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Oxford ranked first in the world for Computer Science

Oxford Comp Sci

For the second year running the University of Oxford has been ranked first in the world for Computer Science in the Times Higher Education 2022 World University Rankings. The University of Cambridge and Switzerland's EHT Zurich claimed joint fourth. Prof. Leslie Ann Goldberg, head of Computer Science, said'I'm very pleased with this continuing recognition of the outstanding teaching and research taking place in our department.' The Department is one of the longest-established Computer Science departments in the world and is the home to a community of world-class research and teaching. Research activities encompass core Computer Science (algorithms, data, programming languages, and artificial intelligence), as well as human-centred computing, automated verification, computational biology, cyber physical systems, quantum computing and security.


Royal Air Force is testing self-driving cars in Oxfordshire

Daily Mail - Science & tech

The Royal Air Force is testing its own autonomous vehicle to deliver supplies around a base in Oxfordshire to'free up personnel from mundane tasks'. Its specially-designed self-driving car, called Kar-Go, is a zero-emissions delivery vehicle capable of travelling at speeds of up to 60 miles/hour. It's been zipping around the Royal Air Force base of Brize Norton in Oxfordshire, delivering tools, equipment and supplies to personnel as part of a trial. When arriving at its destination on the base, RAF personnel meet Kar-Go and a hatch is automatically released enabling them to collect the cargo. Slightly odd in appearance, Kar-Go looks a bit like a gigantic green computer mouse with protruding wheels, complete with flashing lights and a spacious boot.


Researchers Develop AI Algorithm To Diagnose Deep Vein Thrombois

#artificialintelligence

According to the Centers for Disease Control and Prevention, the number of people who die from deep vein thrombosis (DVT), ten to 30 percent of people will die within one month of diagnosis. The CDC estimates that around 60,000 to 100,000 Americans die of DVT each year. Researchers from the University of Oxford say they have developed an artificial intelligence (AI) algorithm to help diagnose DVT faster and more efficiently than a traditional radiologist-interpreted diagnostic scan. Working with researchers at the University of Sheffield and UK startup, ThinkSono, the collaborative team trained a machine learning AI algorithm called AutoDVT to differentiate patients with DVT from those who did not. The researchers believe this rapid diagnosis could reduce long patient waiting lists and unnecessary prescriptions to treat DVT when the patients do not have DVT.


Marta Kwiatkowska and Susan Murphy win Van Wijngaarden Awards 2021 for preventing software faults and for improving decision making in health

Oxford Comp Sci

The Van Wijngaarden Awards 2021 are awarded to computer scientist Marta Kwiatkowska and mathematician Susan A. Murphy for the numerous and highly significant contributions they made to their respective research areas: preventing software faults and improving decision making in health. The five-yearly award is established by CWI, the national research institute for mathematics and computer science in the Netherlands, and is named after former CWI director Aad van Wijngaarden. The winners receive the prize during a festive soirée on 18 November in Amsterdam. Marta Kwiatkowska (University of Oxford) is a computer scientist who pioneered research on modelling, verification, and synthesis of probabilistic systems. She led the development of the highly influential PRISM probabilistic model checker, which is widely used for research and teaching and which has been downloaded over 80,000 times. In her research Kwiatkowska showed the relevance of PRISM by applying it in several areas, including ubiquitous computing, system biology, DNA computing, and most recently, safety for AI.


Artificial Intelligence Screens for COVID-19 26% Faster Than Lateral Flow Tests

#artificialintelligence

This article is based on research findings that are yet to be peer-reviewed. Results are therefore regarded as preliminary and should be interpreted as such. Find out about the role of the peer review process in research here. For further information, please contact the cited source. As society transitions to "living with COVID-19", having access to both efficient and accurate screening tools is integral.


