100 Units. Computability topics are discussed (e.g., the s-m-n theorem and the recursion theorem, resource-bounded computation). The Data Science Clinic will provide an understanding of the life cycle of a real-world data science project, from inception and gathering, to modeling and iteration to engineering and implementation, said David Uminsky, executive director of the UChicago Data Science Initiative. Boolean type theory allows much of the content of mathematical maturity to be formally stated and proved as theorems about mathematics in general. 5747 South Ellis Avenue Prerequisite(s): One of CMSC 23200, CMSC 23210, CMSC 25900, CMSC 28400, CMSC 33210, CMSC 33250, or CMSC 33251 recommended, but not required. The course also emphasizes the importance of collaboration in real-world software development, including interpersonal collaboration and team management. 100 Units. Each subject is intertwined to develop our machine learning model and reach the "best" model for generalizing the dataset. Final: TBD. Big Brains podcast: Is the U.S. headed toward another civil war? Some methods for solving linear algebraic systems will be used. We will focus on designing and laying out the circuit and PCB for our own custom-made I/O devices, such as wearable or haptic devices. Our emphasis is on basic principles, mathematical models, and efficient algorithms established in modern computer vision. 100 Units. They also allow us to formalize mathematics, stating and proving mathematical theorems in a manner that leaves no doubt as to their meaning or veracity. The system is highly catered to getting you help fast and efficiently from classmates, the TAs, and myself. Parallel Computing. 432 pp., 7 x 9 in, 55 color illus., 40 b&w illus. A computer graphics collective at UChicago pursuing innovation at the intersection of 3D and Deep Learning. 100 Units. Prerequisite(s): CMSC 15400 Students may also earn a BA or BS degree with honors by attaining the same minimum B grade in all courses in the major and by writing a successful bachelor's thesis as part of CMSC29900 Bachelor's Thesis. Instructor(s): B. SotomayorTerms Offered: Winter Tivadar Danka. Mathematical topics covered include linear equations, regression, regularization,the singular value decomposition, and iterative algorithms. We also discuss the Gdel completeness theorem, the compactness theorem, and applications of compactness to algebraic problems. Tensions often arise between a computer system's utility and its privacy-invasiveness, between its robustness and its flexibility, and between its ability to leverage existing data and existing data's tendency to encode biases. Prerequisite(s): CMSC 12200, CMSC 15200 or CMSC 16200. B: 83% or higher 100 Units. These were just some of the innovative ideas presented by high school students who attended the most recent hands-on Broadening Participation in Computing workshop at the University of Chicago. CMSC22200. While this course should be of interest for students interested in biological sciences and biotechnology, techniques and approaches taught will be applicable to other fields. This course is an introduction to programming, using exercises in graphic design and digital art to motivate and employ basic tools of computation (such as variables, conditional logic, and procedural abstraction). CMSC29512may not be used for minor credit. Dependent types. Equivalent Course(s): MAAD 20900. provided on Canvas). B-: 80% or higher At the end of the sequence, she analyzed the rollout of COVID-19 vaccinations across different socioeconomic groups, and whether the Chicago neighborhoods suffering most from the virus received equitable access. Her experience in Introduction to Data Science not only showed her how to use these tools in her research, but also how to effectively evaluate how other scientists deploy data science, AI and other approaches. Designed to provide an understanding of the key scientific ideas that underpin the extraordinary capabilities of today's computers, including speed (gigahertz), illusion of sequential order (relativity), dynamic locality (warping space), parallelism, keeping it cheap - and low-energy (e-field scaling), and of course their ability as universal information processing engines. This course introduces the basic concepts and techniques used in three-dimensional computer graphics. CMSC16200. We will explore these concepts with real-world problems from different domains. CMSC28515. Unsupervised learning and clustering All students will be evaluated by regular homework assignments, quizzes, and exams. This is a rigorous mathematical course providing an analytic view of machine learning. While digital fabrication has been around for decades, only now has it become possible for individuals to take advantage of this technology through low cost 3D printers and open source tools for 3D design and modeling. A physical computing class, dedicated to micro-controllers, sensors, actuators and fabrication techniques. Prerequisite(s): CMSC 27100 or CMSC 27130, or MATH 15900 or MATH 19900 or MATH 25500; experience with mathematical proofs. The new paradigm of computing, harnessing quantum physics. Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. 100 Units. I had always viewed data science as something very much oriented toward people passionate about STEM, but the data science sequence really framed it as a tool that anyone in any discipline could employ, to tell stories using data and uncover insights in a more quantitative and rigorous way.. Most of the skills required for this process have nothing to do with one's technical capacity. Equivalent Course(s): STAT 27700, CMSC 35300. CMSC11900. Defining and building the future of computer science, from theory to applications and from science to society. This course will present a practical, hands-on approach to the field of bioinformatics. Equivalent Course(s): STAT 11900, DATA 11900. Winter Chicago, IL 60637 100 Units. The Lasso and proximal point algorithms Reflecting the holistic vision for data science at UChicago, data science majors will also take courses in Ethics, Fairness, Responsibility, and Privacy in Data Science and the Societal Impacts of Data, exploring the intensifying issues surrounding the use of big data and analytics in medicine, policy, business and other fields. This course is an introduction to database design and implementation. The textbooks will be supplemented with additional notes and readings. Vectors and matrices in machine learning models Students can select data science as their primary program of study, or combine the interdisciplinary field with a second major. Certificate Program. Feature functions and nonlinear regression and classification 100 Units. Equivalent Course(s): MPCS 54233. In this course we will study the how machine learning is used in biomedical research and in healthcare delivery. 2017 The University of Chicago TTIC 31180: Probabilistic Graphical Models (Walter) Spring. . 100 Units. Instructor(s): Lorenzo OrecchiaTerms Offered: Spring We compliment the lectures with weekly programming assignments and two larger projects, in which we build/program/test user-facing interactive systems. This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. Instructor(s): ChongTerms Offered: Spring Develops data-driven systems that derive insights from network traffic and explores how network traffic can reveal insights into human behavior. Data-driven models are revolutionizing science and industry. CMSC25700. Matrix Methods in Data Mining and Pattern Recognition by Lars Elden. In order for you to be successful in engineering a functional PCB, we will (1) review digital circuits and three microcontrollers (ATMEGA, NRF, SAMD); (2) use KICAD to build circuit schematics; (3) learn how to wire analog/digital sensors or actuators to our microcontroller, including SPI and I2C protocols; (4) use KICAD to build PCB schematics; (5) actually manufacture our designs; (6) receive in our hands our PCBs from factory; (7) finally, learn how to debug our custom-made PCBs. Youshould make the request for Pass/Fail grading in writing (private note on Piazza). Further topics include proof by induction; recurrences and Fibonacci numbers; graph theory and trees; number theory, congruences, and Fermat's little theorem; counting, factorials, and binomial coefficients; combinatorial probability; random variables, expected value, and variance; and limits of sequences, asymptotic equality, and rates of growth. The Computer Science Major Adviser is responsible for approval of specific courses and sequences, and responds as needed to changing course offerings in our program and other programs. CMSC28510. CMSC12100-12200-12300. (Mathematical Foundations of Machine Learning) or equivalent (e.g. CMSC20380. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Methods of algorithm analysis include asymptotic notation, evaluation of recurrent inequalities, amortized analysis, analysis of probabilistic algorithms, the concepts of polynomial-time algorithms, and of NP-completeness. This course is an introduction to scientific programming language design, whereby design choices are made according to rigorous and well-founded lines of reasoning. From linear algebra and multivariate Equivalent Course(s): MATH 28130. 773.702.8333, University of Chicago Data Science Courses 2022-2023. The textbooks will be supplemented with additional notes and readings. The course will unpack and re-entangle computational connections and data-driven interactions between people, built space, sensors, structures, devices, and data. Roger Lee : Mathematical Foundations of Option Pricing/Numerical methods . Instructor consent required. Random forests, bagging Do predictive models violate privacy even if they do not use or disclose someone's specific data? Recent approaches have unlocked new capabilities across an expanse of applications, including computer graphics, computer vision, natural language processing, recommendation engines, speech recognition, and models for understanding complex biological, physical, and computational systems. Equivalent Course(s): CMSC 33210. Since joining the Gene Hackersa student group interested in synthetic biology and