Bayesian stochastic blockmodels for community detection in networks and community-structured covariance selection

Networks have been widely used to describe interactions among objects in diverse fields. Given the interest in explaining a network by its structure, much attention has been drawn to finding clusters of nodes with dense connections within clusters but sparse connections between clusters. Such clusters are called communities, and identifying such clusters is known as community detection. Here, to perform community detection, I focus on stochastic blockmodels (SBM), a class of statistically-based generative models. I present a flexible SBM that represents different types of data as well as node attributes under a Bayesian framework. The proposed models explicitly capture community behavior by guaranteeing that connections are denser within communities than between communities. First, I present a degree-corrected SBM based on a logistic regression formulation to model binary networks. To fit the model, I obtain posterior samples via Gibbs sampling based on Polya-Gamma latent variables. I conduct inference based on a novel, canonically mapped centroid estimator that formally addresses label non-identifiability and captures representative community assignments. Next, to accommodate large-scale datasets, I further extend the degree-corrected SBM to a broader family of generalized linear models with group correction terms. To conduct exact inference efficiently, I develop an iteratively-reweighted least squares procedure that implicitly updates sufficient statistics on the network to obtain maximum a posteriori (MAP) estimators. I demonstrate the proposed model and estimation on simulated benchmark networks and various real-world datasets. Finally, I develop a Bayesian SBM for community-structured covariance selection. Here, I assume that the data at each node are Gaussian and a latent network where two nodes are not connected if their observations are conditionally independent given observations of other nodes. Under the context of biological and social applications, I expect that this latent network shows a block dependency structure that represents community behavior. Thus, to identify the latent network and detect communities, I propose a hierarchical prior in two levels: a spike-and-slab prior on off-diagonal entries of the concentration matrix for variable selection and a degree-corrected SBM to capture community behavior. I develop an efficient routine based on ridge regularization and MAP estimation to conduct inference

Image-Guided Hypofractionated Radiosurgery of Large and Complex Brain Lesions

Hypofractionated radiosurgery either through frame or image guidance has emerged as the most important area of research and development for intracranial and extracranial radiosurgery. In this chapter, we focused on discussions of three state-of-the-art platforms: Frame- and Image-Guided Gamma Knife, Robotic X-Band Cykerknife, and Flattening-Filter-Free intensity-modulated S-band medical linear accelerators. Practical principles with detailed workflow and clinical implementations are presented in a systematic approach. With rapid evolvement of both hardware and software in the realm of delivering hypofractionated radiosurgery, this chapter aims to offer a reader physical clarity on judging and balancing of achieving high-precision and high-quality treatments with practical examples and guidelines on intracranial applications

Multi-modality Empowered Network For Facial Action Unit Detection

This paper presents a new thermal empowered multi-task network (TEMT-Net) to improve facial action unit detection. Our primary goal is to leverage the situation that the training set has multi-modality data while the application scenario only has one modality. Thermal images are robust to illumination and face color. In the proposed multi-task framework, we utilize both modality data. Action unit detection and facial landmark detection are correlated tasks. To utilize the advantage and the correlation of different modalities and different tasks, we propose a novel thermal empowered multi-task deep neural network learning approach for action unit detection, facial landmark detection and thermal image reconstruction simultaneously. The thermal image generator and facial landmark detection provide regularization on the learned features with shared factors as the input color images. Extensive experiments are conducted on the BP4D and MMSE databases, with the comparison to the state-of-the-art methods. The experiments show that the multi-modality framework improves the AU detection significantly

Research and Development of Carbon Footprint Analysis In Hunan Province

AbstractBased on the definition of carbon footprint and elements that affect it, a model was constructed for empirical research, using data of cities in Hunan Province from 2005 to 2009, to calculate the amount of carbon footprint and to analyze the relationships between carbon footprint and each of the elements, including population, level of economic development, industrial structure and energy structure. In addition, this paper also puts forward solutions to further low-carbon development of Hunan Province in the areas of low-carbon development mechanism energy structure low carbon life style and talent development

Graphene-Based Terahertz Holographic Conformal Antenna

In this paper, a conformal graphene holographic antenna designed for terahertz (THz) band is proposed. The radiation principle of the proposed pattern reconfigurable antenna is based on the holographic technology. The surface reactance modulation and pattern steering capability can be easily facilitated by a tunable DC-biased graphene patch array. Thanks to the super thin structure and excellent mechanical property of graphene, the proposed THz graphene holographic antenna can be designed conformal to required platforms easily. Besides, the equal size as well as same spacing of graphene patches make it easy to modeling and manufacture. To verify the proposed idea, an antenna conformal to a cylinder is designed and simulated. The results of full wave simulation software HFSS shown that the conformal antenna has great performance