300 cases (people who had been diagnosed with COPD) from clinics at UHCW NHS Trust together with 300 controls (people who had been diagnosed with COPD, but had been treated at UHCW NHS Trust for other conditions). The dataset contains the following variables and associated codes; case (0=control,1=case) vape (0=never user,1=former or current user of vapes/e-cigarettes) age (age in years) smoke (0=never smoked,1=current or former smoker) dep (0=none,1=deprived) male (0=female,1=male) Assignment Tasks PART A [Maximum 5 pages – 15 marks] Answer the following questions based on the ccvape.RData dataset described above. Note it is not necessary to write a report – just answer the questions, but do not include R code or paste R output (construct appropriate tables – you can include high quality plots from R if you wish) – marks will be lost for this. 1. Undertake an Exploratory Data Analysis of the ccvape.RData dataset, in particular identify any covariates which may be potential confounders. [5 marks] 2. Calculate and interpret the unadjusted Odds Ratio (OR) and it’s 95% CI and P-value to assess the association between vaping and being a case/control. [2 marks] 3. Using a binomial GLM calculate and interpret the Odds Ratio (OR) and it’s 95% CI and P-value to assess the association between vaping or not and being a case/control adjusting for the potential confounding variables you identified in Q.1. [3 marks] 4. Are any other variables also required to be included in the binomial GLM you fitted in Q.3? Justify your ‘final model’ and contrast the results obtained with those you obtained in Q.2 and Q.3. [3 marks] 5. Your clinical colleagues suggest that a 20% relative increase in the risk of being diagnosed with COPD associated with vape use would be of public health importance (given the number of people in the population who now vape/use e-cigarettes). They also suggest that a 10% probability of making a Type II error and a 5% probability of making a Type I error would be appropriate in any future study. Describe how you would design (including a sample size calculation) a future study based on this information (and the results of your analyses in questions 1 to 4). [2 marks] PART B [500 words maximum - 5 marks] Based on your answers to PART A write a Press Release about the study for the University to make available to the media. Marks will be awarded for; • Appropriate interpretation of the results in PART A • Discussion of strengths and limitations of the study • Appropriate use of non-technical language for an informed lay audience • Inclusion of an “anonymous” quote from you as “Researcher Jo Bloggs said … “” ” • Discussion of potential next steps/further research You can find examples of Press Releases on the University website at; https://warwick.ac.uk/newsandevents/pressreleases/and two good recent health-related examples are; https://warwick.ac.uk/newsandevents/pressreleases/?newsItem=8a17841b8defef98018df5c098261254and https://warwick.ac.uk/newsandevents/pressreleases/?newsItem=8a17841a8d79730b018d9e2bbb0e054b General Instructions It is expected that PART A and B will be professionally presented (e.g. using LaTeX, WORD or R Markdown) and submitted on Moodle as a single pdf document. Do NOT put your name on the document or in the filename, please just use your student number. For PART B include a word count at the end of the Press Release. No appendices are allowed. General questions about the assignment can be posted (anonymously if you wish) to the Discussion Forum on Moodle page for ST332, and these will be answered, questions emailed directly will not be answered.
Department of Computer Science COMP212 - 2025 - CA Assignment 1 Coordination and Leader Election Simulating and Evaluating Distributed Protocols in Java Assessment Information Assignment Number 1 (of 2) Weighting 15% Assignment Circulated 7th February 2025 Deadline 6th March 2025, 17:00 UK Time Submission Mode Electronic via CANVAS Learning outcomes assessed (1) An appreciation of the main principles underlying distributed systems: processes, communication, nam- ing, synchronisation, consistency, fault tolerance, and security. (3) Knowledge and understanding of the es- sential facts, concepts, principles and theories relating to Computer Science in general, and Distributed Com- puting in particular. (4) A sound knowledge of the crite- ria and mechanisms whereby traditional and distributed systems can be critically evaluated and analysed to de- termine the extent to which they meet the criteria de- fined for their current and future development. Purpose of assessment Marking criteria This assignment assesses the understanding of coordina- tion and leader election in distributed systems and im- plementing, simulating, and evaluating distributed pro- tocols by using the Java programming language. Marks for each question are indicated under the corre- sponding question. Submission necessary in order to satisfy Module requirements? No Late Submission Penalty Standard UoL Policy. 