Bayesian Filtering for Multi-period Mean-Variance Portfolio Selection. (arXiv:1911.07526v1 [q-fin.PM])

For a long investment time horizon, it is preferable to rebalance the portfolio weights at intermediate times. This necessitates a multi-period market model in which portfolio optimization is usually done through dynamic programming. However, this assumes a known distribution for the parameters of the financial time series. We consider the situation where this distribution is…

Transfer Learning of fMRI Dynamics. (arXiv:1911.06813v1 [eess.IV])

As a mental disorder progresses, it may affect brain structure, but brain function expressed in brain dynamics is affected much earlier. Capturing the moment when brain dynamics express the disorder is crucial for early diagnosis. The traditional approach to this problem via training classifiers either proceeds from handcrafted features or requires large datasets to combat…

Causal inference with recurrent data via inverse probability treatment weighting method (IPTW). (arXiv:1911.06868v1 [stat.ME])

Propensity score methods are increasingly being used to reduce estimation bias of treatment effects for observational studies. Previous research has shown that propensity score methods consistently estimate the marginal hazard ratio for time to event data. However, recurrent data frequently arise in the biomedical literature and there is a paucity of research into the use…

The Laplace transform of the integrated Volterra Wishart process. (arXiv:1911.07719v1 [math.PR])

We establish an explicit expression for the conditional Laplace transform of the integrated Volterra Wishart process in terms of a certain resolvent of the covariance function. The core ingredient is the derivation of the conditional Laplace transform of general Gaussian processes in terms of Fredholm’s determinant and resolvent. Furthermore , we link the characteristic exponents…

Analysis of the light production and propagation in the 4-tonne dual-phase demonstrator. (arXiv:1911.06880v1 [physics.ins-det])

The Deep Underground Neutrino Experiment (DUNE) is a leading-edge experiment designed to perform neutrino science and proton decay searches. In particular, the far detector will consist of four 10-kton Liquid Argon (LAr) Time Projection Chambers using both single and dual-phase technologies. The latter provides charge amplification in the gaseous phase. In order to optimize these…

A Bootstrap-based Inference Framework for Testing Similarity of Paired Networks. (arXiv:1911.06869v1 [stat.ME])

We live in an interconnected world where network valued data arises in many domains, and, fittingly, statistical network analysis has emerged as an active area in the literature. However, the topic of inference in networks has received relatively less attention. In this work, we consider the paired network inference problem where one is given two…

Using the Multirhodotron as an Advanced Rhodotron. (arXiv:1911.06887v1 [physics.acc-ph])

This article assesses the use of a new type of electron accelerator, the Multirhodotron, for four new purposes that cannot be implemented using Rhodotrons and linacs. This study awards some arguments about possible replacement of nuclear reactors by electron accelerators in process of producing of medical isotopes on a global scale, about new possible electron…

QC-Automator: Deep Learning-based Automated Quality Control for Diffusion MR Images. (arXiv:1911.06816v1 [eess.IV])

Quality assessment of diffusion MRI (dMRI) data is essential prior to any analysis, so that appropriate pre-processing can be used to improve data quality and ensure that the presence of MRI artifacts do not affect the results of subsequent image analysis. Manual quality assessment of the data is subjective, possibly error-prone, and infeasible, especially considering…

Opportunities for artificial intelligence in advancing precision medicine. (arXiv:1911.07125v1 [cs.AI])

Machine learning (ML), deep learning (DL), and artificial intelligence (AI) are of increasing importance in biomedicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health. High-throughput technologies are delivering growing volumes…