Discovery of Community Structures in Static and Dynamic Networks

With the development of computer technology, researchers are able to observe and collect enormous amount of data, where the independent and identical distributed assumption is violated. For example, in sociology, individuals in an organization interact with each other to change the underlying social structure; in biology, understanding the gene-gene interaction helps researchers to detect potential diseases; in politics, voters are mutually influenced before the election via private/public speeches and parades, which might ultimately change the election results. It is crucial to study how individuals interact with each other from the data, which would lead to tremendous contributions to the society. Centuries ago, mathematicians started to describe the interaction of objects with mathematical language in the field of graph theory. The concepts of vertices/nodes and edges are the cornerstone of graph theory. Vertex can be used to describe individual, and edge is a way to portray interaction between a pair of vertices. Taking advantage of the accumulated discoveries in graph theory, statisticians are able to develop stochastic models to make inference of the data, which can be represented by network structures. My main research goal is to develop statistical models to discover the underlying community structure in various types of network data, including a snap shot of a network and time-varying network. The word community is an intermediate concept between a single node and the whole network, and can refer to a partition, a block structure, etc. Additionally, I desire to make my models be feasible to large size data, so that gigantic networks, e.g. social network, can be analyzed using my contributed methodologies. Spectral clustering type of methods, which usually require less computational resources, are proposed to achieve the research goal. I first explore the methodologies of discovering community structure under an unobserved latent space by shrinking the latent positions of nodes belonging to the same community. Unlike traditional community detection algorithms, the information of edge covariates are taken into consideration for better estimation. I apply the proposed algorithm on an attorney friendship network to check the correlation between friendship status and office location. I am also interested in analyzing dynamic network data, where a series of networks are observed. For example, the friendship between the same group of undergraduate students are different in the forth year comparing to the first year. One way to detect communities with dynamic network is to treat network on each time point independently. It is convenient, however, historical information (e.g. the network or community structure in the previous time points), which has potential to improve the estimation accuracy, is ignored. I build an algorithm to borrow the historical information and improve the clustering quality with the help of degree of nodes

An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features, and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches

Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection

Co-saliency detection aims to discover the common and salient foregrounds from a group of relevant images. For this task, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC). Three major contributions have been made, and are experimentally shown to have substantial practical merits. First, we propose a graph convolutional network design to extract information cues to characterize the intra- and interimage correspondence. Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion. Third, we present a unified framework with encoder-decoder structure to jointly train and optimize the graph convolutional network, attention graph cluster, and co-saliency detection decoder in an end-to-end manner. We evaluate our proposed GCAGC method on three cosaliency detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method obtains significant improvements over the state-of-the-arts on most of them.Comment: CVPR202

A Unified Framework for Guiding Generative AI with Wireless Perception in Resource Constrained Mobile Edge Networks

With the significant advancements in artificial intelligence (AI) technologies and powerful computational capabilities, generative AI (GAI) has become a pivotal digital content generation technique for offering superior digital services. However, directing GAI towards desired outputs still suffer the inherent instability of the AI model. In this paper, we design a novel framework that utilizes wireless perception to guide GAI (WiPe-GAI) for providing digital content generation service, i.e., AI-generated content (AIGC), in resource-constrained mobile edge networks. Specifically, we first propose a new sequential multi-scale perception (SMSP) algorithm to predict user skeleton based on the channel state information (CSI) extracted from wireless signals. This prediction then guides GAI to provide users with AIGC, such as virtual character generation. To ensure the efficient operation of the proposed framework in resource constrained networks, we further design a pricing-based incentive mechanism and introduce a diffusion model based approach to generate an optimal pricing strategy for the service provisioning. The strategy maximizes the user's utility while enhancing the participation of the virtual service provider (VSP) in AIGC provision. The experimental results demonstrate the effectiveness of the designed framework in terms of skeleton prediction and optimal pricing strategy generation comparing with other existing solutions

An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning

As a noninvasive and "task-free" technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks

Hot deformation behavior and processing maps of diamond/Cu composites

The hot deformation behaviors of 50 vol pct uncoated and Cr-coated diamond/Cu composites were investigated using hot isothermal compression tests under the temperature and strain rate ranging from 1073 K to 1273 K (800 C to 1000 C) and from 0.001 to 5 s1, respectively. Dynamic recrystallization was determined to be the primary restoration mechanism during deformation. The Cr3C2 coating enhanced the interfacial bonding and resulted in a larger flow stress for the Cr-coated diamond/Cu composites. Moreover, the enhanced interfacial affinity led to a higher activation energy for the Cr-coated diamond/Cu composites (238 kJ/mol) than for their uncoated counterparts (205 kJ/mol). The strain-rate-dependent constitutive equations of the diamond/Cu composites were derived based on the Arrhenius model, and a high correlation (R = 0.99) was observed between the calculated flow stresses and experimental data. With the help of processing maps, hot extrusions were realized at 1123 K/0.01 s1 and 1153 K/0.01 s1 (850 C/0.01 s1 and 880 C/0.01 s1) for the uncoated and coated diamond/Cu composites, respectively. The combination of interface optimization and hot extrusion led to increases of the density and thermal conductivity, thereby providing a promising route for the fabrication of diamond/Cu composites