1 Overall marking scheme The coursework for COMP212 consists of two assignments contributing altogether 30% of the final mark. The contribution of the individual assignments is as follows: Assignment 1 15% Assignment 2 15% TOTAL 30% 2 Objectives This assignment requires you to implement in Java two distributed algorithms for leader election in a ring network and then to experimentally validate their correctness and evaluate their performance. 3 Description of coursework Throughout this coursework, the network on which our algorithms are to be executed is a bidirectional ring, as depicted in Figure 1. Figure 1: A bidirectional ring network on n processors. In our setting, all processors execute the same algorithm, do not know the number n of processors in the system in advance, but they do know the structure of the network and are equipped with unique ids. The ids are not necessarily consecutive and for simplicity you can assume that they are chosen from {1, 2,...,αn}, where α ≥ 1 is a small constant (e.g., for α = 3, the n processors will be every time assigned unique ids from {1, 2, . . . , 3n − 1, 3n}). Additionally, every processor can distinguish its clockwise from its counterclockwise neighbour, so that, for example, it can choose to send to only one of them or to send a different message to each of them. Processors execute in synchronous rounds, as in every example we have discussed so far in class. 3.1 Implementing the LCR Algorithm—30% of the assignment mark As a first step, you are required to implement the LCR algorithm for leader election in a ring. The pseudocode of the non-terminating version of LCR can be found in the lecture notes and is also given here for convenience (Algorithm 1). Algorithm 1 LCR (non-terminating version) Code for processor ui , i ∈ {1, 2,..., n}: Initially: ui knows its own unique id stored in myIDi sendIDi := myIDi statusi := “ unknown ” 1: if round = 1 then 2: send ⟨sendIDi ⟩ to clockwise neighbour 3: else// round > 1 4: upon receiving ⟨inID⟩ from counterclockwise neighbour 5: if inID > myIDi then 6: sendIDi := inID 7: send ⟨sendIDi ⟩ to clockwise neighbour 8: else if inID = myIDi then 9: statusi := “ leader ” 10: else if inID < myIDi then 11: do nothing 12: end if 13: end if You are required to implement a terminating version of the LCR algorithm in which all processors eventually terminate and know the id of the elected leader. 3.2 Implementing the HS Algorithm—30% of the assignment mark Next, you are required to implement another algorithm for leader election on a ring, known as the HS algorithm. As LCR, HS also elects the processor with the maximum id. The main difference is that HS, instead of trying to send ids all the way around in one direction (which is what LCR does), has every processor trying to send its id in both directions some distance away (e.g., k) and then has the ids turn around and come back to the originating processor. As long as a processor succeeds, it does so repeatedly (in “phases”) to successively greater distances (doubling the distance to be travelled each time, e.g., 2k). See Figure 2 for an illustration. Figure 2: Trajectories of successive “phases” originating at processor u4 (imagine the rest of the processors doing something similar in parallel, but not depicted here). The id transmitted byu4 aims to travel some distance out in both directions and then return back. If it succeeds, then u4 doubles the aimed distance and repeats. Informally, each processor ui “operates in phases” l = 0, 1, . . . (where each phase l consists of one or more rounds). In each phase l, processor ui sends out a “token” (i.e., a message) containing its id idi in both directions. These are intended to travel distance 2l (that is, as in Figure 2, distance 20 = 1 for l = 0, distance 21 = 2 for l = 1, distance 22 = 4 for l = 2, and so on) and then return to their origin. If both tokens manage to return back then ui goes to the next phase, otherwise it stops to produce its own tokens (and only performs from that point on the rest of the algorithm’s operations). A token is discarded if it ever meets a processor with greater id while travelling outwards (away from its origin). While travelling inwards (back to its origin), a token is forwarded by all processors without any check. The termination criterion is as follows: If a token travelling outwards meets its own origin ui (meaning that this token managed to perform a complete turn of the whole ring while travelling outwards), thenui elects itself as the leader. Observe that in order for tokens to know how far they should travel each time and in which direction, this information has to be included inside the transmitted messages (that is, apart from the id being transmitted, the messages should also contain this auxiliary information). The pseudocode of the non-terminating version of HS is given in Algorithm 2. As with LCR, you are required to implement a terminating version of the HS algorithm in which all processors eventually terminate and know the id of the elected leader. 