11,977 research outputs found
Small-variance asymptotics for Bayesian neural networks
Bayesian neural networks (BNNs) are a rich and flexible class of models that have several advantages over standard feedforward networks, but are typically expensive to train on large-scale data. In this thesis, we explore the use of small-variance asymptotics-an approach to yielding fast algorithms from probabilistic models-on various Bayesian neural network models. We first demonstrate how small-variance asymptotics shows precise connections between standard neural networks and BNNs; for example, particular sampling algorithms for BNNs reduce to standard backpropagation in the small-variance limit. We then explore a more complex BNN where the number of hidden units is additionally treated as a random variable in the model. While standard sampling schemes would be too slow to be practical, our asymptotic approach yields a simple method for extending standard backpropagation to the case where the number of hidden units is not fixed. We show on several data sets that the resulting algorithm has benefits over backpropagation on networks with a fixed architecture.2019-01-02T00:00:00
PhylOTU: a high-throughput procedure quantifies microbial community diversity and resolves novel taxa from metagenomic data.
Microbial diversity is typically characterized by clustering ribosomal RNA (SSU-rRNA) sequences into operational taxonomic units (OTUs). Targeted sequencing of environmental SSU-rRNA markers via PCR may fail to detect OTUs due to biases in priming and amplification. Analysis of shotgun sequenced environmental DNA, known as metagenomics, avoids amplification bias but generates fragmentary, non-overlapping sequence reads that cannot be clustered by existing OTU-finding methods. To circumvent these limitations, we developed PhylOTU, a computational workflow that identifies OTUs from metagenomic SSU-rRNA sequence data through the use of phylogenetic principles and probabilistic sequence profiles. Using simulated metagenomic data, we quantified the accuracy with which PhylOTU clusters reads into OTUs. Comparisons of PCR and shotgun sequenced SSU-rRNA markers derived from the global open ocean revealed that while PCR libraries identify more OTUs per sequenced residue, metagenomic libraries recover a greater taxonomic diversity of OTUs. In addition, we discover novel species, genera and families in the metagenomic libraries, including OTUs from phyla missed by analysis of PCR sequences. Taken together, these results suggest that PhylOTU enables characterization of part of the biosphere currently hidden from PCR-based surveys of diversity
Luminous Red Galaxies in Clusters: Central Occupation, Spatial Distributions, and Mis-centering
Luminous Red Galaxies (LRG) from the Sloan Digital Sky Survey are considered
among the best understood samples of galaxies, and they are employed in a broad
range of cosmological studies. Because they form a relatively homogeneous
population, with high stellar masses and red colors, they are expected to
occupy halos in a relatively simple way. In this paper, we study how LRGs
occupy massive halos via direct counts in clusters and we reveal several
unexpected trends suggesting that the connection between LRGs and dark matter
halos may not be straightforward. Using the redMaPPer cluster catalog, we
derive the central occupation of LRGs as a function richness, Ncen({\lambda}).
Assuming no correlation between cluster mass and central galaxy luminosity at
fixed richness, we show that clusters contain a significantly lower fraction of
central LRGs than predicted from the two-point correlation function. At halo
masses of 10^14.5 Msun, we find Ncen=0.73, compared to Ncen of 0.89 from
correlation studies. Our central occupation function for LRGs converges to 0.95
at large halo masses. A strong anti-correlation between central luminosity and
cluster mass at fixed richness is required to reconcile our results with those
based on clustering studies. We also derive P_BNC, the probability that the
brightest cluster member is not the central galaxy. We find P_BNC ~ 20-30%
which is a factor of ~2 lower than the value found by Skibba et al. 2011.
Finally, we study the radial offsets of bright non-central LRGs from cluster
centers and show that bright non-central LRGs follow a different radial
distribution compared to red cluster members, which follow a
Navarro-Frank-White profile. This work demonstrates that even the most massive
clusters do not always have an LRG at the center, and that the brightest galaxy
in a cluster is not always the central galaxy.Comment: 18 pages, 9 figures, 4 tables, submitted to MNRAS, included the
referee comment
AMICO galaxy clusters in KiDS-DR3: sample properties and selection function
We present the first catalogue of galaxy cluster candidates derived from the
third data release of the Kilo Degree Survey (KiDS-DR3). The sample of clusters
has been produced using the Adaptive Matched Identifier of Clustered Objects
(AMICO) algorithm. In this analysis AMICO takes advantage of the luminosity and
spatial distribution of galaxies only, not considering colours. In this way, we
prevent any selection effect related to the presence or absence of the
red-sequence in the clusters. The catalogue contains 7988 candidate galaxy
clusters in the redshift range 0.13.5 with a purity
approaching 95% over the entire redshift range. In addition to the catalogue of
galaxy clusters we also provide a catalogue of galaxies with their
probabilistic association to galaxy clusters. We quantify the sample purity,
completeness and the uncertainties of the detection properties, such as
richness, redshift, and position, by means of mock galaxy catalogues derived
directly from the data. This preserves their statistical properties including
photo-z uncertainties, unknown absorption across the survey, missing data,
spatial correlation of galaxies and galaxy clusters. Being based on the real
data, such mock catalogues do not have to rely on the assumptions on which
numerical simulations and semi-analytic models are based on. This paper is the
first of a series of papers in which we discuss the details and physical
properties of the sample presented in this work.Comment: 16 pages, 14 figures, 3 tables, submitted to MNRA
Technical Report: Distribution Temporal Logic: Combining Correctness with Quality of Estimation
We present a new temporal logic called Distribution Temporal Logic (DTL)
defined over predicates of belief states and hidden states of partially
observable systems. DTL can express properties involving uncertainty and
likelihood that cannot be described by existing logics. A co-safe formulation
of DTL is defined and algorithmic procedures are given for monitoring
executions of a partially observable Markov decision process with respect to
such formulae. A simulation case study of a rescue robotics application
outlines our approach.Comment: More expanded version of "Distribution Temporal Logic: Combining
Correctness with Quality of Estimation" to appear in IEEE CDC 201
Technical report: Distribution Temporal Logic: combining correctness with quality of estimation
We present a new temporal logic called Distribution Temporal Logic (DTL) defined over predicates of belief states and hidden states of partially observable systems. DTL can express properties involving uncertainty and likelihood that cannot be described by existing logics. A co-safe formulation of DTL is defined and algorithmic procedures are given for monitoring executions of a partially observable Markov decision process with respect to such formulae. A simulation case study of a rescue robotics application outlines our approach
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