Hidden Tree Structure is a Key to the Emergence of Scaling in the World Wide Web

Preferential attachment is the most popular explanation for the emergence of scaling behavior in the World Wide Web, but this explanation has been challenged by the global information hypothesis, the existence of linear preference and the emergence of new big internet companies in the real world. We notice that most websites have an obvious feature that their pages are organized as a tree (namely hidden tree) and hence propose a new model that introduces a hidden tree structure into the Erd\H{o}s-R\'e}yi model by adding a new rule: when one node connects to another, it should also connect to all nodes in the path between these two nodes in the hidden tree. The experimental results show that the degree distribution of the generated graphs would obey power law distributions and have variable high clustering coefficients and variable small average lengths of shortest paths. The proposed model provides an alternative explanation to the emergence of scaling in the World Wide Web without the above-mentioned difficulties, and also explains the "preferential attachment" phenomenon.Comment: 4 Pages, 7 Figure

3D Radiation Hydrodynamic Simulations of Gravitational Instability in AGN Accretion Disks: Effects of Radiation Pressure

We perform 3D radiation hydrodynamic local shearing box simulations to study the outcome of gravitational instability (GI) in optically thick Active Galactic Nuclei (AGN) accretion disks. GI develops when the Toomre parameter QT \leq 1, and may lead to turbulent heating that balances radiative cooling. However, when radiative cooling is too efficient, the disk may undergo runaway gravitational fragmentation. In the fully gas-pressure-dominated case, we confirm the classical result that such a thermal balance holds when the Shakura-Sunyaev viscosity parameter (alpha) due to the gravitationally-driven turbulence is \sim 0.2, corresponding to dimensionless cooling times Omega tcool \sim 5. As the fraction of support by radiation pressure increases, the disk becomes more prone to fragmentation, with a reduced (increased) critical value of alpha (omega tcool). The effect is already significant when the radiation pressure exceeds 10% of the gas pressure, while fully radiation-pressure-dominated disks fragment at Omega tcool <50 . The latter translates to a maximum turbulence level alpha<0.02, comparable to that generated by Magnetorotational Instability (MRI). Our results suggest that gravitationally unstable (QT \sim 1) outer regions of AGN disks with significant radiation pressure (likely for high/near- Eddington accretion rates) should always fragment into stars, and perhaps black holes.Comment: 26 pages, 19 figures, ApJ in Pres

PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation

Today, scene graph generation(SGG) task is largely limited in realistic scenarios, mainly due to the extremely long-tailed bias of predicate annotation distribution. Thus, tackling the class imbalance trouble of SGG is critical and challenging. In this paper, we first discover that when predicate labels have strong correlation with each other, prevalent re-balancing strategies(e.g., re-sampling and re-weighting) will give rise to either over-fitting the tail data(e.g., bench sitting on sidewalk rather than on), or still suffering the adverse effect from the original uneven distribution(e.g., aggregating varied parked on/standing on/sitting on into on). We argue the principal reason is that re-balancing strategies are sensitive to the frequencies of predicates yet blind to their relatedness, which may play a more important role to promote the learning of predicate features. Therefore, we propose a novel Predicate-Correlation Perception Learning(PCPL for short) scheme to adaptively seek out appropriate loss weights by directly perceiving and utilizing the correlation among predicate classes. Moreover, our PCPL framework is further equipped with a graph encoder module to better extract context features. Extensive experiments on the benchmark VG150 dataset show that the proposed PCPL performs markedly better on tail classes while well-preserving the performance on head ones, which significantly outperforms previous state-of-the-art methods.Comment: To be appeared on ACMMM 202

Impact of sphingomyelin levels on coronary heart disease and left ventricular systolic function in humans

Sphingomyelin (SM) is an abundant phospholipid in cell membranes and in lipoproteins. In human plasma, SM is mainly found in atherogenic lipoproteins; therefore, higher levels of SM may promote atherogenesis. We investigated the relations between plasma SM levels and the presence of angiographic coronary heart disease (CHD) and left ventricular systolic dysfunction. We studied 732 patients referred for coronary angiography. Median SM levels were higher among patients with CHD and in those with LV systolic dysfunction (LVEF<50%) than in patients without CHD or LV dysfunction. SM levels were significantly correlated with fibrinogen levels, diabetes, apoB, and triglyceride levels. On multivariate analyses, higher median SM levels were associated with a higher risk of CHD and lower LV ejection fraction. The pro-atherogenic property of plasma SM might be related to 1) CHD; 2) LV systolic dysfunction; and 3) metabolism of apoB-containing or triglyceride-rich lipoproteins