3.3 Experimental Evaluation, Comparison & Report—40% of the assignment mark After implementing the terminating LCR and HS algorithms, the next step is to conduct an experimental evaluation of their correctness and performance. Correctness. Execute each algorithm in rings of varying size (e.g., n = 3, 4, . . . , 1000, . . .; actually, up to a point where simulation does take too much time to complete) and starting from various different id assignments for each given ring size. For instance, you could execute them on both specifically constructed id assignments (e.g., ids ascending clockwise or counterclockwise) and random id assignments. In each execution, your simulator should check that eventually precisely one leader is elected. Of course, this will not be a replacement of a formal proof that the algorithms are correct as you won’t be able to test them on all possible combinations of ring sizes and id assignments, but at least it will be a first indication that they may do as intended. Performance. Execute, as above, each algorithm in rings of varying size and starting from various different id assignments for each given ring size. For each execution, your simulator should record the number of rounds and the total number of messages transmitted until termination. 1. Execute both algorithms in rings of varying size for the case in which ids are always clockwise ordered. 2. Execute both algorithms in rings of varying size for the case in which ids are always counterclockwise ordered. 3. Execute both algorithms in rings of varying size and various random id assignments for each given ring size. Note here that both algorithms should be simulated (e.g., one after the other) on every given choice of ring size and id assignment, so that a comparison of their performance makes sense. In Summary: For both correctness validation and performance evaluation a suggestion is to simulate both algorithms (for all types of id assignments mentioned above) in rings containing up to at least 1000 processors. Specifically in the case of random id assignments, for each ring size n repeat the simulation for many different id assignments (e.g., at least 100 distinct simulations) and record the correctness and the worst, the best, and the average performance so that you get meaningful results. Algorithm 2 HS (non-terminating version) Messages are triples of the form ⟨ID,direction,hopCount⟩, where direction ∈ {out,in} and hopCount positive integer. Code for processor ui , i ∈ {1, 2,..., n}: Initially: ui knows its own unique id stored in myIDi sendClocki containing a message to be forwarded clockwise or null, initially sendClocki := ⟨myIDi , out,1⟩ sendCounterclocki containing a message to be forwarded counterclockwise or null, initially sendCounterclocki := ⟨myIDi , out,1⟩ statusi ∈ { “ unknown ” , “ leader ”}, initially statusi := “ unknown ” phasei recording the current phase number, nonnegative integer, initially phasei = 0 1: upon receiving ⟨inID, out,hopCount⟩ from counterclockwise neighbour 2: if inID > myIDi and hopCount > 1 then 3: sendClocki := ⟨inID, out,hopCount − 1⟩ 4: else if inID > myIDi and hopCount = 1 then 5: sendCounterclocki := ⟨inID, in,1⟩ 6: else if inID = myIDi then 7: statusi := “ leader ” 8: end if 9: 10: upon receiving ⟨inID, out,hopCount⟩ from clockwise neighbour 11: if inID > myIDi and hopCount > 1 then 12: sendCounterclocki := ⟨inID, out,hopCount − 1⟩ 13: else if inID > myIDi and hopCount = 1 then 14: sendClocki := ⟨inID, in,1⟩ 15: else if inID = myIDi then 16: statusi := “ leader ” 17: end if 18: 19: upon receiving ⟨inID, in,1⟩ from counterclockwise neighbour, in which inID ≠ myIDi 20: sendClocki := ⟨inID, in,1⟩ 21: 22: upon receiving ⟨inID, in,1⟩ from clockwise neighbour, in which inID ≠ myIDi 23: sendCounterclocki := ⟨inID, in,1⟩ 24: 25: upon receiving ⟨inID, in,1⟩ from both clockwise and counterclockwise neighbours, in both of which inID = myIDi holds 26: phasei := phasei + 1 27: sendClocki := ⟨myIDi , out,2phasei⟩ 28: sendCounterclocki := ⟨myIDi , out,2phasei⟩ 29: 30: // The