Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model

Recently brain networks have been widely adopted to study brain dynamics, brain development and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. Firstly, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits the model performance on clinical phenotypes predictions. Moreover, few of current graph learning model is interpretable, which may not be capable to provide biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. In order to further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning. We evaluate this framework on different classification and regression tasks using the data from HCP and OASIS. Our results from extensive experiments demonstrate the superiority of the proposed model compared to several state-of-the-art techniques. Additionally, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers

New Supernova Candidates from SDSS-DR7 of Spectral Survey

The letter presents 25 discovered supernova candidates from SDSS-DR7 with our dedicated method, called Sample Decrease, and 10 of them were confirmed by other research groups, and listed in this letter. Another 15 are first discovered including 14 type Ia and one type II based on Supernova Identification (SNID) analysis. The results proved that our method is reliable, and the description of the method and some detailed spectra analysis procedures were also presented in this letter.Comment: 6 pages, 3 figure

3D bi-directional transformer U-Net for medical image segmentation

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics

WISDOM Project -- XV. Giant Molecular Clouds in the Central Region of the Barred Spiral Galaxy NGC 5806

We present high spatial resolution ($\approx24$ pc) Atacama Large Millimeter/sub-millimeter Array $^{12}$CO(2-1) observations of the central region of the nearby barred spiral galaxy NGC 5806. NGC 5806 has a highly structured molecular gas distribution with a clear nucleus, a nuclear ring and offset dust lanes. We identify $170$ spatially- and spectrally-resolved giant molecular clouds (GMCs). These clouds have comparable sizes ($R_{\mathrm{c}}$) and larger gas masses, observed linewidths ($\sigma_{\mathrm{obs,los}}$) and gas mass surface densities than those of clouds in the Milky Way disc. The size -- linewidth relation of the clouds is one of the steepest reported so far ($\sigma_{\mathrm{obs,los}}\propto R_{\mathrm{c}}^{1.20}$), the clouds are on average only marginally bound (with a mean virial parameter $\langle\alpha_{\mathrm{vir}}\rangle\approx2$), and high velocity dispersions are observed in the nuclear ring. These behaviours are likely due to bar-driven gas shocks and inflows along the offset dust lanes, and we infer an inflow velocity of $\approx120$ kms$^{-1}$ and a total molecular gas mass inflow rate of $\approx5$ M$_\odot$ yr$^{-1}$ into the nuclear ring. The observed internal velocity gradients of the clouds are consistent with internal turbulence. The number of clouds in the nuclear ring decreases with azimuthal angle downstream from the dust lanes without clear variation of cloud properties. This is likely due to the estimated short lifetime of the clouds ($\approx6$ Myr), which appears to be mainly regulated by cloud-cloud collision and/or shear processes. Overall, it thus seems that the presence of the large-scale bar and gas inflows to the centre of NGC 5806 affect cloud properties.Comment: Accepted for publication in MNRAS, 20 pages, 16 figure

Estimating dust attenuation from galactic spectra : II. stellar and gas attenuation in star-forming and diffuse ionized gas regions in MaNGA

We investigate the dust attenuation in both stellar populations and ionized gas in kiloparsec-scale regions in nearby galaxies using integral field spectroscopy data from MaNGA MPL-9. We identify star-forming (H II) and diffuse ionized gas (DIG) regions from MaNGA data cubes. From the stacked spectrum of each region, we measure the stellar attenuation, E( ) B V - star, using the technique developed by Li et al., as well as the gas attenuation, E( ) B V - gas, from the Balmer decrement. We then examine the correlation of E( ) B V - star, E( ) B V - gas, E() () B V EB V - -- gas star, and E( )( ) B V EB V - - star gas with 16 regional/global properties, and for regions with different Hα surface brightnesses (ΣHα). We find a stronger correlation between E( ) B V - star and E( ) B V - gas in regions of higher ΣHα. The luminosity-weighted age (tL) is found to be the property that is the most strongly correlated with E( ) B V - star, and consequently, with E() () B V EB V - -- gas star and E( )( ) B V EB V - - star gas. At fixed ΣHα, log10tL is linearly and negatively correlated with E( ) B V - star E( ) B V - gas at all ages. Gas-phase metallicity and ionization level are important for the attenuation in the gas. Our results indicate that the ionizing source for DIG regions is likely distributed in the outskirts of galaxies, while for H II regions, our results can be well explained by the two-component dust model of Charlot & Fall

WISDOM Project – XIX. Figures of merit for supermassive black hole mass measurements using molecular gas and/or megamaser kinematics

The mass (MBH) of a supermassive black hole (SMBH) can be measured using spatially-resolved kinematics of the region where the SMBH dominates gravitationally. The most reliable measurements are those that resolve the smallest physical scales around the SMBHs. We consider here three metrics to compare the physical scales probed by kinematic tracers dominated by rotation: the radius of the innermost detected kinematic tracer Rmin normalised by respectively the SMBH’s Schwarzschild radius (RSchw ≡ 2GMBH/c2, where G is the gravitational constant and c the speed of light), sphere-of-influence (SOI) radius ($R_\mathrm{SOI}\equiv GM_\mathrm{BH}/\sigma _\mathrm{e}^2$, where σe is the stellar velocity dispersion within the galaxy’s effective radius) and equality radius (the radius Req at which the SMBH mass equals the enclosed stellar mass, MBH = M*(Req), where M*(R) is the stellar mass enclosed within the radius R). All metrics lead to analogous simple relations between Rmin and the highest circular velocity probed Vc. Adopting these metrics to compare the SMBH mass measurements using molecular gas kinematics to those using megamaser kinematics, we demonstrate that the best molecular gas measurements resolve material that is physically closer to the SMBHs in terms of RSchw but is slightly farther in terms of RSOI and Req. However, molecular gas observations of nearby galaxies using the most extended configurations of the Atacama Large Millimeter/sub-millimeter Array can resolve the SOI comparably well and thus enable SMBH mass measurements as precise as the best megamaser measurements