Analytical controllability of deterministic scale-free networks and Cayley trees

According to the exact controllability theory, the controllability is investigated analytically for two typical types of self-similar bipartite networks, i.e., the classic deterministic scale-free networks and Cayley trees. Due to their self-similarity, the analytical results of the exact controllability are obtained, and the minimum sets of driver nodes (drivers) are also identified by elementary transformations on adjacency matrices. For these two types of undirected networks, no matter their links are unweighted or (nonzero) weighted, the controllability of networks and the configuration of drivers remain the same, showing a robustness to the link weights. These results have implications for the control of real networked systems with self-similarity.Comment: 7 pages, 4 figures, 1 table; revised manuscript; added discussion about the general case of DSFN; added 3 reference

Molecular Lines of 13 Galactic Infrared Bubble Regions

We investigated the physical properties of molecular clouds and star formation processes around infrared bubbles which are essentially expanding HII regions. We performed observations of 13 galactic infrared bubble fields containing 18 bubbles. Five molecular lines, 12CO (J=1-0), 13CO (J=1-0), C18O(J=1-0), HCN (J=1-0), and HCO+ (J=1-0), were observed, and several publicly available surveys, GLIMPSE, MIPSGAL, ATLASGAL, BGPS, VGPS, MAGPIS, and NVSS, were used for comparison. We find that these bubbles are generally connected with molecular clouds, most of which are giant. Several bubble regions display velocity gradients and broad shifted profiles, which could be due to the expansion of bubbles. The masses of molecular clouds within bubbles range from 100 to 19,000 solar mass, and their dynamic ages are about 0.3-3.7 Myr, which takes into account the internal turbulence pressure of surrounding molecular clouds. Clumps are found in the vicinity of all 18 bubbles, and molecular clouds near four of these bubbles with larger angular sizes show shell-like morphologies, indicating that either collect-and-collapse or radiation-driven implosion processes may have occurred. Due to the contamination of adjacent molecular clouds, only six bubble regions are appropriate to search for outflows, and we find that four of them have outflow activities. Three bubbles display ultra-compact HII regions at their borders, and one of them is probably responsible for its outflow. In total, only six bubbles show star formation activities in the vicinity, and we suggest that star formation processes might have been triggered.Comment: 55 Pages, 32 figures. Accepted for publication in A

Training-Time-Friendly Network for Real-Time Object Detection

Modern object detectors can rarely achieve short training time, fast inference speed, and high accuracy at the same time. To strike a balance among them, we propose the Training-Time-Friendly Network (TTFNet). In this work, we start with light-head, single-stage, and anchor-free designs, which enable fast inference speed. Then, we focus on shortening training time. We notice that encoding more training samples from annotated boxes plays a similar role as increasing batch size, which helps enlarge the learning rate and accelerate the training process. To this end, we introduce a novel approach using Gaussian kernels to encode training samples. Besides, we design the initiative sample weights for better information utilization. Experiments on MS COCO show that our TTFNet has great advantages in balancing training time, inference speed, and accuracy. It has reduced training time by more than seven times compared to previous real-time detectors while maintaining state-of-the-art performances. In addition, our super-fast version of TTFNet-18 and TTFNet-53 can outperform SSD300 and YOLOv3 by less than one-tenth of their training time, respectively. The code has been made available at \url{https://github.com/ZJULearning/ttfnet}.Comment: Accepted to AAAI2020 (8 pages, 3 figures

Quantum-Mechanical Investigation of Field-Emission Mechanism of a Micrometer-Long Single-Walled Carbon Nanotube

The charge distribution and electrostatic potential along 1 μm long single-walled carbon nanotube were investigated using quantum mechanical simulations under realistic field-emission experimental conditions. Single layer of carbon atoms were found to shield electric field at the tip where field penetration occurred strongly. This strong penetration resulted to nonlinear decrease of potential barrier for emission. This was found to be responsible for low threshold voltage other than well-known geometrical field enhancement factor.published_or_final_versio

An association study of NRAMP1, VDR, MBL and their interaction with the susceptibility to tuberculosis in a Chinese population

SummaryObjectivesTo investigate natural-resistance-associated macrophage protein 1 (NRAMP1), mannose-binding lectin (MBL), vitamin D receptor (VDR) gene polymorphisms and their interaction with susceptibility to pulmonary tuberculosis (PTB) in a Chinese population.MethodsA case-control study was conducted in PTB (n=151), age- and sex- matched healthy controls (HCs) (n=453). Genetic polymorphisms of NRAMP1 (INT4, D543NA and 3′UTR), MBL (HL, PQ, XY and AB) and VDR (FokI and Taq) were analyzed by using PCR-restriction fragment length polymorphism (RFLP) and PCR- single- strand conformation polymorphism (SSCP) techniques. Multifactor dimensionality reduction (MDR) analysis was carried out to assess the effects of the interaction between SNPs.ResultsThe distribution of NRAMP1- 3′UTR (TGTG/del), MBL- HL (H/L) and FokI (F/f) were significantly different between PTB patients and HCs (p<0.05). HPYA (OR: 1.88; 95% CI: 1.22-2.91), LPXA (OR: 3.17; 95% CI: 1.69- 5.96), LQYA (OR: 3.52; 95%CI: 1.50-8.23) and LPYB (OR: 12.37; 95%CI: 3.75- 40.85) of MBL were risk haplotypes for PTB. The TGTG- H- f (OR: 1.70; 95%CI: 1.10-2.62) and del- H-f (OR: 3.48; 95% CI: 1.45-8.37) of 3′UTR- HL- FokI were also high-risk haplotypes associated with tuberculosis.ConclusionsOur study suggests that genotypes of many polymorphic genes are associated with TB, it is necessary to further explore the mechanism of genotypes and gene-gene interaction in susceptibility to tuberculosis

Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images

Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This issue primarily stems from the model being trained on pairs of single-scale images and their corresponding text descriptions. Moreover, direct training on images of unlimited sizes is unfeasible, as it would require an immense number of text-image pairs and entail substantial computational expenses. To overcome these challenges, we propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to efficiently generate well-composed images of any size, while minimizing the need for high-memory GPU resources. Specifically, the initial stage, dubbed Any Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a restricted range of ratios to optimize the text-conditional diffusion model, thereby improving its ability to adjust composition to accommodate diverse image sizes. To support the creation of images at any desired size, we further introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the subsequent stage. This method allows for the rapid enlargement of the ASD output to any high-resolution size, avoiding seaming artifacts or memory overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks demonstrate that ASD can produce well-structured images of arbitrary sizes, cutting down the inference time by 2x compared to the traditional tiled algorithm