following to be always executed by all processors, i.e., 31: // also in round 1 in which no message has been received 32: send ⟨sendClocki ⟩ to clockwise neighbour 33: send ⟨sendCounterclocki ⟩ to counterclockwise neighbour After gathering the simulation data, plot them as follows. In each plot, the x-axis will represent the (increasing) size of the ring and the y-axis will represent the complexity mea- sure (e.g., number of rounds or number of messages). You may produce individual plots depicting the performance of each algorithm (possibly comparing against standard complex- ity functions, like n, nlog n, or n2 ) and you are required to produce plots comparing the performance of both algorithms in identical settings. For example, when measuring the total number of messages in the case of counterclockwise increasing ids, a plot would show at the same time the performance of both algorithms for increasing ring size n, using curves of different colours and possibly also a legend with explanations. Then, for each given ring size, the corresponding point of each curve will represent the total number of messages generated by the algorithm (indicated on the y-axis). You can use gnuplot, JavaPlot or any other plotting software that you are familiar with. The final crucial step is to prepare a concise report (at most 5 pages including plots) clearly describing your design, the main functionality of your simulator, the set of experi- ments conducted, and the findings of your experimental evaluation of the above algorithms. In particular, in the latter part you should try to draw conclusions about (i) the algorithms’ correctness and (ii) the performance (time and messages) of each algorithm individually (e.g., what was the worst/best/average performance of each algorithm as a function of n? For example, we know from the lectures that the worst-case communication complexity of LCR is O(n2 ): can you verify this experimentally?) and when the two algorithms are being compared against each other (e.g., which one performs better and in which settings?).
PHAS0008 - Experimental Methods Coursework (2024-25) To be submitted via Moodle/Turnitin by 17:00 on Monday 24th February 2025. Answers to questions 1 - 3 should be type-written and submitted as a single pdf file. Please be careful to explain your methods and working in your pdf file. Supporting Python notebooks or spreadsheets for Q1c should be uploaded separately, but please note that your pdf file should contain your answers and explain fully your working for these questions. All files submitted must be named in the following format: • {student number}-{description}.{extension} Useful information can be found on the PHAS0008 Moodle pages, for example under Module Resources and Documentation. Please remember we expect our students to maintain the highest Academic Integrity during their studies - please read the guidance and note that you will be required to submit your UCL Academic Integrity course Certificate before you can submit your coursework. Provisional part marks are shown in [brackets]. Q 1. a) Write down expressions for r2, x2 and reduced xr(2)ed, and explain the meaning of each symbol in these equations. [3] b) Explain how r 2 and reduced xr(2)ed tests can be used to provide guidance on which of two or more theoretical models is the best fit to experimental data. Explain also, using sketches if necessary, the meaning of the term overfitting in the context of theoretical fits to experimental data. [5] c) Students were tasked to use a current balance to investigate how the magnetic force, F, perpendicular to a linear conducting wire varies with the current I. To this end, the position, y, of a small counterweight was measured such that F Ⅸ y. Diagrams and the data collected are given in the supporting file PHAS0008-CW-data-images-2024-25-v1 and the Table below. Table 1: Current balance data for y vs I Current (I) / A Runs 1,2,3 ΔI Counterweight position (y) / mm Run 1 Run 2 Run 3 Δ y 0.2 0.005 1.6 1.6 1.4 0.2 0.4 0.005 3.2 3.4 3.2 0.2 0.6 0.005 4.8 5.0 4.8 0.2 0.8 0.005 7.4 6.6 6.6 0.2 1 0.005 8.6 8.8 8.6 0.2 1.2 0.005 11.2 10.8 11.0 0.2 1.4 0.005 12.6 12.8 12.8 0.2 1.6 0.005 14.8 14.6 14.6 0.2 1.8 0.005 17.0 17.2 17.2 0.2 2 0.005 19.4 19.2 19.2 0.2 By minimizing x2, fit the data for y vs I to a series of polynomials of form.: a0, a0 + a1I, a0 + a1I + a2I2, and a0 + a1I + a2I2 + a3I3 . Plot the data and the fits. For each fit, please also give r 2, x2 and reduced xr(2)ed. Please note: you should fit to and plot one combined mean dataset (with appropriate uncertainties) rather than Runs 1, 2 and 3 separately. Please also remember to show all your working in your main pdf file. [10] Hence, discuss