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  1. Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained on observed outcomes (e.g. whether it has rained or not, or whether a patient has died or not), because the ground-truth probabilities of the events of interest are typically unknown. The problem is therefore analogous to binary classification, with the difference that the objective is to estimate probabilities rather than predicting the specific outcome. This work investigates probability estimation from high-dimensional data using deep neural networks. There exist several methods to improve the probabilities generated by these models butmore »they mostly focus on model (epistemic) uncertainty. For problems with inherent uncertainty, it is challenging to evaluate performance without access to ground-truth probabilities. To address this, we build a synthetic dataset to study and compare different computable metrics. We evaluate existing methods on the synthetic data as well as on three real-world probability estimation tasks, all of which involve inherent uncertainty: precipitation forecasting from radar images, predicting cancer patient survival from histopathology images, and predicting car crashes from dashcam videos. We also give a theoretical analysis of a model for high-dimensional probability estimation which reproduces several of the phenomena evinced in our experiments. Finally, we propose a new method for probability estimation using neural networks, which modifies the training process to promote output probabilities that are consistent with empirical probabilities computed from the data. The method outperforms existing approaches on most metrics on the simulated as well as real-world data.« less
    Free, publicly-accessible full text available January 1, 2023
  2. Free, publicly-accessible full text available December 1, 2022
  3. In this work we study numerically liquid metal flow in a square duct under the influence of a transverse magnetic field applied in a spanwise direction (coplanar). The key interest of the present study is an attempt of passive control of flow regimes developed under magnetic field and thermal loads by applying specially shaped conditions, such as swirling, at the duct inlet. In this paper, we report results of numerical simulations of the interaction of swirling flow and transverse magnetic field in a square duct flow. Analysis of the obtained regimes might be important for the development of an experimentalmore »setup, in order to design corresponding inlet sections.« less
  4. Small RNAs (sRNAs), ~20–25 nucleotide (nt) in size, regulate various biological processes in plants through directing sequence-specific gene silencing. sRNAs are derived from either single- or double-stranded precursor RNAs. Proper levels of sRNAs are crucial for plant growth, development, genomic stability, and adaptation to abiotic and biotic stresses. Studies have identified the machineries controlling sRNA levels through biogenesis and degradation. This chapter covers recent progresses related to mechanisms governing small RNA biogenesis and degradation.