Machine learning algorithm to diagnose deep vein thrombosis

#artificialintelligence

A team of researchers are developing the use of an artificial intelligence (AI) algorithm with the aim of diagnosing deep vein thrombosis (DVT) more quickly and as effectively as traditional radiologist-interpreted diagnostic scans, potentially cutting down long patient waiting lists and avoiding patients unnecessarily receiving drugs to treat DVT when they don't have it. DVT is a type of blood clot most commonly formed in the leg, causing swelling, pain and discomfort--if left untreated, it can lead to fatal blood clots in the lungs. Researchers at Oxford University, Imperial College and the University of Sheffield collaborated with the tech company ThinkSono (which is led by Fouad Al-Noor and Sven Mischkewitz), to train a machine learning AI algorithm (AutoDVT) to distinguish patients who had DVT from those without DVT. The AI algorithm accurately diagnosed DVT when compared to the gold standard ultrasound scan, and the team worked out that using the algorithm could potentially save health services $150 per examination. "Traditionally, DVT diagnoses need a specialist ultrasound scan performed by a trained radiographer, and we have found that the preliminary data using the AI algorithm coupled to a hand-held ultrasound machine shows promising results," said study lead Dr. Nicola Curry, a researcher at Oxford University's Radcliffe Department of Medicine and clinician at Oxford University Hospitals NHS Foundation Trust.


Artificial Intelligence Screens for COVID-19 26% Faster Than Lateral Flow Tests

#artificialintelligence

This article is based on research findings that are yet to be peer-reviewed. Results are therefore regarded as preliminary and should be interpreted as such. Find out about the role of the peer review process in research here. For further information, please contact the cited source. As society transitions to "living with COVID-19", having access to both efficient and accurate screening tools is integral.


New AI technology screens for COVID faster than lateral flow tests

#artificialintelligence

Researchers at the University of Oxford are seeking NHSX funding for an artificial intelligence (AI) COVID screening test. Results of a three-month evaluation study at John Radcliffe Hospital found the CURIAL-Rapide test could screen emergency department (ED) patients at the bedside within 10 minutes, without needing a laboratory. Results were available 45 minutes after patients arrived at the ED – 26% faster than with lateral flow tests (LFTs). When compared against PCR testing, the AI test was more likely to identify COVID patients than LFTs and corrected ruled out the infection 99.7% of the time. Collaborating with University Hospitals Birmingham NHS Foundation Trust, Portsmouth University Hospitals NHS Trust, and Bedfordshire Hospitals NHS Foundation trust, the study found CURIAL-Rapide performed consistently across 72,000 admissions to five UK hospitals. Another AI model named CURIAL-Lab, which uses routine blood tests performed in a laboratory alongside vital signs, was at least as effective as CURIAL-Rapide when tested at hospitals.


AI detects Covid 26% faster than lateral flow tests

#artificialintelligence

Artificial intelligence can enable busy NHS emergency departments to perform bedside checks for Covid-19 in just 10 minutes without the need for a laboratory, a study led by Oxford University shows. During a three-month evaluation at John Radcliffe Hospital, Oxford's main accident and emergency centre, the study found that AI test results were available 45 minutes after a patient arrived, 26% faster those for a lateral flow test. The AI screening test, known as CURIAL-Rapide, uses routine healthcare data (blood tests and vital signs) to screen patients for Covid-19. Compared to lateral flow tests, the AI test was more likely to identify Covid-19 in patients and correctly ruled out the infection 99.7% of the time, the research found. In addition, a collaboration with five NHS trusts between December 2020 and March 2021 – University Hospitals Birmingham, Portsmouth University and Bedfordshire Hospitals – the study found that the AI test performed consistently in 72,000 admissions. It provided reliable negative results for uninfected patients up to 98.8% of the time and was 21% more effective at identifying Covid-19 positive patients than lateral flow tests.


#IJCAI2021 invited talks round-up 1: fairness in multiwinner voting, and combining AI and robotics to augment human abilities

AIHub

There is an exciting, and varied, programme of eight invited talks at the International Joint Conference on Artificial Intelligence (IJCAI-21) this year. On the opening day of the conference, we heard presentations from Edith Elkind (University of Oxford), who talked about fairness in multiwinner voting, and Masahiro Fujita (SonyAI) who discussed combining AI and robotics for augmenting human abilities. Edith works in algorithmic game theory, with a focus on algorithms for collective decision making and coalition formation. She began by giving a brief overview of the field of computational social choice. This area of research, at the interface of social choice theory and computer science, really began in earnest following COMSOC '06, the first International Workshop on Computational Social Choice.