which order polynomial provides the best fit to the data and, therefore, whether the results are consistent with a linear relationship between the magnetic force, F, and current, I. Comment also on whether the students have estimated their uncertainties correctly, and whether their experiment would benefit from another repeat with the same equipment. [6] Q 2. a) Summarise the steps involved in conducting a risk assessment for an experiment. When should a risk assessment be conducted? [2] b) Explain what PAT testing means, and, in this context, Class 1, Class 2 and Class 3 appliances. Sketch the symbols for Class 1, Class 2 and Class 3 appliances, and give 2 examples of each type of appliance. [4] c) With regards to laser safety, explain what is meant by the acronyms AOR, AEL and MPE. Describe the basis for the following classifications of lasers: Class 1C, Class 2M and Class 4. [4] d) To provide the Laboratories with liquid nitrogen, a 150 L cryogen storage dewar is filled on the loading bay outside the Department (entrance near the Massey lecture theatre), carried up in the lift and thence into the Lab. Write a risk assessment for this procedure. Please remember to be quantitative where possible, for example, when considering the volume of nitrogen and the lift. [6] Q 3. a) In the context of referencing, explain what is meant by the acronym “ DOI” . Please give the DOI for the PHAS0008 “Experimental Guidance Notes” on experiment NX741. [2] Choose two research articles published in the last 12 months that you find interesting in the broad field of physics, selected from the JournalsNature,Science,Nature Physics, Nature CommunicationsorPhysical Review letters. At least one of your choices should include a UCL author, for example from the UCL Department of Physics & Astronomy, to be highlighted in bold. Please be careful to choose original research articles, rather than reviews/commentaries. b) For your chosen articles, provide the full reference in IEEE format, including the title and DOI. [2] c) Write a short (maximum 100 word) summary of each of your papers (so, 2 summaries in total) in your own words, including the context, experimental methods, main results and conclusions/significance. An example, from 2023, is provided below. [6] [1] Albrechtsen, S.H., Schouder, C.A., Viñas Muñoz, A., Christensen, J.K., Petersen, C.E., Pi, M., & Stapelfeldt, H. Observing the primary steps of ion solvation in helium droplets. Nature 623, 319–323 (2023).https://doi.org/10.1038/s41586-023-06593-5 The interaction of ions with solvents is an important fundamental process, with relevance across a broad range of natural and applied sciences. This paper describes experiments in which sequential solvation of a sodium ion by helium atoms was probed over picosecond time-scales. The team first created liquid helium nanodroplets. By exploiting laser-induced ionisation of neutral atoms, they then implanted a single Na+ and, a short time later, Xe+ ion. Spectroscopic interrogation of the ejection of Na+ Hen clusters was then used to track the sodium-helium solvation in realtime. This work opens the door for new insight into time-resolved ion-solvent binding. [100 words]
Advanced Econometrics I EMET4314/8014 Semester 1, 2025 Assignment 1 (due by: Tuesday week 2, 11:00am) Exercises Provide transparent derivations. Justify steps that are not obvious. Use self sufcient proofs. Make reasonable assumptions where necessary. 1. Consider the space Z = (0, 1] equipped with the metric d(x, y) = |x − y|. Consider the following sequence in Z: xn = 1/n, n = 1, 2,.... Is it a Cauchy sequence? Does it converge? 2. Let X, Y be elements from a Hilbert space. Prove: (i) Cauchy-Schwarz inequality: |⟨X, Y⟩| ≤ ∥X∥ · ∥Y∥ (ii) Triangle inequality: ∥X + Y∥≤∥X∥ + ∥Y∥ 3. Prove: If E (X2) < ∞ and E (Y2) < ∞, then • |EX| < ∞ and |EY | < ∞; • |E(XY )| < ∞; • |Cov(X, Y )| < ∞. This is useful: to guarantee existence of covariances, we only need fnite second moments. That is why we defne L2 to be the space of random variables with fnite second moments. Related useful fact (for your enjoyment, no need to prove): E (|Y | p) < ∞ implies E (|Y | q) < ∞ for 1 ≤ q ≤ p (by Liapunov’s inequality). 4. Prove: Cov(X, Y ) = Cov(X, E(Y |X)) 5. Prove: if X ∈ {0, 1} then Var/Cov(X,Y X ) = E(Y |X = 1) − E(Y |X = 0). 6. Consider the space L2, as defned in the lecture. Let X, Y ∈ L2. Prove that E(XY ) is an inner product. 7. Let X2, X3, Y ∈ L2. Find the projection of Y on sp (X2, X3). (What I’m trying to say here is that you are NOT including the constant in the span.) Use the following Gram-Schmidt orthogonalization procedure to construct an or-thonormal set: Lemma 1 (Gram-Schmidt). Let V1, V2, V3,... be a linearly independent sequence in an inner product space. Set U1 = V1/||V1||, and defne recursively: Then U1, U2, U3,... is an orthonormal sequence with sp(U1, U2,...,Uk) = sp(V1, V2,...,Vk). 8. Let X1,...,XK, Y ∈ L2. Use calculus to derive the following: where X := (X1,...,XK)′ so that dim X = dim b = K × 1. This demonstrates that Psp(X1,...,XK)Y can also be obtained by “traditional” methods.
