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When acting as a privacy-enhancing technology, synthetic data generation (SDG) aims to maintain a resemblance to the real data while excluding personally-identifiable information. Many SDG algorithms provide robust differential privacy (DP) guarantee
Externí odkaz:
http://arxiv.org/abs/2410.05506
Autor:
Filienko, Daniil, Wang, Yinzhou, Jazmi, Caroline El, Xie, Serena, Cohen, Trevor, De Cock, Martine, Yuwen, Weichao
While Large Language Models (LLMs) are being quickly adapted to many domains, including healthcare, their strengths and pitfalls remain under-explored. In our study, we examine the effects of prompt engineering to guide Large Language Models (LLMs) i
Externí odkaz:
http://arxiv.org/abs/2409.00112
Autor:
Bekaert, Hans, Van Dooren, Wim, Van Winckel, Hans, Poessel, Markus, Thiering, Inge, Nicolini, Marco, Artioli, Enrico, Avgerinou, Despina, Balla, Eleana, De Cock, Mieke
In the context of the European Erasmus+ project Teaching ASTronomy at the Educational level (TASTE), we investigated the extent to which a learning module at school and a set of activities during a planetarium visit help students to gain insight in t
Externí odkaz:
http://arxiv.org/abs/2405.19741
Data is the lifeblood of the modern world, forming a fundamental part of AI, decision-making, and research advances. With increase in interest in data, governments have taken important steps towards a regulated data world, drastically impacting data
Externí odkaz:
http://arxiv.org/abs/2402.08614
Synthetic data generation (SDG) has become increasingly popular as a privacy-enhancing technology. It aims to maintain important statistical properties of its underlying training data, while excluding any personally identifiable information. There ha
Externí odkaz:
http://arxiv.org/abs/2402.06699
Autor:
Joly, C., Berthelot, S., Blivet, S., Chatelet, F., Gandolfo, N., Lhomme, C., Mavilla, G., Saugnac, H., Olivier, G., Pierens, M., Yaniche, J-F., Bouly, F., Bourrion, O., Gomez-Martinez, Y., Tourres, D., Gaudin, C., Bolli, J-L., Garçia-Alfonso, I., Della Faille, P., Vanderlinden, M., De Cock, W.
Within the framework of the first phase of MYRRHA (Multi-purpose hYbrid Research Reactor for High-tech Applications) project, called MINERVA, IJCLab was in charge of a fully equipped Spoke cryomodule prototype development, tested at 2K. It integrates
Externí odkaz:
http://arxiv.org/abs/2310.07426
Autor:
Campo, Bavo De Cock
Calibration is a pivotal aspect in predictive modeling, as it ensures that the predictions closely correspond with what we observe empirically. The contemporary calibration framework, however, is predominantly focused on prediction models where the o
Externí odkaz:
http://arxiv.org/abs/2309.08559
Users worldwide access massive amounts of curated data in the form of rankings on a daily basis. The societal impact of this ease of access has been studied and work has been done to propose and enforce various notions of fairness in rankings. Curren
Externí odkaz:
http://arxiv.org/abs/2303.02916
Autor:
Pereira, Mayana, Pentyala, Sikha, Nascimento, Anderson, Sousa Jr., Rafael T. de, De Cock, Martine
Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education. Synthetic data generation algorithms with privacy guarantees are emerging as a paradigm to break this
Externí odkaz:
http://arxiv.org/abs/2210.07332
Traditional mathematical models of Hele--Shaw flow consider the injection (or withdrawal) of an air bubble into (or from) an infinite body of viscous fluid. The most commonly studied feature of such a model is how the Saffman-Taylor instability drive
Externí odkaz:
http://arxiv.org/abs/2206.12602