Self-Supervised Audio-Visual Co-Segmentation

Segmenting objects in images and separating sound sources in audio are challenging tasks, in part because traditional approaches require large amounts of labeled data. In this paper we develop a neural network model for visual object segmentation and sound source separation that learns from natural videos through self-supervision. The model is an extension of recently proposed work that maps image pixels to sounds. Here, we introduce a learning approach to disentangle concepts in the neural networks, and assign semantic categories to network feature channels to enable independent image segmentation and sound source separation after audio-visual training on videos. Our evaluations show that the disentangled model outperforms several baselines in semantic segmentation and sound source separation.Comment: Accepted to ICASSP 201

DIVE in the cosmic web: voids with Delaunay Triangulation from discrete matter tracer distributions

We present a novel parameter-free cosmological void finder (\textsc{dive}, Delaunay TrIangulation Void findEr) based on Delaunay Triangulation (DT), which efficiently computes the empty spheres constrained by a discrete set of tracers. We define the spheres as DT voids, and describe their properties, including an universal density profile together with an intrinsic scatter. We apply this technique on 100 halo catalogues with volumes of 2.5\,$h^{-1}$Gpc side each, with a bias and number density similar to the BOSS CMASS Luminous Red Galaxies, performed with the \textsc{patchy} code. Our results show that there are two main species of DT voids, which can be characterised by the radius: they have different responses to halo redshift space distortions, to number density of tracers, and reside in different dark matter environments. Based on dynamical arguments using the tidal field tensor, we demonstrate that large DT voids are hosted in expanding regions, whereas the haloes used to construct them reside in collapsing ones. Our approach is therefore able to efficiently determine the troughs of the density field from galaxy surveys, and can be used to study their clustering. We further study the power spectra of DT voids, and find that the bias of the two populations are different, demonstrating that the small DT voids are essentially tracers of groups of haloes.Comment: 12 pages, 13 figure

The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: a tomographic analysis of structure growth and expansion rate from anisotropic galaxy clustering

We perform a tomographic analysis of structure growth and expansion rate from the anisotropic galaxy clustering of the combined sample of Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 12, which covers the redshift range of $0.2<z<0.75$. In order to extract the redshift information of anisotropic galaxy clustering, we analyse this data set in nine overlapping redshift slices in configuration space and perform the joint constraints on the parameters $(D_V, F_{\mathrm{AP}}, f\sigma_8)$ using the correlation function multipoles. The analysis pipeline is validated using the MultiDark-Patchy mock catalogues. We obtain a measurement precision of $1.5\%-2.9\%$ for $D_V$, $5.2\%-9\%$ for $F_{\mathrm{AP}}$ and $13.3\%-24\%$ for $f \sigma_8$, depending on the effective redshift of the slices. We report a joint measurement of $(D_V, F_{\mathrm{AP}}, f\sigma_8)$ with the full covariance matrix in nine redshift slices. We use our joint BAO and RSD measurement combined with external datasets to constrain the gravitational growth index $\gamma$, and find $\gamma=0.656 \pm 0.057$, which is consistent with the $\Lambda$CDM prediction within 95\% CL.Comment: 8 pages, 8 figures, 2 tables, accepted for publication MNRAS. The measured results including the full covariance matrices are made available at https://github.com/ytcosmo/TomoBAORSD and tomographic clustering data used in this work is available at https://sdss3.org//science/boss_publications.ph