A Review of the Theory of Galactic Winds Driven by Stellar Feedback

Galactic winds from star-forming galaxies are crucial to the process of galaxy formation and evolution, regulating star formation, shaping the stellar mass function and the mass-metallicity relation, and enriching the intergalactic medium with metals. Galactic winds associated with stellar feedback may be driven by overlapping supernova explosions, radiation pressure of starlight on dust grains, and cosmic rays. Galactic winds are multiphase, the growing observations of emission and absorption of cold molecular, cool atomic, ionized warm and hot outflowing gas in a large number of galaxies have not been completely understood. In this review article, I summarize the possible mechanisms associated with stars to launch galactic winds, and review the multidimensional hydrodynamic, radiation hydrodynamic and magnetohydrodynamic simulations of winds based on various algorithms. I also briefly discuss the theoretical challenges and possible future research directions.Comment: 47 pages, 7 figures. Accepted for publication in the special issue of Galaxie

Laplacian coefficients of unicyclic graphs with the number of leaves and girth

Let $G$ be a graph of order $n$ and let $\mathcal{L}(G,\lambda)=\sum_{k=0}^n (-1)^{k}c_{k}(G)\lambda^{n-k}$ be the characteristic polynomial of its Laplacian matrix. Motivated by Ili\'{c} and Ili\'{c}'s conjecture [A. Ili\'{c}, M. Ili\'{c}, Laplacian coefficients of trees with given number of leaves or vertices of degree two, Linear Algebra and its Applications 431(2009)2195-2202.] on all extremal graphs which minimize all the Laplacian coefficients in the set $\mathcal{U}_{n,l}$ of all $n$-vertex unicyclic graphs with the number of leaves $l$, we investigate properties of the minimal elements in the partial set $(\mathcal{U}_{n,l}^g, \preceq)$ of the Laplacian coefficients, where $\mathcal{U}_{n,l}^g$ denote the set of $n$-vertex unicyclic graphs with the number of leaves $l$ and girth $g$. These results are used to disprove their conjecture. Moreover, the graphs with minimum Laplacian-like energy in $\mathcal{U}_{n,l}^g$ are also studied.Comment: 19 page, 4figure

The Roots and Links in a Class of MM-Matrices

In this paper, we discuss exiting roots of sub-kernel transient matrices $P$ associated with a class of $M-$ matrices which are related to generalized ultrametric matrices. Then the results are used to describe completely all links of the class of matrices in terms of structure of the supporting tree.Comment: 11 pages, 1 figur

clcNet: Improving the Efficiency of Convolutional Neural Network using Channel Local Convolutions

Depthwise convolution and grouped convolution has been successfully applied to improve the efficiency of convolutional neural network (CNN). We suggest that these models can be considered as special cases of a generalized convolution operation, named channel local convolution(CLC), where an output channel is computed using a subset of the input channels. This definition entails computation dependency relations between input and output channels, which can be represented by a channel dependency graph(CDG). By modifying the CDG of grouped convolution, a new CLC kernel named interlaced grouped convolution (IGC) is created. Stacking IGC and GC kernels results in a convolution block (named CLC Block) for approximating regular convolution. By resorting to the CDG as an analysis tool, we derive the rule for setting the meta-parameters of IGC and GC and the framework for minimizing the computational cost. A new CNN model named clcNet is then constructed using CLC blocks, which shows significantly higher computational efficiency and fewer parameters compared to state-of-the-art networks, when being tested using the ImageNet-1K dataset. Source code is available at https://github.com/dqzhang17/clcnet.torch

Large deviations for quasilinear parabolic stochastic partial differential equations

In this paper, we establish the Freidlin-Wentzell's large deviations for quasilinear parabolic stochastic partial differential equations with multiplicative noise, which are neither monotone nor locally monotone. The proof is based on the weak convergence approach