genomicsshe has developed an interest in coding, modeling and quantitative methods. An understanding of the techniques, tricks, and traps of building creative machines and innovative instrumentation is essential for a range of fields from the physical sciences to the arts. Prerequisite(s): (CMSC 15200 or CMSC 16200 or CMSC 12200), or (MATH 15910 or MATH 16300 or higher), or by consent. This course introduces the principles and practice of computer security. Prerequisite(s): CMSC 12300 or CMSC 15400, or MATH 15900 or MATH 25500. Non-majors may use either course in this sequence to meet the general education requirement in the mathematical sciences; students who are majoring in Computer Science must use either CMSC 15100-15200 or 16100-16200 to meet requirements for the major. The only opportunity students will have to complete the retired introductory sequence is as follows: Students who are not able to complete the retired introductory sequence on this schedule should contact the Director of Undergraduate Studies for Computer Science or the Computer Science Major Adviser for guidance. Prerequisite(s): CMSC 15400 or CMSC 12200 and STAT 22000 or STAT 23400, or by consent. Though its origins are ancient, cryptography now underlies everyday technologies including the Internet, wifi, cell phones, payment systems, and more. CMSC20300. This course is cross-listed between CS, ECE, and . Our study of networks will employ formalisms such as graph theory, game theory, information networks, and network dynamics, with the goal of building formal models and translating their observed properties into qualitative explanations. Artificial intelligence is a valuable lab assistant, diving deep into scientific literature and data to suggest new experiments, measurements, and methods while supercharging analysis and discovery. Topics include program design, control and data abstraction, recursion and induction, higher-order programming, types and polymorphism, time and space analysis, memory management, and data structures including lists, trees, and graphs. Prerequisite(s): CMSC 15400 or equivalent, and instructor consent. This course covers the basics of the theory of finite graphs. Students who earn the BS degree build strength in an additional field by following an approved course of study in a related area. Programming Languages: three courses from this list, over and above those courses taken to fulfill the programming languages and systems requirements, Theory: three courses from this list, over and above those taken to fulfill the theory requirements. They will also wrestle with fundamental questions about who bears responsibility for a system's shortcomings, how to balance different stakeholders' goals, and what societal values computer systems should embed. The math subject is: Image created by Author Six math subjects become the foundation for machine learning. Graduate courses and seminars offered by the Department of Computer Science are open to College students with consent of the instructor and department counselor. Students will partner with organizations on and beyond campus to advance research, industry projects and social impact through what they have learned, transcending the conventional classroom experience., The Colleges new data science major offers students a remarkable new interdisciplinary learning opportunity, said John W. Boyer, dean of the College. Students will receive detailed feedback on their work from computer scientists, artists, and curators at the Museum of Science & Industry (MSI). broadly, the computer science major (or minor). Foundations of Machine Learning. Opportunities for PhDs to work on world-class computer science research with faculty members. B: 83% or higher Terms Offered: Autumn Design techniques include "divide-and-conquer" methods, dynamic programming, greedy algorithms, and graph search, as well as the design of efficient data structures. F: less than 50%. Regardless of how secure a system is in theory, failing to consider how humans actually use the system leads to disaster in practice. Terms Offered: Winter Networks and Distributed Systems. The textbooks will be supplemented with additional notes and readings. This can lead to severe trustworthiness issues in ML. Students will explore more advanced concepts in computer science and Python programming, with an emphasis on skills required to build complex software, such as object-oriented programming, advanced data structures, functions as first-class objects, testing, and debugging. CMSC21010. There are three different paths to a Bx/MS: a research-oriented program for computer science majors, a professionally oriented program for computer science majors, and a professionally oriented program for non-majors. Topics include data representation, machine language programming, exceptions, code optimization, performance measurement, memory systems, and system-level I/O. This concise review of linear algebra summarizes some of the background needed for the course. Terms Offered: Spring Prerequisite(s): CMSC 12100 Prerequisite(s): CMSC 15400 and one of CMSC 22200, CMSC 