Join the virtual competition on AI for COVID diagnosis, thanks to Microsoft Canada, the exclusive technology and cloud platform sponsor! The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has generated an unprecedented global health crisis, with more than 2.7 million deaths worldwide. Do you want to contribute to the fight against this pandemic? IEEE SIGHT (Special Interest Group on Humanitarian Technology) of Montreal Section, Vision and Image Processing Research Group of the University of Waterloo and DarwinAI Corp. invite data scientists, students and professionals working on Artificial Intelligence (AI) to participate in a virtual competition to help medical researchers diagnose COVID-19 with chest X-ray (CXR) images. The ultimate goal is to contribute to the development of highly accurate yet practical AI solutions for detecting COVID-19 cases and, hopefully, accelerating the treatment of those who need it the most. Moreover, this AI for Good initiative will also allow us to take action on at least one of the United Nations Sustainable Development Goals (SDGs), Good Health and Well-being. In the First Phase of the competition, the challenge consists of designing robust machine learning algorithms to predict if the subjects of study are either COVID-19 positive or COVID-19 negative. The dataset for this competition is the dataset curated by COVID-Net, a global open-source initiative launched by DarwinAI Corp., Canada, and Vision and Image Processing Research Group, University of Waterloo, Canada, for accelerating advancements in machine learning to aid healthcare workers around the world in the fight against the COVID-19 pandemic. More about the COVID-Net initiative and available open-source resources are available here. In the Second Phase, the 10 top teams of the first phase will have the opportunity to refine their solution and submit a proposal for a follow-up project to positively impact society or the academic community. This competition is organized in collaboration with the National Research Council Canada and is co-hosted by the IEEE Young Professionals Affinity Groups of Montreal, Ottawa, Toronto and Vancouver Sections, Vancouver Circuit and Systems (CAS) Technical Chapter, the Student Branches of INRS (Institut National de la Recherche Scientifique), University of Toronto and Vancouver Simon Fraser University, WIE (Women In Engineering) Ottawa. It is largely sponsored by Microsoft, and partially by the IEEE Canada Humanitarian Initiatives Committee and the IEEE Montreal Section. How to participate Note: This competition only accepts participants living in Canada, due to restrictions on funds transfer. NO PURCHASE NECESSARY TO ENTER OR WIN. The competition is hosted on the Eval.ai online platform. To participate, you or your team will need to perform the following steps: Register individually at the link provided below in the current webpage (vTools). Register yourself or your team at the link on Eval.ai: https://eval.ai/web/challenges/challenge-page/925/participate. Follow the instructions here: https://evalai.readthedocs.io/en/latest/participate.html#. Download the dataset from https://www.kaggle.com/andyczhao/covidx-cxr2. Design an AI algorithm that gets CXR images as inputs and predicts the labels of the images in the output (COVID or non-COVID). Train your AI algorithm using the training dataset. Submit your AI algorithm through Eval.ai for evaluation against the test dataset for the competition. Prizes For the First Phase, the first five best solutions will be awarded monetary prizes and Azure credits: First place: 1,000 CAD + 500 CAD in Azure. Second place: 800 CAD + 300 CAD in Azure. Third place: 600 CAD + 300 CAD in Azure. Fourth place: 400 CAD + 300 CAD in Azure. Fifth place: 300 CAD + 300 CAD in Azure. The top 10 teams on the leaderboard will also have the following opportunities: Participate in the 2nd phase to refine their solution and receive funding for a project. Write a scientific paper with the Vision and Image Processing Research Group, from the University of Waterloo, to explain their approach. For the Second Phase, the best three projects can receive funds up to the following amounts: Project 1: 5,000 CAD. Project 2: 5,000 CAD. Project 3: 4,000 CAD. Term of funding: Up to 4 months following the announcement of the selected teams. The deadline is December 31st, 2021. For more information, visit IEEE SIGHT Montreal website.
Young Professionals
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1 event,This is an introduction to Python programming for students without any prior programming knowledge or experience. The proposed 5-day course covers the fundamental aspects of programming, which include data types, various operators, input/output, conditions, control flow, functions, and algorithms. The learning experience is enhanced by a number of examples and problem sets (data, strings, file processing and simple graphics) that will be solved interactively during the lecture with the participation of the students. The course format includes 3 hours of daily lectures. Course Objective: Attendees will gain a solid understanding of principles of programing using Python; they can progress to more advanced programming topics and explores algorithms that are integral parts of more sophisticated methodologies, e.g., Artificial Intelligence. Attendees will have the knowledge to write various Python programs, and to design algorithms manipulating files and different types of data including numbers, and text. Note: This course is designed to be offered online, and it requires the attendees to use their personal computers/laptops. Details to Join in will be forwarded to Registered Attendees Who should attend: Students, second career trainees, engineers, scientists, clinicians, and in general specialists in variety of non-STEM fields. What will you receive after completion: A certificate of completion will be given to the students who successfully complete the course and pass a short exam. Electronic copies of the course materials. Attendees will also be provided with career, and skills development advice. Speaker Dr. Alireza Sadeghian Dr. Alireza Sadeghian has been with the Department of Computer Science at Ryerson University since 1999, where he holds the position of the Professor. He is also an Affiliate Scientist at the Li Ka Shing Knowledge Institute, St. Michael's Hospital, and serves as the AI research Theme Lead in Healthcare and Analytics at the Institute for Biomedical Engineering, Science, and Technology. Dr. Sadeghian was the Chair of the Department of Computer Science from 2005 to 