Bayesian Additive Regression Networks

We apply Bayesian Additive Regression Tree (BART) principles to training an ensemble of small neural networks for regression tasks. Using Markov Chain Monte Carlo, we sample from the posterior distribution of neural networks that have a single hidden layer. To create an ensemble of these, we apply Gibbs sampling to update each network against the residual target value (i.e. subtracting the effect of the other networks). We demonstrate the effectiveness of this technique on several benchmark regression problems, comparing it to equivalent shallow neural networks, BART, and ordinary least squares. Our Bayesian Additive Regression Networks (BARN) provide more consistent and often more accurate results. On test data benchmarks, BARN averaged between 5 to 20 percent lower root mean square error. This error performance does come at the cost, however, of greater computation time. BARN sometimes takes on the order of a minute where competing methods take a second or less. But, BARN without cross-validated hyperparameter tuning takes about the same amount of computation time as tuned other methods. Yet BARN is still typically more accurate

BARMPy: Bayesian Additive Regression Models Python Package

We make Bayesian Additive Regression Networks (BARN) available as a Python package, \texttt{barmpy}, with documentation at \url{https://dvbuntu.github.io/barmpy/} for general machine learning practitioners. Our object-oriented design is compatible with SciKit-Learn, allowing usage of their tools like cross-validation. To ease learning to use \texttt{barmpy}, we produce a companion tutorial that expands on reference information in the documentation. Any interested user can \texttt{pip install barmpy} from the official PyPi repository. \texttt{barmpy} also serves as a baseline Python library for generic Bayesian Additive Regression Models

MicroRNA-155 is induced during the macrophage inflammatory response

The mammalian inflammatory response to infection involves the induction of several hundred genes, a process that must be carefully regulated to achieve pathogen clearance and prevent the consequences of unregulated expression, such as cancer. Recently, microRNAs (miRNAs) have emerged as a class of gene expression regulators that has also been linked to cancer. However, the relationship between inflammation, innate immunity, and miRNA expression is just beginning to be explored. In the present study, we use microarray technology to identify miRNAs induced in primary murine macrophages after exposure to polyriboinosinic:polyribocytidylic acid or the cytokine IFN-{beta}. miR-155 was the only miRNA of those tested that was substantially up-regulated by both stimuli. It also was induced by several Toll-like receptor ligands through myeloid differentiation factor 88- or TRIF-dependent pathways, whereas up-regulation by IFNs was shown to involve TNF-{alpha} autocrine signaling. Pharmacological inhibition of the kinase JNK blocked induction of miR-155 in response to either polyriboinosinic:polyribocytidylic acid or TNF-{alpha}, suggesting that miR-155-inducing signals use the JNK pathway. Together, these findings characterize miR-155 as a common target of a broad range of inflammatory mediators. Importantly, because miR-155 is known to function as an oncogene, these observations identify a potential link between inflammation and cancer

The Impact of Residential Life Hall Directors Becoming Hourly Employees in the Minnesota State University System

Live-on residential life hall director staff shifted from a salaried employment status to an hourly employment status as a result of a reinterpretation of the duties test of the Fair Labor Standards Act (FLSA) for these positions. Transitioning live-on residential life staff to hourly employment status occurred at institutions in the Minnesota State University System resulting in an unusual employment status for hall directors in the Minnesota State System compared to the national norm. Residence hall directors who became hourly employees served as the population of this basic qualitative study. The purpose was to explore the impact on worker performance resulting from the transition to hourly employment status. Blumberg and Pringle’s (1982) theory of worker performance served as the theoretical framework that guided this study. Kezar’s (2013) expansion of this theory of worker performance also contributed to the theoretical underpinning of this study. The narratives that emerged from this qualitative study provides context for future higher education leaders faced with the changing employment status of employees who have historically been salaried and then must transition to overtime-eligible status. Non-exempt (overtime-eligible) professionals in a higher education setting are likely confined to performing their duties in the context of a 40-hour workweek. The impact of this on professional residential life hall directors will be discussed

Monitoring grain boundary migration during recrystallisation using topotomography

International audienceThe growth of a single grain during recrystallisation into a mildly deformed {001}100 oriented single crystal has been monitored by synchrotron radiation using the topotomo technique. The formation and migration of individual facets is analysed using a new method which measures distances between grain boundary segments at different time steps along parallel lines normal to the facet plane. One facet is shown to move with a constant rate, while it remains planar and keeps the same boundary plane orientation. The formation of another facet, which is analysed in detail, reveals that first a planar boundary with a different orientation forms before it changes its boundary plane orientation into that of the final facet. It is argued that the local microstructural configuration in front of moving grain boundaries has a considerable influence on the kinetics of individual boundary segments and facets