A trust-region method for stochastic variational inference with applications to streaming data

Stochastic variational inference allows for fast posterior inference in complex Bayesian models. However, the algorithm is prone to local optima which can make the quality of the posterior approximation sensitive to the choice of hyperparameters and initialization. We address this problem by replacing the natural gradient step of stochastic varitional inference with a trust-region update. We show that this leads to generally better results and reduced sensitivity to hyperparameters. We also describe a new strategy for variational inference on streaming data and show that here our trust-region method is crucial for getting good performance.Comment: in Proceedings of the 32nd International Conference on Machine Learning, 201

A Daily Diary Investigation of Latino Ethnic Identity, Discrimination, and Depression

The objectives of the current study were to document the effects of discrimination on Latino mental health and to identify the circumstances by which ethnic identity serves a protective function. Instances of discrimination and depressive symptoms were measured every day for 13 days in a sample of Latino adults (N = 91). Multilevel random coefficient modeling showed a 1-day lagged effect in which increases in depression were observed the day following a discriminatory event. The findings also revealed differential effects of ethnic identity exploration and commitment. Whereas ethnic identity exploration was found to exacerbate the influence of daily discrimination on next-day depression, ethnic identity commitment operated as a stress buffer, influencing the intensity of and recovery from daily discrimination. The findings are discussed within a stress and coping perspective that identifies appropriate cultural resources for decreasing the psychological consequences associated with daily discrimination

Production Practices of Arkansas Beef Cattle Producers

This report contains information from a 1996 survey on production practices of Arkansas beef cattle producers. While several studies have been completed on the profitability of retained ownership of beef cattle, few empirical data are available on production practices of cow/calf and stocker operations in Arkansas. This report shows that there are some differences in production methods across operation types. Further, the report summarizes demographic characteristics of Arkansas cow/calf and stocker operations. The results of this study can be particularly helpful in providing the needed data for studying the potential economic impact of feeding weaned calves to heavier weights in Arkansas as a value-added production alternative to selling calves at weaning. It should also prove helpful in the formulation of budgets and simulation models

A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data

Background: A better understanding of the mechanisms involved in gas-phase fragmentation of peptides is essential for the development of more reliable algorithms for high-throughput protein identification using mass spectrometry (MS). Current methodologies depend predominantly on the use of derived m/z values of fragment ions, and, the knowledge provided by the intensity information present in MS/MS spectra has not been fully exploited. Indeed spectrum intensity information is very rarely utilized in the algorithms currently in use for high-throughput protein identification. Results: In this work, a Bayesian neural network approach is employed to analyze ion intensity information present in 13878 different MS/MS spectra. The influence of a library of 35 features on peptide fragmentation is examined under different proton mobility conditions. Useful rules involved in peptide fragmentation are found and subsets of features which have significant influence on fragmentation pathway of peptides are characterised. An intensity model is built based on the selected features and the model can make an accurate prediction of the intensity patterns for given MS/MS spectra. The predictions include not only the mean values of spectra intensity but also the variances that can be used to tolerate noises and system biases within experimental MS/MS spectra. Conclusion: The intensity patterns of fragmentation spectra are informative and can be used to analyze the influence of various characteristics of fragmented peptides on their fragmentation pathway. The features with significant influence can be used in turn to predict spectra intensities. Such information can help develop more reliable algorithms for peptide and protein identification

Tailoring many-body entanglement through local control

We construct optimal time-local control pulses based on a multipartite entanglement measure as target functional. The underlying control Hamiltonians are derived in a purely algebraic fashion, and the resulting pulses drive a composite quantum system rapidly into that highly entangled state which can be created most efficiently for a given interaction mechanism, and which bears entanglement that is robust against decoherence. Moreover, it is shown that the control scheme is insensitive to experimental imperfections in first order.Comment: 12 pages, 11 figure

The Future of the European Growth and Stability Pact

Konvergenzkriterie, Finanzpolitik, Institutionelle Infrastruktur, Internationale wirtschaftspolitische Koordination, Europäische Wirtschafts- und Währungsunion, EU-Staaten, Convergence criteria, Fiscal policy, Institutional infrastructure, Economic policy coordination, European Economic and Monetary Union, EU countries

Gene length as a regulator for ribosome recruitment and protein synthesis : theoretical insights

The authors would like to acknowledge the funding provided by the European Union Seventh Framework Programme [FP7/2007–2013] (NICHE; grant agreement 289384) (LDF). LDF also acknowledges the funding provided by the São Paulo Research Foundation (FAPESP - grant #2015/26989-4). AM was partially funded by the UK Biotechnology and Biological Research Council (BBSRC), through grant BB/N015711/1. LC would like to acknowledge Maria Carmen Romano, Jean Hausser, Marco Cosentino Lagomarsino, Jean-Charles Walter and Norbert Kern for early discussions on this work, and the CNRS for having granted him a “demi-délégation” (2017–18). We would like to dedicate this work in memory of Maxime Clusel and Vladimir Lorman.Peer reviewedPublisher PDFPublisher PD