Recently, a clinical study led by Finnish researchers was launched in Tampere to use the latest analytical methods to recognize those myocardial infarction patients at high risk of complications. The project makes comprehensive use of data generated during treatment, but which is usually fragmented into separate systems, and complements it by continuing to monitor how the patient's heart is functioning after he or she has been discharged from hospital. The mass of data thus gathered is analysed using A.I. and machine-learning methods, which have been taught with the help of former patient-treatment data and developed to be applied to myocardial infarction patients. What the study means for the patients in practice is that a small ECG recorder is attached to their chest when they are leaving the hospital. It can also be linked to the Internet for monitoring purposes for as long as the measurements require.
The European Robotics Forum 2018 (ERF2018), the most influential meeting of the robotics community in Europe, takes place in Tampere on 13-15 March 2018. ERF brings together over 900 leading scientists, companies, and policymakers for the largest robotics networking event in Europe. Under the theme "Robots and Us", the over 50 workshops cover current societal and technical themes, including human-robot-collaboration and how robotics can improve industrial productivity and service sector operations. During the opening the ERF2018, on 13 March, Juha Heikkilä, Head of unit, EC DG CNECT, explained that "the European Robotics Forum has been instrumental in breaking down silos and bringing together a strong, integrated robotics community in Europe. This year's theme, "Robots and Us", reflects the increasingly broad impact of robotics and allows discussing not just technology but also the all-important non-technological aspects of robotics."
We present the Video Ladder Network (VLN) for efficiently generating future video frames. VLN is a neural encoder-decoder model augmented at all layers by both recurrent and feedforward lateral connections. At each layer, these connections form a lateral recurrent residual block, where the feedforward connection represents a skip connection and the recurrent connection represents the residual. Thanks to the recurrent connections, the decoder can exploit temporal summaries generated from all layers of the encoder. This way, the top layer is relieved from the pressure of modeling lower-level spatial and temporal details. Furthermore, we extend the basic version of VLN to incorporate ResNet-style residual blocks in the encoder and decoder, which help improving the prediction results. VLN is trained in self-supervised regime on the Moving MNIST dataset, achieving competitive results while having very simple structure and providing fast inference.
Transport safety agency Trafi has granted a handful of permits to three research and development institutions to test automatic cars in Finland. The agency handed over permits to Helsinki's Metropolia University of Applied Sciences and the state-owned R&D organisation VTT to begin testing the driverless cars in the capital region between July and August. Finnish IT services company Tieto will receive a license to test its automatic vehicles in August. Other trials will begin in Tampere in September. The buses were previously taken out for trial runs during the Vantaa housing fair last year.
The Center for Visual and Decision Informatics (CVDI), a leader in big data science and analytics, is poised to become the largest center of its kind in the U.S. after recently receiving approval for a second round of National Science Foundation funding. The Center works with government, industry and academia to develop next-generation tools and techniques that help decision makers improve how they analyze and interpret large volumes of data. The University of Louisiana at Lafayette and Drexel University established CVDI in 2012 as an NSF Industry University Cooperative Research Center. CVDI is the only such NSF Center in the nation that focuses on data science, big data analytics and visual analytics. Over the next five years, CVDI will consist of seven universities and 40 industry partners and will generate more than $12 million in funding.