Towards unsupervised segmentation in high-resolution medical nano-imaging

Recent advances in cellular and subcellular microscopy demonstrated its potential towards unraveling the mechanisms of various diseases at the molecular level. From a computer vision perspective nano-imaging is an inherently complex environment as can for example be seen from Fig.1(a,c). For the image analysis of intracellular organisms in high-resolution microscopy, new techniques which are capable of handling high-throughput data in a single pass and real time are of special interest. The additional emphasis is put therein on automated solutions which can provide the objective quantitative information in a reasonable time frame. The state-of-the-art is dominated by manual data annotation[1]and the early attempts to automate the segmentation are based on statistical machine-learning techniques[4]

Understanding Higher-Order Nonlocal Halo Bias at Large Scales by Combining the Power Spectrum with the Bispectrum

Understanding the relation between underlying matter distribution and biased tracers such as galaxies or dark matter halos is essential to extract cosmological information from ongoing or future galaxy redshift surveys. At sufficiently large scales such as the baryon acoustic oscillation (BAO) scale, a standard approach for the bias problem on the basis of the perturbation theory (PT) is to assume the local bias model in which the density field of biased tracers is deterministically expanded in terms of matter density field at the same position. The higher-order bias parameters are then determined by combining the power spectrum with higher-order statistics such as the bispectrum. As is pointed out by recent studies, however, nonlinear gravitational evolution naturally induces nonlocal bias terms even if initially starting only with purely local bias. As a matter of fact, previous works showed that the second-order nonlocal bias term, which corresponds to the gravitational tidal field, is important to explain the characteristic scale-dependence of the bispectrum. In this paper we extend the nonlocal bias term up to third order, and investigate whether the PT-based model including nonlocal bias terms can simultaneously explain the power spectrum and the bispectrum of simulated halos in N-body simulations. The bias renormalization procedure ensures that only one additional term is necessary to be introduced to the power spectrum as a next-to-leading order correction, even if third-order nonlocal bias terms are taken into account. We show that the power spectrum, including density and momentum, and the bispectrum between halo and matter in N-body simulations can be simultaneously well explained by the model including up to third-order nonlocal bias terms at k ≲ 0.1h/Mpc. Also, the results are in a good agreement with theoretical predictions of a simple coevolution picture, although the agreement is not perfect. These trend can be found for a wide range of halo mass, 0.7 ≲ Mhalo[1013M⊙/h] ≲ 20 at various redshifts, 0 ≤ z ≤ 1. These demonstrations clearly show a failure of the local bias model even at such large scales, and we conclude that nonlocal bias terms should be consistently included in order to accurately model statistics of halos