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Over the past few years hyperspectral (HS) imaging has been broadly applied in a wealth of different applications with remote sensing of the environment being the most prominent one. HS imaging provides a rich amount of information by generating images and videos of high spectral resolution captured at a wide range of the electro-magnetic spectrum. Recently, HS data have been shown to offer remarkable advances to a new field of significant interest i.e., medical HS (mHS) imaging. The high spectral resolution of HS data makes them amenable to identifying even subtle spectral differences related to various pathological conditions. In view of that, mHS images and videos have received considerable attention lately. mHS data have already been used for non-invasive diagnosis of several types of cancer e.g. brain, tongue cancer, as well as for diabetic foot diagnosis and surgical guidance. mHS imaging is anticipated to remarkably flourish in the years to come taking into account the recent advances that have occurred in the development of micro-size and low-cost HS cameras. However, despite this large progress in HS imaging hardware, sophisticated algorithms capable to interpret these data are still missing. HyPPOCRATES aims at deriving new powerful mHS image and video interpretation schemes tailored to mHS data processing, by applying novel machine learning ideas. To this end, the problems of subspace clustering and unmixing will be investigated for performing refined mHS image and video understanding. Along those lines, constrained matrix and tensor factorization approaches will be explored for devising computationally efficient and scalable machine learning algorithms. Overall, the main objective of the project is to bridge the gap between the recent advances in mHS imaging and those in machine learning research. This way, the researcher aspires to go the diagnostic process of several serious diseases, such as various types of cancer, one step further.
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Massive stars dominate their surroundings during their short lifetimes, while their explosive deaths impact the chemical evolution and spatial cohesion of their hosts. After birth, their evolution is largely dictated by their ability to remove layers of hydrogen from their envelopes. Multiple lines of evidence are pointing to violent, episodic mass-loss events being responsible for removing a large part of the massive stellar envelope, especially in low-metallicity galaxies. Episodic mass loss, however, is not understood theoretically, neither accounted for in state-of-the-art models of stellar evolution, which has far-reaching consequences for many areas of astronomy. We aim to determine whether episodic mass loss is a dominant process in the evolution of the most massive stars by conducting the first extensive, multi-wavelength survey of evolved massive stars in the nearby Universe. The project hinges on the fact that mass-losing stars form dust and are bright in the mid-infrared. We plan to (i) derive physical parameters of a large sample of dusty, evolved targets and estimate the amount of ejected mass, (ii) constrain evolutionary models, (iii) quantify the duration and frequency of episodic mass loss as a function of metallicity. The approach involves applying machine-learning algorithms to existing multi-band and time-series photometry of luminous sources in ~25 nearby galaxies. Dusty, luminous evolved massive stars will thus be automatically classified and follow-up spectroscopy will be obtained for selected targets. Atmospheric and SED modeling will yield parameters and estimates of time-dependent mass loss for ~1000 luminous stars. The emerging trend for the ubiquity of episodic mass loss, if confirmed, will be key to understanding the explosive early Universe and will have profound consequences for low-metallicity stars, reionization, and the chemical evolution of galaxies.
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DUST-GLASS aims at improving global dust prediction and monitoring by optimizing an advanced data assimilation system (LETKF scheme) coupled with a sophisticated atmospheric-dust model (NMMB/BSC-Dust). For the accomplishment of these core scientific goals, a fine resolution (0.1o x 0.1o) global dust optical depth (DOD) database, suitable for data assimilation, will be developed via a synergy of state-of-the-art Level 2 satellite retrievals acquired by MODIS, MISR and OMI sensors (2007-2016). The impacts of assimilating this novel dataset (DOD) on model’s predictive skills, both at global and regional scale, will be assessed objectively. Global forecasts (5 days) will be carried out for different periods aiming at studying dust aerosols’ mobilization and transport from the major dust sources of the planet, while a global reanalysis (0.5o x 0.7o) dataset will be generated for long-term dust monitoring. In addition, regional short-term (84 hours) forecasts will be conducted for 20 Mediterranean dust outbreaks identified by a satellite algorithm in the framework of the MDRAF project (fellow’s previous MC-IEF). In the evaluation analysis, the model’s dust outputs will be compared versus measurements derived by ground networks (AERONET, MAN, ACTRIS) as well as against columnar/vertical satellite retrievals (MODIS, MISR, CALIOP). Moreover, temperature and radiation will be also considered since “corrections” on dust fields, thanks to data assimilation, are expected to be evident on both parameters due to dust-radiation interactions. The aforementioned variables will be compared against observations obtained by ground networks (ISB, RAOB, BSRN) and reanalysis/analysis products (ERA-Interim, FNL). Considering the multifaceted role of dust, the scientific outcomes of DUST-GLASS are expected to contribute effectively to interdisciplinary studies regarding dust aerosols as well as their associated impacts on health, anthropogenic activities, environment, weather and climate.
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