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Yannick Becker

Publications and source records attributed to Yannick Becker.

2 recordsLinked to original sources

Longitudinal MRI template of the baboon brain from birth to adolescence

The baboon (Papio) is an invaluable resource within nonhuman primate research, having the advantage of being a cercopithecoid (Old World monkey) with one of the largest brains among non-hominid primates. In order to facilitate comparative developmental neuroscience research, we present the BABACOOL (BAby Brain Atlas COnstruction for Optimized Labeled segmentation) approach for creating multi-modal developmental atlases, which we used to produce BaBa21, a population-based longitudinal developmental baboon template. BaBa21 is a spatio-temporal template that consists of structural (T1- and T2-weighted) images and tissue probability maps from a population of 21 baboons (Papio anubis) scanned at 4 timepoints beginning from about 2 weeks after birth and continuing to sexual maturity (5 years). Further, his study offers a fully automatic method for generating a template at any intermediate age for future age-specific group studies. This resource is made available to provide a normalization target for baboon data across the lifespan, including intermediate timepoints, and moreover facilitate neuroimaging research in baboons, comparative research with humans and nonhuman primate species for which developmental templates are available (e.g., macaques).

q-bio.QM

Risk management in multi-objective portfolio optimization under uncertainty

In portfolio optimization, decision makers face difficulties from uncertainties inherent in real-world scenarios. These uncertainties significantly influence portfolio outcomes in both classical and multi-objective Markowitz models. To address these challenges, our research explores the power of robust multi-objective optimization. Since portfolio managers frequently measure their solutions against benchmarks, we enhance the multi-objective min-regret robustness concept by incorporating these benchmark comparisons. This approach bridges the gap between theoretical models and real-world investment scenarios, offering portfolio managers more reliable and adaptable strategies for navigating market uncertainties. Our framework provides a more nuanced and practical approach to portfolio optimization under real-world conditions.

q-fin.PM