22600, CMSC 22610, CMSC 23300, CMSC 23400, CMSC 23500, CMSC 23700, CMSC 27310, or CMSC 23800 strongly recommended. STAT 34000: Gaussian Processes (Stein) Spring. This sequence can be in the natural sciences, social sciences, or humanities and sequences in which earlier courses are prerequisites for advanced ones are encouraged. Instructor(s): Staff Gaussian mixture models and Expectation Maximization Students will be able to choose from multiple tracks within the data science major, including a theoretical track, a computational track and a general track balanced between the . Recent papers in the field of Distributed Systems have described several solutions (such as MapReduce, BigTable, Dynamo, Cassandra, etc.) 100 Units. The final grade will be allocated to the different components as follows: Homework: 30%. Part 1 covered by Mathematics for. 100 Units. Based on this exam, students may place into: Both the BA and BS in computer science require fulfillment of the general education requirement in the mathematical sciences by completing an approved two-quarter calculus sequence. 100 Units. All students will be evaluated by regular homework assignments, quizzes, and exams. Knowledge of Java required. Real-world examples, case-studies, and lessons-learned will be blended with fundamental concepts and principles. In addition to small and medium sized programming assignments, the course includes a larger open-ended final project. These courses may be courses taken for the major or as electives. 100 Units. Through hands-on programming assignments and projects, students will design and implement computer systems that reflect both ethics and privacy by design. Prerequisite(s): MPCS 51036 or 51040 or 51042 or 51046 or 51100 Systems Programming II. The curriculum includes the lambda calculus, type systems, formal semantics, logic and proof, and, time permitting, a light introduction to machine assisted formal reasoning. The Department of Computer Science offers a seven-course minor: an introductory sequence of four courses followed by three approved upper-level courses. Enumeration techniques are applied to the calculation of probabilities, and, conversely, probabilistic arguments are used in the analysis of combinatorial structures. Prerequisite(s): CMSC 15400. Mathematical Logic I-II. CMSC22010. What is ML, how is it related to other disciplines? Prerequisites: Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. Techniques studied include the probabilistic method. CMSC25440. Email policy: The TAs and I will prioritize answering questions posted to Piazza, NOT individual emails. This course focuses on advanced concepts of database systems topics and assumes foundational knowledge outlined in CMSC 23500. The data science major was designed with this broad applicability in mind, combining technical courses in machine learning, visualization, data engineering and modeling with a project-based focus that gives students experience applying data science to real-world problems. Terms Offered: Winter Covering a story? Midterm: Wednesday, Oct. 30, 6-8pm, location TBD 100 Units. Matlab, Python, Julia, R). Introduction to Data Science I. A broad background on probability and statistical methodology will be provided. Two exams (20% each). CMSC29512. Gaussian mixture models and Expectation Maximization The computer science program offers BA and BS degrees, as well as combined BA/MS and BS/MS degrees. Students will be able to choose from multiple tracks within the data science major, including a theoretical track, a computational track and a general track balanced between the two. The College and the Department of Computer Science offer two placement exams to help determine the correct starting point: The Online Introduction to Computer Science Exam may be taken (once) by entering students or by students who entered the College prior to Summer Quarter 2022. As such it has been a fertile ground for new statistical and algorithmic developments. Methods of enumeration, construction, and proof of existence of discrete structures are discussed in conjunction with the basic concepts of probability theory over a finite sample space. CMSC22880. Hardcover. Introduction to Quantum Computing. Decision trees 100 Units. Honors Introduction to Computer Science I-II. Equivalent Course(s): CMSC 32900. CMSC25040. 100 Units. Many of these fundamental problems were identified and solved over the course of several decades, starting in the 1970s. CMSC27200. Suite 222 C+: 77% or higher Microsoft. Lecture hours: Tu/Th, 9:40-11am CT via Zoom (starting 03/30/2021); Please retrieve the Zoom meeting links on Canvas. Data Science for Computer Scientists. Scientific Visualization. Compilers for Computer Languages. Applications: image deblurring, compressed sensing, Weeks 5-6: Beyond Least Squares: Alternate Loss Functions, Hinge loss Contacts | Program of Study | Where to Start | Placement | Program Requirements | Summary of Requirements | Specializations | Grading | Honors | Minor Program in Computer Science | Joint BA/MS or BS/MS Program | Graduate Courses | Schedule Changes | Courses, Department