97 research outputs found
Faculty Opinions recommendation of A new potential biomarker for dementia with Lewy bodies: Skin nerve α-synuclein deposits.
No full textFaculty Opinions recommendation of Blood-based NfL: A biomarker for differential diagnosis of parkinsonian disorder.
No full textFaculty Opinions recommendation of Levodopa-carbidopa intestinal gel in advanced Parkinson's: Final results of the GLORIA registry.
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Epidemiology of tardive dyskinesia before and during the era of modern antipsychotic drugs
No full textNon-Monotonicity in Empirical Learning Curves: Identifying non-monotonicity through slope approximations on discrete points
No full textLearning curves are used to shape the performance of a Machine Learning (ML) model with respect to the size of the set used for training it. It was commonly thought that adding more training samples would increase the model's accuracy (i.e., they are monotone), but recent works show that may not always be the case. In other words, some learners on some problems show non-monotonic behaviour. To this extent, we introduce a new method to identify non-monotonicity in empirical learning curves by approximating the curve's slope through regression around the discrete points it is defined on.This paper formalises this metric and then evaluates its accuracy through different experiments. Finally, we run the proposed metric on a subset of the extensive Learning Curve Database (LCDB) by Mohr et al. to gain better insights into the problem of non-monotonicity of learning. We found that the metric can identify non-monotonicity in learning curves well (98% experimental accuracy) and does not consider small increases due to measurement error as non-monotonicity in the curve. Finally, we have identified that non-monotonicity may be a property of some classifiers, such as Linear Discriminant Analysis. Moreover, we identified that non-monotonicity is frequently observed in datasets with faster training times.CSE3000 Research ProjectComputer Science and Engineerin
Collaborative Solution Architecture for Developing a National Interoperability Framework in Romania
No full textInteroperability framework is a set of standards and guidelines that describe how organizations have established or will establish to interact. The framework is not static, but one that adapts to the change of standards, administrative requirements and technology. It can be adapted to the socio - economic, political, cultural, linguistic, historical and geographical purposes and to a specific context or situation. The article aims to clarify the essential concepts necessary for outlining Romanian national interoperability framework and to propose collaborative solution architecture for its development, updating and maintaining.Interoperability Framework, National Interoperability Framework, European Interoperability Strategy, Collaborative Solution Architecture
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