CIT 5960 2025 - HW4 (deadlines as per Gradescope) This homework deals with the following topics * sorting algorithms that are faster than n log n * one question on DFS • The homework has to be submitted in electronic form. as a pdf file. You can use any editing software you want in order to type up and then produce the pdf. No handwritten solutions. • For any algorithm that we have done in class that you want to use, you are allowed to say “using Quicksort” or something like that. You are also allowed to just use its runtime without having to reprove it. • Explain all the intermediate mathematical simplification steps. • For a question that involves an algorithm that we cover in class, you can use the final big-O result. No need to show the derivation again. For example, if binary search shows up in your algorithm you can just say “we know binary search is O(log n).” • For all questions in this HW and subsequent HWs the goal is to find algorithms that are most efficient in terms of big-O analysis. You do receive partial credit if your algorithm is less efficient than the best. You do not receive credit though if your algorithm computes an incorrect result. So be sure to check for correctness before you worry about efficiency. • Unless otherwise specified, you should write your algorithm analysis as “In the worst case, this algorithm is ....”. • Reminder: Your algorithm should not rely on a fancy data structure in a particular language. Your algorithm needs to be in plain English or in pseudocode. Real code that has bugs could easily result in loss of points. • You do not have to worry about tiny edge cases like empty arrays, empty lists etc. • For these questions remember that the list of sorting algorithms that we have covered is selection sort, insertion sort, merge sort, quick sort, counting sort, and radix sort. Student Name: 〈 Your Name 〉 Collaborators(if any) : 〈 Your Collaborators 〉(at most 2 other collaborators) Questions 1. (3 points) You are giving an array of n 5-digit numbers. Instead of using the usual base 10 representation, you decide to convert all these numbers into a different base. Your new base is going to be m. Assume that to convert a single 5 digit number to base m there is a function that works in constant time. Now after all numbers are converted to base m, you decide to run radix sort on these new numbers using a counting sort in each of the steps. What is the runtime going to be? Please explain. 2. (7 points) There is an n sized array which contains integers in the range [1, n3]. Which sorting algorithm (among the ones covered in class) should we use and why? List all the sorting algorithms that we covered in lectures, tell us the big-O run time that each of them could take in this situation. Please explain why they would take that runtime. No pseudocode needed here. The points are for the discussion of the algorithms. 3. (7 points) Watch this famous obama sorting video. Bubble sort, an algorithm that we did not cover, is a Θ(n2) algorithm. We’d like you to help the former president by discussing all the algorithms that we have covered in this class and whether or not they would be applicable in the situation that was provided to him.
Problem Set 2 Due: Friday, February 21, 5:00 p.m. Eastern Time Applied Econ 440.602: Macroeconomic Theory Spring, 2025 1. Balanced-Budget Multipliers. Consider a closed economy where aggregate planned expenditure is given by: and consumption and output are given by: where I have used the same notation that we used in class. Also, maintain the assumptions that we did in class that c and d are positive and that mpc is between zero and one. An equilibrium requires that Y = Y pe. Assume that, initially, the government is running a balanced budget, meaning that T = G. (a) Consider a proposal for the government to abandon its balanced budget and engage in deficit spending. That is, G goes up, but T remains fixed at its initial level. Assume that the central bank acts to keep the real interest rate constant: r = , an exogenous constant, both before and after any change in fiscal policy. i. Derive an expression for the government-spending multiplier . ii. Is this government-spending multiplier positive, negative, or zero? Explain how you know mathematically. iii. Is this government-spending