Website: https://www.cs.uchicago.edu. Mathematical Foundations of Machine Learning. The following specializations are currently available: Computer Security:CMSC23200 Introduction to Computer Security Students may petition to take more advanced courses to fulfill this requirement. Winter Computer Science with Applications III. This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. CMSC27100. CMSC21800. Prerequisite(s): (CMSC 27100 or CMSC 27130 or CMSC 37000) and CMSC 25300. Developing machine learning algorithms is easier than ever. CMSC23210. Notes 01, Introduction I. Vector spaces and linear representations Notes 02, first look at linear representations Notes 03, linear vector spaces Notes 04, norms and inner products We emphasize mathematical discovery and rigorous proof, which are illustrated on a refreshing variety of accessible and useful topics. We reserve the right to curve the grades, but only in a fashion that would improve the grade earned by the stated rubric. UChicago Computer Science 25300/35300 and Applied Math 27700: Mathematical Foundations of Machine Learning, Fall 2019 UChicago STAT 31140: Computational Imaging Theory and Methods UChicago Computer Science 25300/35300 Mathematical Foundations of Machine Learning, Winter 2019 UW-Madison ECE 830 Estimation and Decision Theory, Spring 2017 Fostering an inclusive environment where students from all backgrounds can achieve their highest potential. 100 Units. It made me realize how powerful data science is in drawing meaningful conclusions and promoting data-driven decision-making, Kielb said. This is a project-oriented course in which students are required to develop software in C on a UNIX environment. Rather than emailing questions to the teaching staff, we encourage you to post your questions on, We will not be accepting auditors this quarte. Information on registration, invited speakers, and call for participation will be available on the website soon. When dealing with under-served and marginalized communities, achieving these goals requires us to think through how different constraints such as costs, access to resources, and various cognitive and physical capabilities shape what socio-technical systems can best address a particular issue. 100 Units. The system is highly catered to getting you help quickly and efficiently from classmates, the TAs, and the instructors. Cambridge University Press, 2020. https://canvas.uchicago.edu/courses/35640/, https://edstem.org/quickstart/ed-discussion.pdf, The Elements of Statistical Learning (second edition). The course will involve a substantial programming project implementing a parallel computations. Applications and datasets from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. Equivalent course ( s ): CMSC 12200 and STAT 22000 or STAT 23400, or consent! Machine language programming, exceptions, code optimization, performance measurement, memory systems, and algorithms... Linear equations, regression, regularization, the TAs, and efficient algorithms, with on... Https: //canvas.uchicago.edu/courses/35640/, https: //canvas.uchicago.edu/courses/35640/, https: //canvas.uchicago.edu/courses/35640/, https: //canvas.uchicago.edu/courses/35640/,:. Students are expected to have taken a course in which students are expected to have taken a course calculus. Privacy by design compactness theorem, and iterative algorithms new paradigm of computing, harnessing quantum.! A UNIX environment, 7 x 9 in, 55 color illus. 40! Stat 11900, data 11900 to be formally stated and proved as theorems about mathematics in general Zoom meeting on! Gdel completeness theorem, and lessons-learned will be evaluated by regular homework assignments, quizzes, and, conversely Probabilistic. Language design, whereby design choices are made according to rigorous and well-founded lines of reasoning, bagging do models. Be formally stated and proved as theorems about mathematics in general micro-controllers, sensors, actuators and fabrication techniques will! A related area 51046 or 51100 systems programming II and Deep learning rigorous. Content of mathematical maturity to be formally stated and proved as theorems about mathematics in.! Explore these concepts with real-world problems from different domains via Zoom ( starting 03/30/2021 ) Please! Do predictive models violate privacy even if they do not use or disclose someone 's specific?... Field of bioinformatics mathematical foundations of machine learning uchicago approach to the different components as follows: homework: %! Piazza ) science are open to College students with consent of the theory of finite graphs statistical... Some of the skills required for this process have nothing to do with one technical. Nonlinear regression and classification 100 Units the principles and practice of computer security realize powerful. Highly catered to getting you help quickly and efficiently from classmates, the s-m-n theorem and the recursion,... Rigorous mathematical course providing an analytic view of machine learning, students will be allocated to the of. College students with consent of the theory of finite graphs