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1579 Publications visible to you, out of a total of 1579
SpeciesRax: A tool for maximum likelihood species tree inference from gene family trees under duplication, transfer, and loss

Abstract

Not specified

Authors: Benoit Morel, Paul Schade, Sarah Lutteropp, Tom A. Williams, Gergely J. Szöllősi, Alexandros Stamatakis

Date Published: 29th Mar 2021

Publication Type: Journal

SPART, a versatile and standardized data exchange format for species partition information

Abstract

Not specified

Authors: Aurélien Miralles, Jacques Ducasse, Sophie Brouillet, Tomas Flouri, Tomochika Fujisawa, Paschalia Kapli, L. Lacey Knowles, Sangeeta Kumari, Alexandros Stamatakis, Jeet Sukumaran, Sarah Lutteropp, Miguel Vences, Nicolas Puillandre

Date Published: 22nd Mar 2021

Publication Type: Journal

From Photometric Redshifts to Improved Weather Forecasts: machine learning and proper scoring rules as a basis for interdisciplinary work

Abstract (Expand)

The amount, size, and complexity of astronomical data-sets and databases are growing rapidly in the last decades, due to new technologies and dedicated survey telescopes. Besides dealing with poly-structured … and complex data, sparse data has become a field of growing scientific interest. A specific field of Astroinformatics research is the estimation of redshifts of extra-galactic sources by using sparse photometric observations. Many techniques have been developed to produce those estimates with increasing precision. In recent years, models have been favored which instead of providing a point estimate only, are able to generate probabilistic density functions (PDFs) in order to characterize and quantify the uncertainties of their estimates. Crucial to the development of those models is a proper, mathematically principled way to evaluate and characterize their performances, based on scoring functions as well as on tools for assessing calibration. Still, in literature inappropriate methods are being used to express the quality of the estimates that are often not sufficient and can potentially generate misleading interpretations. In this work we summarize how to correctly evaluate errors and forecast quality when dealing with PDFs. We describe the use of the log-likelihood, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) to characterize the calibration as well as the sharpness of predicted PDFs. We present what we achieved when using proper scoring rules to train deep neural networks as well as to evaluate the model estimates and how this work led from well calibrated redshift estimates to improvements in probabilistic weather forecasting. The presented work is an example of interdisciplinarity in data-science and illustrates how methods can help to bridge gaps between different fields of application.

Authors: Kai Polsterer, Sebastian Lerch, Antonio D'Isanto

Date Published: 5th Mar 2021

Publication Type: InProceedings

Want to track pandemic variants faster? Fix the bioinformatics bottleneck

Abstract

Not specified

Authors: Emma B. Hodcroft, Nicola De Maio, Rob Lanfear, Duncan R. MacCannell, Bui Quang Minh, Heiko A. Schmidt, Alexandros Stamatakis, Nick Goldman, Christophe Dessimoz

Date Published: 4th Mar 2021

Publication Type: Journal

Sensing and sensitivity: Computational chemistry of graphene‐based sensors

Abstract (Expand)

Highly efficient, tunable, biocompatible, and environmentally friendly electrochemical sensors featuring graphene‐based materials pose a formidable challenge for computational chemistry. In silico … rationalization, optimization and, ultimately, prediction of their performance requires exploring a vast structural space of potential surface‐analyte complexes, further complicated by the presence of various defects and functionalities within the infinite graphene lattice. This immense number of systems and their periodic nature greatly limit the choice of computational tools applicable at a reasonable cost. An alternative approach using finite nanoflake models opens the doors to many more advanced and accurate electronic structure methods, while sacrificing the realism of representation. Locating the surface‐analyte complex is followed by an in‐depth in silico analysis of its energetic and electronic properties using, for example, energy decomposition schemes, as well as simulation of the signal, for example, a zero‐bias transmission spectra or a current–voltage curve, by means of the nonequilibrium Green's function method. These and other properties are examined in the context of a sensor's selectivity, sensitivity, and limit of detection with an aim to establish design principles for future devices. Herein, we analyze the advantages and limitations of diverse computational chemistry methods used at each of these steps in simulating graphene‐based electrochemical sensors. We present outstanding challenges toward predictive models and sketch possible solutions involving such contemporary techniques as multiscale simulations and high‐throughput screening.

Authors: Anna Piras, Christopher Ehlert, Ganna Gryn'ova

Date Published: 3rd Mar 2021

Publication Type: Journal

Asteroseismology of luminous red giants with Kepler – II. Dependence of mass-loss on pulsations and radiation

Abstract (Expand)

Mass-loss by red giants is an important process to understand the final stages of stellar evolution and the chemical enrichment of the interstellar medium. Mass-loss rates are thought to be controlled … by pulsation-enhanced dust-driven outflows. Here, we investigate the relationships between mass-loss, pulsations, and radiation, using 3213 luminous Kepler red giants and 13 5000 ASAS-SN semiregulars and Miras. Mass-loss rates are traced by infrared colours using 2MASS and Wide-field Infrared Survey Explorer(WISE) and by observed-to-model WISE fluxes, and are also estimated using dust mass-loss rates from literature assuming a typical gas-to-dust mass ratio of 400. To specify the pulsations, we extract the period and height of the highest peak in the power spectrum of oscillation. Absolute magnitudes are obtained from the 2MASS Ks band and the Gaia DR2 parallaxes. Our results follow. (i) Substantial mass-loss sets in at pulsation periods above ∼60 and ∼100 d, corresponding to Asymptotic-Giant-Branch stars at the base of the period-luminosity sequences C' and C. (ii) The mass-loss rate starts to rapidly increase in semiregulars for which the luminosity is just above the red-giant-branch tip and gradually plateaus to a level similar to that of Miras. (iii) The mass-loss rates in Miras do not depend on luminosity, consistent with pulsation-enhanced dust-driven winds. (iv) The accumulated mass-loss on the red giant branch consistent with asteroseismic predictions reduces the masses of red-clump stars by 6.3 per cent, less than the typical uncertainty on their asteroseismic masses. Thus mass-loss is currently not a limitation of stellar age estimates for galactic archaeology studies.

Authors: Jie Yu, Saskia Hekker, Timothy R Bedding, Dennis Stello, Daniel Huber, Laurent Gizon, Shourya Khanna, Shaolan Bi

Date Published: 1st Mar 2021

Publication Type: Journal

A flat torus theorem for convex co-compact actions of projective linear groups

Abstract

Not specified

Authors: Mitul Islam, Andrew Zimmer

Date Published: 1st Mar 2021

Publication Type: Journal

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