KU Leuven KU Leuven in Short KU Leuven - History
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WELCOME Prof. Paul Leroux KU Leuven KU Leuven in short KU Leuven - history . KU Leuven founded in 1425 . Oldest Catholic University in the world 2008 6 university colleges of the KU Leuven 2013 Association sign an agreement to join their KU Leuven expands to include academic educational programmes in the Associated degree programmes hosted at university Faculty of Engineering Technology and colleges within KU Leuven Association Bioscience Engineering Eminent scholars and scientists KU Leuven expands across Flanders KU Leuven in 10 locations spread over 14 campuses Leuven Sint-Lucas Campus, Ghent Group T Campus, Leuven Sint-Lucas Campus, Brussels De Nayer Campus, Sint-Katelijne Waver Brussels Campus Geel Campus Ghent, Technology Campus Carolus Campus, Antwerp Bruges Campus Sint-Andries Campus, Antwerp Kulak Campus, Kortrijk Aalst Campus Diepenbeek Campus* * The degree programme in Diepenbeek is jointly offered by Hasselt University and KU Leuven. Mission Excellence in academic education Excellence in research Distinguished service to society Programmes 78 Bachelors’s programmes 205 Master’s programmes 44 advanced Master’s programmes Characteristics Distinctive vision of education and learning Culture of quality Innovative learning environment Flexibility Internationalisation Extensive range of education facilities Figures: 2017-2018 academic year Enrolment statistics Total number of students: 56,842 Bachelor 44.5% Initial Master 31.8% Advanced Master 4.8% Doctoral Programme 10,3% Academic Teacher Training 0.6% Other 8.0% Largest student populations: Business & Economics Medicine Engineering Technology Law Arts 14% first-time students 49% female students Figures: June 2018 International students OVER 150 NATIONALITIES 10,239 international students 18% international students Top 5: EU Netherlands 1,926 Italy 728 Spain 568 Germany 479 Greece 266 Top 5: non-EU China 927 India 511 United States 366 Turkey 235 Iran 196 Figures: June 2018 Research excellence KU LEUVEN: A LEADING EUROPEAN RESEARCH UNIVERSITY Top 10 HEI institutions with regard to number of projects (label = budget in million €) number of signed projects, eCORDA H2020 database, September 30, 2017 0 50 100 150 200 250 300 350 400 UNIVERSITY OF CAMBRIDGE 217 UNIVERSITY OF OXFORD 223 UNIVERSITY COLLEGE LONDON 210 KOBENHAVNS UNIVERSITET 139 IMPERIAL COLLEGE 150 KU LEUVEN 132 TECHNISCHE UNIVERSITEIT DELFT 134 UNIVERSITY OF EDINBURGH 132 ETH Zürich 103 EPFL 125 Total EC Contribution: €132 million Source: signed grants, eCORDA H2020 database, February 2018 Number of projects: 238 Europe’s most innovative university KU Leuven is named Europe’s most innovative university by Reuters. In this study, Reuters aims to identify which institutions contribute the most to science and technology, and have the greatest impact on the global economy. Reuters top 100: Europe’s Most Innovative Universities Reuters’ study is based on: » Number of publications 1. KU Leuven » Patent application (number 2. Imperial College London of applications, patents 3. University of Cambridge granted, ...) 4. École Polytechnique Fédérale de Lausanne » Number of citations of 5. Technical University of Munich patents and publications 6. University of Erlangen-Nuremberg (in other patents and 7. Delft University of Technology publications) 8. University of Oxford » Number of industrial 9. University of Munich cooperations 10. University of Zurich »... A highly ranked university #5 #47 in the Reuters World Ranking of Most in the Times Higher Education Innovative Universities (2017); World University Ranking (2018) the highest-ranked European university #6 The sixth university in the European #81 Commission Horizon 2020 programme in the QS World University Ranking #11 The 11th university in the ERC grants (2019) programme with 72 projects (as host institution) Spin-off companies As of late 2017 124 spin-off companies 6,700 direct jobs 7 IPO’s (Initial Public Offering) Cummulative number of spin-offs created Figures: 2017 Figures: 14 Advanced Integrated Sensing Lab (ADVISE) ADVISE at KU Leuven, Geel Campus ADVISE - people • 3 professors • 3 full-time lecturers • 3 post-doctoral researchers • > 20 PhD students Harsh radiation environment electronics Application fields Expertise • RF, analog and mixed-signal radhard IC design • Modelling radiation effects ITER: Nuclear fusion CERN: CMS experiment Space missions • FPGA and board level Reactor safety SCK-CEN: prototyping MYRRHA Robotic decommissioning • Radiation hardened digital IC design & synthesis Accident intervention ADVISE RELY LAB facilities X-ray total dose testing Climate chamber Two-photon absorption laser radiation test system H2020 ITN RADSAGA project (2017-2021) http://radsaga.web.cern.ch/ Research