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The Photometric objects Around Cosmic webs (PAC) approach developed in Xu et al. has the advantage of making full use of spectroscopic and deeper photometric surveys. With the merits of PAC, the excess surface density n ¯ 2 w p of neighboring galaxies can be measured down to stellar mass 1010.80 M ⊙ around quasars at redshift 0.8 < z s < 1.0, with the data from the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey and the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys. We find that n ¯ 2 w p generally increases quite steeply with the decrease of the separation. Using the subhalo abundance-matching method, we can accurately model the n ¯ 2 w p both on small and large scales. We show that the steep increase of n ¯ 2 w p toward the quasars requires that a large fraction f sate = 0.29 − 0.06 + 0.05 of quasars should be satellites in massive halos, and we find that this fraction measurement is insensitive to the assumptions of our modeling. This high satellite fraction indicates that the subhalos have nearly the same probability of hosting quasars as the halos for the same (infall) halo mass and that the large-scale environment has negligible effect on the quasar activity. We show that even with this high satellite fraction, each massive halo on average does not host more than one satellite quasar, due to the sparsity of quasars. [ABSTRACT FROM AUTHOR]

Purpose: We propose a convolutional neural network (CNN) based on wavelet for verifying the activation regions decided with statistical analysis. Because the functional magnetic resonance imaging (fMRI) data contains lots of noises, it is difficult to get the data of blood-oxygen-level dependent (BOLD) signal directly for intervention testing like animal studies. So it is difficult to effectively verify these activation regions. Based on the rapid development of deep learning technology. Materials and methods: We select the task fMRI data of presenting food and nonfood pictures to volunteer subjects from open public data, whose website is . Firstly, the brain activation regions are obtained by utilizing the method of statistical analysis. Then the spatial coordinates are acquired from the activation regions by checking the atlas table. The P-value of the activation regions are less 0.05. The activation regions are the most responsive to perceive the differences of BOLD in the brain between the two states, presenting food and nonfood pictures. We select the part task fMRI data of from the activation regions, for preparing the training and validation samples. Then we design a deep leaning network based on wavelet to classify the task fMRI data between food and nonfood. Results and conclusions: The classification accuracy is 80.23%. However, when we select the spatial coordinates of other inactivation regions, the classification accuracy is only 60%. The differences of classification accuracy between the activation regions and the inactivation regions prove that the activation regions selected with statistical analysis method are accurate and effective. The two methods of deep learning and statistical analysis can be cross-validated for the study of human being brain. [ABSTRACT FROM AUTHOR]