multiplier greater than, less than, or equal to one in absolute value? Explain how you know mathematically. (b) Consider a proposal to expand government spending, while maintaining a balanced budget G = T. That is, G goes up, and T is simultaneously adjusted to increase dollar-for-dollar with G. Again, assume that the central bank acts to keep the real interest rate constant: r = , an exogenous constant, both before and after any change in fiscal policy. i. Derive an expression for the government-spending multiplier . ii. Is this government-spending multiplier positive, negative, or zero? Explain how you know mathematically. iii. Is this government-spending multiplier greater than, less than, or equal to one in absolute value? Explain how you know mathematically. (c) Now, let’s compare the above two proposals. Which one will result in a larger change in output? Explain how you know mathematically, and provide a brief economic explanation in words. (d) When analyzing each proposal, we assumed that the central bank took action to keep interest rates constant. Assume that prices are totally sticky, so the real interest rate is equal to the nominal interest rate. When the fiscal authority increases G, what happens to Md , the demand curve in the market for money? What does the central bank have to do in the market for money to keep interest rates constant? Provide a supply-and-demand graph along with a brief explanation in words. 2. An Alternative IS Curve and a Deflationary Shock. This question will have you derive a version of the IS curve, which characterizes an equilibrium in the market for goods, under an alternative set of assumptions about investment behavior. Then, you’ll use this IS curve to develop an AD-AS framework to analyze the response to a deflationary shock. Assume that the economy is closed, so equation (1) continues to describe aggregate planned expenditures. Also, like before, assume that consumption is given by equation (2). In class, we had assumed that investment only depended on interest rates. However, one might also expect investment to depend on income. For instance, one component of investment is expenditure on new housing, and someone with more income is likely to buy a larger house. Suppose that investment is given by: where is autonomous investment, r is the real interest rate on bonds, d is a parameter that governs the sensitivity of investment to interest rates, and mpi is the marginal propensity to invest. Assume that mpi > 0, and mpi + mpc < 1. Monetary policy and aggregate supply take the same forms that we assumed in class. Specifically, the central bank follows the interest-rate rule: The aggregate supply (AS) curve takes the usual form. In each of the above two expressions, the notation is the same as we introduced in class. (a) Derive the IS curve under these alternative assumptions. In other words, provide an expression for Y in terms of r. (This expression will also contain the exogenous quantities , , , and , as well as the parameters mpc, c, d, and mpi.) (b) Your answer to part (a) should show that Y is decreasing in r. Let’s investigate this negative relationship more closely. i. If r increases by one, then how much does Y decrease? ii. Does a high value of mpi make Y more or less sensitive to changes in r? In a sentence or two, provide an economic interpretation. (c) Derive the aggregate demand (AD) curve. That is, combine the IS curve with the monetary policy function (5) to write Y as a function of π. (d) Assume that the economy is initially in a long-run equilibrium, with Y = Y p . Then, suppose that there’s a court decision that gives unions less bargaining power, making it harder for them to obtain higher wages. We will model this change as an decrease in ρ. In a sentence or two, explain why the decrease in union bargaining power would act as an deflationary shock. (e) Depict the short-run response of the economy to the court decision using an AD-AS graph, holding all else equal. Label the axes appropriately, and label potential output appropriately. Label the initial aggregate demand curve as AD; label the initial aggregate supply curve as AS; label the new aggregate supply curve as AS′ . What happens to inflation and output? (f) Suppose that the central bank responds to the court decision by trying to stabilize output. That is, the central bank adjusts r in order to keep Y at Y p . Use an AD-AS graph to depict the court decision and the central bank’s response in the short run. Label the axes appropriately, and label potential output appropriately. Label the initial aggregate demand and supply curves as AD and AS; label the new aggregate demand and supply curves as AD′ and AS′ . When the central bank acts to stabilize output, what happens to inflation, relative to the case where the central bank doesn’t adjust r at all? (g) Suppose that, instead of trying to stabilize output, the central bank responds to the court decision by trying to stabilize inflation. That is, the central bank adjusts r in order to keep π at its initial level. Use an AD-AS graph to depict the court decision and the central bank’s response in the short run. Label the axes appropriately, and label potential output appropriately. Label the initial aggregate demand and supply curves as AD and AS; label the new aggregate demand and supply curves as AD′ and AS′ . When the central bank acts to stabilize inflation, what happens to output, relative to the case where the central bank doesn’t adjust r at all? (h) Now, suppose that the central bank ultimately decides to take a hands-off approach, meaning that it doesn’t adjust r at all after the court decision. For the economy to transition to a new long-run equilibrium, what curve(s) shift(s), and why? Draw an AD-AS graph depicting the shift, with the curves and axes appropriately labeled. 3. Exploring Okun’s Law. The relationship between real output and unemployment is known as Okun’s law. There are several versions of Okun’s law, and this exercise will have you explore them empirically. Start off by going to FRED to get the following data: unemployment (UNRATE) and real GDP (GDPC1). The raw unemployment data is monthly, but we’ll have to make it quarterly to match the GDP data. When you pull up the unemployment data in FRED, click on “Edit Graph,” go to “Modify Frequency,” and select “Quarterly,” with aggregation method “End of Period.” Your sample for the variables should run from 1948 to present, when both variables are available. (Some, but not all, of the questions below will specialize to the pre-2020 sample to avoid large outliers.) Throughout, Let ut denote the unemployment rate at date t, let Yt denote real GDP at date t, and let %∆Yt ≡ (Yt − Yt−1) /Yt−1 denote the GDP growth rate at date t. (a) The version of Okun’s law presented in Mishkin says that the growth rate of output %∆Yt is systematically related to the change in unemployment ∆ut ≡ ut − ut−1. i. Construct two scatter plots with %∆Yt on the horizontal axis and ∆ut on the vertical axis. For the first plot, use only data from 1948 to 2019. For the second plot, use the full sample from 1948 to present. ii. Using the 1948–2019 sample, compute the correlation coefficient between %∆Yt and ∆ut. iii. Using the 1948–2019 sample, estimate the regression: where ϵt is a residual. What is the estimated value of β1? (b) Some economists prefer to look at how deviations from trend in GDP are related to deviations from trend in unemployment. To do so, it’s necessary to specify what, exactly, we mean by trend. We’ll use the Hodrick-Prescott (HP) filter. For any variable xt, the HP trend τt is the solution to the following minimization problem: where λ > 0 is a parameter that controls how smooth the trend is. Notice that the first sum is minimized by setting τt = xt, and the second sum is minimized by a linear trend (by setting ∆τt equal to a constant). Hence, a low value of λ produces a more flexible trend that closely tracks all the fluctuations in the data, and a high value of λ produces a more rigid trend that’s closer to a straight line. When working with quarterly data, it’s standard practice to set λ = 1, 600, and that’s what you should do when applying the HP filter in this exercise. In Stata, you can compute the HP trend using the command tsfilter hp. In MATLAB, you can compute the HP trend using the command hpfilter. (You can also download an add-in for Microsoft Excel that will compute the HP filter, but I encourage you to do the exercise using a statistical or mathematical coding language.) i. Let τt (u) denote the HP trend of ut. Let u ∗ t ≡ ut −τt (u) denote the deviation in unemployment from the HP trend. Plot u*t over the sample from 1948–2019. ii. Let τt (y) denote the HP trend of log (Yt), where log (·) denotes the natural logarithm. Let y*t ≡ 100 × h log (Yt) − τt (y) i denote the deviation in log GDP from the HP trend, multiplied by 100. (By looking at the log deviation times 100, we can interpret y*t as the approximate percent deviation from trend in real GDP.) Plot y*t over the sample from 1948–2019. iii. Using the 1948–2019 sample, construct a scatter plot with y*t on the horizontal axis and u*t on the vertical axis. iv. Using the 1948–2019 sample, what is the correlation coefficient between y*t and u*t? v. Using the 1948–2019 sample, estimate the regression: where ϵt is a residual. What is the estimated value of β1?