team: ADVISE Machine learning Monitoring applications Expertise Epileptic seizure Radar-based life-sign Data set generation (acquisition sys. development) detection monitoring Structural health monitoring Radar-based fall detection Modelling complex signals using semi- and unsupervised (deep) machine learning strategies Anomaly detection in nuclear waste barrels Domestic activity monitoring DistributedFigure 4: Neural Netwo rkmachinearchitecture of the ba selearningline systemconsisting underof two 1D conv ollimitingutional layers and o ne fully connected layer circumstancesand the amount of full sessions of a certai n activity (e.g. a cooking from 50 tot 8000 Hz. An overview of the Neural Network archi- sessi on). Compared to the SINS dataset, we have combined the tecture isshown in Figure 4. It uses an input size of 40x501 which activity ”Phone call” and ”Visit” into one activity named ”Social ar e thelog-mel frames in a total durat ion of 10 s. Thefirst convolu- activity”. The class ”Absence” is ref erred to as not bei ng present tional layer has 32 filters with a kernel size of 40x5 with a stride of in the room. However, the data does contain recordings when a one, so therefore convolution is only performed over the timeaxis. person ispresent in another room. Depending on the activity,thisis Subsampling is then performed by Max Pooling by a factor 5. The noticable in the recording. The class”Other” is referred to as bei ng resulting featuremap of 32x99 isthen provided to asecond convolu- present in the room but not doing any another activity in the list. tional layer that has 64 filterswith akernel sizeof 32x3 and astride All the other activities are self-explanatory. Note that thedataset is of one. Subsequentially, this is subsampled using Max Pooling by unbalanced. afactor 3. After each convolutional layer Batch Normalization and Theevaluation dataset isacquired in asimilar manner asthede- ReLU activation is used. The resulting output, a feature vector of velopment dataset and has a similar class distribution. Morestatis- 64 coefficients, is then provided to a Fully Connected (FC) layer tics about this dataset will be made available after the task is fin- of 64 neurons with ReLU activation. The output layer consistsof ished. The dataset was provided as audio only. It contains data 9neurons representing the output classes with Softmaxactivation. recorded by all seven microphone arrays on a different time-wise For regularization wehaveused Dropout (20%) between each layer. subset as the development set. The final evaluation will be based Thenetwork istrained using Adam optimizer with alearning rateof on the data obtained by the microphone arrays not present in the 0.0001. The used batch sizeis256 segments which resultsin atotal development set. The other microphone arrays are provided togive of 1024 examples provided to the learner given that each segment insights about the overfitting on those positions. contai ns 4 channel s of audio. On each epoch, the trai ning dat aset is Participants were allowed to use external data (including pre- randomly subsampled so that thenumber of examplesfor each class trained models) and data augmentation for system development. It match the size of the smallest cl ass. The performance of the model was not allowed to use the evaluation data to train the submitted isevaluated every 10 epochs, of 500 in total, on a validation subset system in an (un)supervised manner. (30% subsampled from the training set). The model with the high- est scoreisused as thefinal model . Asametric themacro-aver aged F1-score is used, which is the mean of the class-wise F1-scores. 2.4. Baseline system The baseline system is intended to make it easier to participate in 3. BASELINE SYSTEM RESULTS the task and to provide a reference performance. The system has al l the functional ity for dataset handling, calculat ing features and Table 3 presents the results for the baseline system on the devel- model s, and eval uat ing the results. The system is implemented in opment set using the provided cross-validation folds. The per for- Python, primarily using the DCASE UTIL libary [13] for dataset mance on the eval uat ion set will be rel eased when the results of handling and featureextraction and the Keras library [14] for learn- task are made public. Regarding theresultson thedevelopment set, ing. The baseline system trains asingleclassifier model that takes asin- glechannel asinput. During the recording campaign, datawasmea- Activity F1-score dev. F1-score eval. sured si multaneousl y usi ng multiple microphone arrays eachcon- Absence 85.41% NA taining 4 microphones. Hence, each domestic activity isrecorded as Cooking 95.14% NA many timesastherewer emicrophones. Each par al lel recording of a Dishwashing 76.73% NA si ngle activity is consi dered as a different