The goal of SEED is to fundamentally advance the methodology of computer vision by exploiting a dynamic analysis perspective in order to acquire accurate, yet tractable models, that can automatically learn to sense our visual world, localize still and animate objects (e.g. chairs, phones, computers, bicycles or cars, people and animals), actions and interactions, as well as qualitative geometrical and physical scene properties, by propagating and consolidating temporal information, with minimal system training and supervision. SEED will extract descriptions that identify the precise boundaries and spatial layout of the different scene components, and the manner they move, interact, and change over time. For this purpose, SEED will develop novel high-order compositional methodologies for the semantic segmentation of video data acquired by observers of dynamic scenes, by adaptively integrating figure-ground reasoning based on bottom-up and top-down information, and by using weakly supervised machine learning techniques that support continuous learning towards an open-ended number of visual categories. The system will be able not only to recover detailed models of dynamic scenes, but also forecast future actions and interactions in those scenes, over long time horizons, by contextual reasoning and inverse reinforcement learning. Two demonstrators are envisaged, the first corresponding to scene understanding and forecasting in indoor office spaces, and the second for urban outdoor environments. The methodology emerging from this research has the potential to impact fields as diverse as automatic personal assistance for people, video editing and indexing, robotics, environmental awareness, augmented reality, human-computer interaction, or manufacturing.
Advances in tracking technology during the last decade have shown that migratory birds have the capacity to fly longer and faster than we previously thought was possible. Yet, we do not know how birds perform these seemingly impossible travels as it previously only was possible to record spatiotemporal patterns. The overall aim of this project is to reveal constraints and the behavioural and physiological adaptations that has evolved to overcome them, thus making the extreme performances of migratory birds possible. This goal will be met by using novel tracking devices, multisensor data loggers, that in addition to spatiotemporal patterns also record behaviour, including flight altitudes, temperature and detailed timing of flights and stopovers during the entire migration cycle. The few multisensor tracking studies carried out to date have provided hints of stunning new insights, and seriously challenged previously assumed limits on peak flight altitudes, in-flight changes of altitudes, and duration of individual flights. In particular, I have together with colleagues discovered a totally unexpected altitudinal behaviour: some bird species change their flight altitude between night and day, and fly at extremely high altitudes during the day (up to 6000-8000 m). But what makes a migratory bird fly as high as Mount Everest, even when there are no mountains to cross? By launching an extensive multisensor data logging programme, combined with wind tunnel experiments and field studies, the proposed project will change our understanding of the possibilities and limitations of bird migration. This will be done by disentangling the causes and consequences of bird’s altitudinal behaviour, the flexibility, timing and duration of migratory flights (if birds only use diurnal or nocturnal flights, if they prolong flights to last both day and night or even fly nonstop between wintering and breeding grounds), and the costs and consequences of these seemingly extreme behaviours.
Gliomas are the most common brain tumors and the highest-grade glioma, glioblastoma (GBM), is arguably the most aggressive tumor type, with no long-term survivors. Patients with GBM are treated with radiotherapy, chemotherapy, surgery, and tumor treating fields. Despite initial response all tumors recur as incurable lesions; there is an urgent need for novel therapeutic approaches for this patient group. The majority of GBMs recur within the treatment field receiving high-dose radiotherapy during treatment of the primary tumor; the recurrent tumor thus forms in an irradiated microenvironment. Despite the fact that it is the recurrent tumor that ultimately kills the patient and that the majority of new therapeutic agents for GBM are tested clinically in the recurrent setting, the majority of experimental models and clinical materials for drug discovery are based on primary disease. Recent advances established a central role for the tumor microenvironment in determining the therapeutic response of GBM cells, and our lab demonstrated that standard of care radiotherapy of the primary tumor can shape the microenvironment to generate tumor-supportive conditions in the recurrent tumor; These findings suggest that there is untapped potential in targeting the irradiated microenvironment. This proposal aims to explore and exploit the recurrent brain tumor microenvironment by i) consolidating the contribution of the irradiated brain tumor microenvironment to GBM resistance by integrating spatial transcriptomics, single cell RNA sequencing, and multiplexed immunohistochemistry from state-of-the-art murine and human models of GBM treatment and recurrence, and ii) discovering and targeting novel therapeutic targets unique to the post-radiotherapy brain tumor microenvironment by high-throughput phenotypic screening, with the ultimate goal of exploiting reversible stromal radiation responses and leverage novel therapeutic opportunities unique to the irradiated brain.