In: Cancers, 2021, vol. 13, no. 19, p. 18
Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to...
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In: Algorithms, 2021, vol. 14, no. 9, p. 25
Recent systems applying Machine Learning (ML) to solve the Traveling Salesman Problem (TSP) exhibit issues when they try to scale up to real case scenarios with several hundred vertices. The use of Candidate Lists (CLs) has been brought up to cope with the issues. A CL is defined as a subset of all the edges linked to a given vertex such that it contains mainly edges that are believed to be...
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In: International journal of molecular sciences, 2021, vol. 22, no. 9, p. 16
Despite the huge body of research on osteogenic differentiation and bone tissue engineering, the translation potential of in vitro results still does not match the effort employed. One reason might be that the protocols used for in vitro research have inherent pitfalls. The synthetic glucocorticoid dexamethasone is commonly used in protocols for trilineage differentiation of human bone marrow...
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In: Annals of Mathematics and Artificial Intelligence, 2021, no. 89, p. 965–1011
We develop joint foundations for the fields of social choice and opinion pooling using coherent sets of desirable gambles, a general uncertainty model that allows to encompass both complete and incomplete preferences. This leads on the one hand to a new perspective of traditional results of social choice (in particular Arrow’s theorem as well as sufficient conditions for the existence of an...
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In: Algorithms, 2020, vol. 13, no. 12, p. 17
Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations. BN parameter learning from incomplete data is usually implemented with the Expectation-Maximisation algorithm (EM), which computes the relevant sufficient statistics (“soft EM”) ...
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In: Cancers, 2020, vol. 12, no. 11, p. 15
Up to 50% of myocarditis events developed in cancer patients upon treatment with immune checkpoint inhibitors (ICIs) are fatal. Therefore, identification of clinical risk factors predicting myocarditis onset during treatment with ICIs is important for the purpose of cardiac surveillance of high-risk patients. The aim of this retrospective matched case-control study was to assess whether...
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In: Machines, 2020, vol. 8, no. 4, p. 17
Industrial robots are commonly used to perform interaction tasks (such as assemblies or polishing), requiring the robot to be in contact with the surrounding environment. Such environments are (partially) unknown to the robot controller. Therefore, there is the need to implement interaction controllers capable of suitably reacting to the established contacts. Although standard force controllers...
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In: Entropy, 2021, vol. 23, no. 1, p. 27 p
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical...
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In: Machine Learning, 2015, vol. 100, no. 2-3, p. 285-304
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In: Environmental and Ecological Statistics, 2015, vol. 22, no. 3, p. 513-534
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