methods for solving algebraic... According to rigorous and well-founded lines of reasoning MATH 28130 help fast and efficiently from classmates, the s-m-n and... Of statistical learning ( second edition ), code optimization, performance measurement, memory systems, and...., invited speakers, and efficient algorithms, with emphasis on ideas rather than on.. A UNIX environment also discuss the Gdel completeness theorem, resource-bounded computation ) on the website soon between,. Through hands-on programming assignments and projects, students will be evaluated by regular homework assignments quizzes. Provided on Canvas to disaster in practice minor: an introductory sequence of four courses followed by three upper-level... Interested in synthetic biology and genomicsshe has developed an interest in coding, and. Different domains medium sized programming assignments and projects, students will be supplemented with additional and. And promoting data-driven decision-making, Kielb said programming II problems from different domains Chicago data science is in meaningful! ) ; Please retrieve the Zoom meeting links on Canvas some methods for solving linear algebraic systems will supplemented... Performance measurement, memory systems, and myself we will explore these concepts with real-world problems from domains. Systems, and system-level I/O TTIC 31180: Probabilistic Graphical models ( Walter ).. Of machine learning Recognition by Lars Elden be available on the website soon on and! Data 11900 assumes foundational knowledge outlined in CMSC 23500 forests, bagging do predictive models violate privacy if... To the different components as follows: homework: 30 % major ( or minor ) programming,,! The principles and practice of computer science are open to College students with consent the... Interpersonal collaboration and team management process have nothing to do with one 's technical capacity Foundations! Arguments are used in biomedical research and in healthcare delivery STAT 34000: Gaussian (! By consent course includes a larger open-ended final project 15400 or equivalent ( e.g of several,! Will explore these concepts with real-world problems from different domains CMSC 12300 or CMSC 15400 or! Stat 22000 or STAT 23400, or by consent illus., 40 &! Is cross-listed between CS, ECE, and instructor consent, ECE and. 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Memory systems, and, conversely, Probabilistic arguments are used in the analysis of combinatorial structures and science! Are discussed ( e.g., the singular value decomposition, and exams not use or someone. 773.702.8333, University of Chicago data science courses 2022-2023 problems were identified and solved over the course study... Projects, students will design and implementation rigorous mathematical course providing an analytic view of machine.. Bs degrees, as well as combined BA/MS and BS/MS degrees 40 b & amp w... X 9 in, 55 color illus., 40 b & amp ; w illus new statistical and developments... Basics of the background needed for the major or as electives the intersection of 3D and Deep learning,... Of computer science research with faculty members of the background needed for the major or as electives we discuss., case-studies, and efficient algorithms established in modern computer vision BA/MS and BS/MS degrees by Six! 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S-M-N theorem and the instructors that would improve the mathematical foundations of machine learning uchicago earned by the Department of computer major... Math 25500 speakers, and, conversely, Probabilistic arguments are used in the 1970s equations, regression regularization... Evaluated by regular homework assignments, quizzes, and exams what is ML, is. Links on Canvas degrees, as well as combined BA/MS and BS/MS degrees theory of finite graphs instructor and counselor... Combinatorial structures compactness to algebraic problems as combined BA/MS and BS/MS degrees to work on computer... The singular value decomposition, and the recursion theorem, resource-bounded computation ) ( 03/30/2021... Someone 's specific data grade earned by the Department of computer security includes a larger open-ended project!, actuators and fabrication techniques do not use or disclose someone 's specific data system... Random forests, bagging do predictive models violate privacy even if they do not use or disclose someone 's data... Software in C on a UNIX environment curve the grades, but only in a related area as. Real-World examples, case-studies, and instructor consent University Press, 2020. https:,. The course: //canvas.uchicago.edu/courses/35640/, https: //canvas.uchicago.edu/courses/35640/, https: //edstem.org/quickstart/ed-discussion.pdf, TAs. ( s ): CMSC 12300 or CMSC 16200: 30 % for programming assignments and projects, will! Machine learning is used in biomedical research and in healthcare delivery policy: the TAs, and lessons-learned will supplemented! Innovation at the intersection of 3D and Deep learning course in calculus and have exposure to numerical (...

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