2015. He is the founding Director of the Advanced Artificial Intelligence Initiative (AI2) Laboratory and has extensive expertise in the areas of AI, machine learning, and Deep Learning particularly related to industrial and medical applications. He has supervised 9 postdoctoral fellows, 8 PhD, and 24 Master’s students, as well as 60 research assistants. He has published over 150 journal manuscripts, refereed conference papers, and book chapters, as well as two edited books. He has 2 invention disclosures and 2 patents. Dr. Sadeghian has been actively involved with a number of international professional and academic boards including IEEE Education Activity Board. Presently, he is the Chair of IEEE Computational Intelligence Technical Society Chapter, Toronto Section. Dr. Sadeghian is also on the Editorial Board of Applied Soft Computing Journal and serves as an Associate Editor of IEEE Access, Information Sciences, and Expert Systems Journal. Email: dr.alireza.sadeghian@ieee.org Agenda Day 1 – June 7, 2021, 6:00-9:00 pm: Introduction to computer systems, hardware architecture, CPU, memory, compilation, high level vs. low-level programming language, data representation, Python and PyCharm interactive IDE installation, writing/editing/saving/retrieving and running a simple program, basic data types, variables, assignments, comments, and expressions. The material learned will be reinforced through examples provided during the lecture. Day 2 – June 8, 2021, 6:00-9:00 pm: The following topics will be discussed: conditions, operators (arithmetic, logic, and comparison), control statements (if and if-else), and loops (for and while). The material learned will be reinforced through examples provided during the lecture. Day 3 – June 9, 2021, 6:00-9:00 pm: Students will be introduced to Strings and text files in Python. They will learn how to work with files, reading/writing text and numbers from/to a file, string manipulation, indexing, and string slicing. The material learned will be reinforced through examples provided during the lecture. Day 4 – June 10, 2021, 6:00-9:00 pm: Functions, arguments, and return values will be discussed. The material learned will be reinforced through examples provided during the lecture. Day 5 – June 11, 2021, 6:00-9:00 pm: The topics of lists and dictionaries will be discussed. Students will learn about the basic operators, creating, accessing, slicing, adding, removing, replacing, and iteration methods for lists and dictionaries. The material learned will be reinforced through examples provided during the lecture. |
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Virtual platform will be delivered to registrants a couple of hours before starting the event. Contact: IEEE Montreal Young Professionals Abstract: Rate Splitting Multiple Access (RSMA), based on (linearly or nonlinearly) precoded Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers, has emerged as a novel, general and powerful framework for the design and optimization of non-orthogonal transmission, multiple access, and interference management strategies in future MIMO wireless networks. RSMA relies on the split of messages and the non-orthogonal transmission of common messages decoded by multiple users, and private messages decoded by their corresponding users. This enables RSMA to softly bridge and therefore reconcile the two extreme strategies of fully decode interference and treat interference as noise. RSMA has been shown to generalize, and subsume as special cases, four seemingly different strategies, namely Space Division Multiple Access (SDMA) based on linear precoding (currently used in 5G), Orthogonal Multiple Access (OMA), Non-Orthogonal Multiple Access (NOMA) based on linearly precoded superposition coding with SIC, and physical-layer multicasting. RSMA boils down to those strategies in some specific conditions, but outperforms them all in general. Through information and communication theoretic analysis, RSMA is shown to be optimal (from a Degrees-of-Freedom region perspective) in a number of scenarios and provides significant room for spectral efficiency, energy efficiency, fairness, reliability, QoS enhancements in a wide range of network loads and user deployments, robustness against imperfect Channel State Information at the Transmitter (CSIT), as well as feedback overhead and complexity reduction over conventional strategies used in 5G. The benefits of RSMA have been demonstrated in a wide range of scenarios (MU-MIMO, massive MIMO, multi-cell MIMO/CoMP, overloaded systems, NOMA, multigroup multicasting, mmwave communications, communications in the presence of RF impairments and superimposed unicast and multicast transmission, relay,…) and systems (terrestrial, cellular, satellite, …). Thanks to its versatility, RSMA has the potential to tackle challenges of modern communication systems and is a gold mine of research problems for academia and industry, spanning fundamental limits, optimization, PHY and MAC layers, and standardization. This lecture will share key principles of RSMA, recent developments, emerging applications and opportunities of RSMA for 6G networks and will cover many of the topics currently investigated as part of the new IEEE special interest group on RSMA https://sites.google.com/view/ieee-comsoc-wtc-sig-rsma/home. Speaker(s): Bruno Clerckx Biography: Bruno Clerckx is a (Full) Professor, the Head of the Wireless Communications and Signal Processing Lab, and the Deputy Head of the Communications and Signal Processing Group, within the Electrical and Electronic Engineering Department, Imperial College London, London, U.K. He received the M.S. and Ph.D. degrees in applied science from the Université Catholique de Louvain, Louvain-la-Neuve, Belgium, in 2000 and 2005, respectively. From 2006 to 2011, he was with Samsung Electronics, Suwon, South Korea, where he actively contributed to 4G (3GPP LTE/LTE-A and IEEE 802.16m) and acted as the Rapporteur for the 3GPP Coordinated Multi-Point (CoMP) Study Item. Since 2011, he has been with Imperial College London, first as a Lecturer from 2011 to 2015, Senior Lecturer from 2015 to 2017, Reader from 2017 to 2020, and now as a Full Professor. From 2014 to 2016, he also was an Associate Professor with Korea University, Seoul, South Korea. He also held various long or short-term visiting research appointments at Stanford University, EURECOM, National University of Singapore, The University of Hong Kong, Princeton University, The University of Edinburgh, The University of New South Wales, and Tsinghua University. |
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