arXiv subjects

Xiaoyan Lu

Publications and source records attributed to Xiaoyan Lu.

10 recordsLinked to original sources

arXiv2026

Mechanically Assisted Symmetry Reconstruction for Extraordinary Piezoelectricity

Active symmetry control - a central challenge in materials science, particularly in ferroelectrics - is achieved via mechanically assisted poling (MAP) guided by thermodynamics and phase - field modeling. This approach yields extraordinary piezoelectric coefficients (about 5,000 pC/N at 24 degC; 11,700 pC/N at 58 degC) together with about 65% optical transmittance in a classic relaxor ferroelectric, Pb(Mg1/3Nb2/3)O3-PbTiO3. Mechanical suppression of undesirable phases stabilizes a reconstructed symmetry with highly ordered domains, verified by multiple characterization techniques. The strategy is validated across several distinct ferroelectric systems. To demonstrate its practical utility, we fabricate a transparent dual-modal wearable sensor integrating continuous blood pressure monitoring via piezoelectricity with photoplethysmographic SpO2 detection, enabling high-fidelity physiological tracking. This work establishes mechanically assisted symmetry reconstruction as a pathway to multifunctional optoelectronic materials and compact wearable health technologies.

cond-mat.mtrl-sci↗

arXiv2022

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

Deep learning approaches have shown promising results in remote sensing high spatial resolution (HSR) land-cover mapping. However, urban and rural scenes can show completely different geographical landscapes, and the inadequate generalizability of these algorithms hinders city-level or national-level mapping. Most of the existing HSR land-cover datasets mainly promote the research of learning semantic representation, thereby ignoring the model transferability. In this paper, we introduce the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) dataset to advance semantic and transferable learning. The LoveDA dataset contains 5987 HSR images with 166768 annotated objects from three different cities. Compared to the existing datasets, the LoveDA dataset encompasses two domains (urban and rural), which brings considerable challenges due to the: 1) multi-scale objects; 2) complex background samples; and 3) inconsistent class distributions. The LoveDA dataset is suitable for both land-cover semantic segmentation and unsupervised domain adaptation (UDA) tasks. Accordingly, we benchmarked the LoveDA dataset on eleven semantic segmentation methods and eight UDA methods. Some exploratory studies including multi-scale architectures and strategies, additional background supervision, and pseudo-label analysis were also carried out to address these challenges. The code and data are available at https://github.com/Junjue-Wang/LoveDA.

cs.CV↗

arXiv2020

Asymptotic resolution bounds of generalized modularity and multi-scale community detection

The maximization of generalized modularity performs well on networks in which the members of all communities are statistically indistinguishable from each other. However, there is no theory bounding the maximization performance in more realistic networks where edges are heterogeneously distributed within and between communities. Using the random graph properties, we establish asymptotic theoretical bounds on the resolution parameter for which the generalized modularity maximization performs well. From this new perspective on random graph model, we find the resolution limit of modularity maximization can be explained in a surprisingly simple and straightforward way. Given a network produced by the stochastic block models, the communities for which the resolution parameter is larger than their densities are likely to be spread among multiple clusters, while communities for which the resolution parameter is smaller than their background inter-community edge density will be merged into one large component. Therefore, no suitable resolution parameter exits when the intra-community edge density in a subgraph is lower than the inter-community edge density in some other subgraph. For such networks, we propose a progressive agglomerative heuristic algorithm to detect practically significant communities at multiple scales.

cs.SI↗

arXiv2019

On community structure in complex networks: challenges and opportunities

Community structure is one of the most relevant features encountered in numerous real-world applications of networked systems. Despite the tremendous effort of scientists working on this subject over the past few decades to characterize, model, and analyze communities, more investigations are needed to better understand the impact of community structure and its dynamics on networked systems. Here, we first focus on generative models of communities in complex networks and their role in developing strong foundation for community detection algorithms. We discuss modularity and the use of modularity maximization as the basis for community detection. Then, we overview the Stochastic Block Model, its different variants, and inference of community structures from such models. Next, we focus on time evolving networks, where existing nodes and links can disappear and/or new nodes and links may be introduced. The extraction of communities under such circumstances poses an interesting and non-trivial problem that has gained considerable interest over the last decade. We briefly discuss considerable advances made in this field recently. Finally, we focus on immunization strategies essential for targeting the influential spreaders of epidemics in modular networks. Their main goal is to select and immunize a small proportion of individuals from the whole network to control the diffusion process. Various strategies have emerged over the years suggesting different ways to immunize nodes in networks with overlapping and non-overlapping community structure. We first discuss stochastic strategies that require little or no information about the network topology at the expense of their performance. Then, we introduce deterministic strategies that have proven to be very efficient in controlling the epidemic outbreaks, but require complete knowledge of the network.

physics.soc-ph↗

arXiv2019

The evolution of polarization in the legislative branch of government

The polarization of political opinions among members of the U.S. legislative chambers measured by their voting records is greater today than it was thirty years ago. Previous research efforts to find causes of such increase have suggested diverse contributors, like growth of online media, echo chamber effects, media biases, or disinformation propagation. Yet, we lack theoretic tools to understand, quantify, and predict the emergence of high political polarization among voters and their legislators. Here, we analyze millions of roll-call votes cast in the U.S. Congress over the past six decades. Our analysis reveals the critical change of polarization patterns that started at the end of 1980's. In earlier decades, polarization within each Congress tended to decrease with time. In contrast, in the recent decades, the polarization has been likely to grow within each term. To shed light on the reasons for this change, we introduce here a formal model for competitive dynamics to quantify the evolution of polarization patterns in the legislative branch of the U.S. government. Our model represents dynamics of polarization, enabling us to successfully predict the direction of polarization changes in 28 out of 30 U.S. Congresses elected in the past six decades. From the evolution of polarization level as measured by the Rice index, our model extracts a hidden parameter - polarization utility which determines the convergence point of the polarization evolution. The increase in the polarization utility implied by the model strongly correlates with two current trends: growing polarization of voters and increasing influence of election campaign funders. Two largest peaks of the model's polarization utility correlate with significant political or legislative changes happening at the same time.

physics.soc-ph↗

arXiv2019

Regularized Stochastic Block Model for robust community detection in complex networks

The stochastic block model is able to generate different network partitions, ranging from traditional assortative communities to disassortative structures. Since the degree-corrected stochastic block model does not specify which mixing pattern is desired, the inference algorithms, which discover the most likely partition of the networks nodes, are likely to get trapped in the local optima of the log-likelihood. Here we introduce a new model constraining nodes' internal degrees ratios in the objective function to stabilize the inference of block models from the observed network data. Given the regularized model, the inference algorithms, such as Markov chain Monte Carlo, reliably finds assortative or disassortive structure as directed by the value of a single parameter. We show experimentally that the inference of our proposed model quickly converges to the desired assortative or disassortative partition while the inference of degree-corrected stochastic block model gets often trapped at the inferior local optimal partitions when the traditional assortative community structure is not strong in the observed networks.

cs.SI↗

arXiv2018

Scalable prediction of global online media news virality

News reports shape the public perception of the critical social, political and economical events around the world. Yet, the way in which emergent phenomena are reported in the news makes the early prediction of such phenomena a challenging task. We propose a scalable community-based probabilistic framework to model the spreading of news about events in online media. Our approach exploits the latent community structure in the global news media and uses the affiliation of the early adopters with a variety of communities to identify the events widely reported in the news at the early stage of their spread. The time complexity of our approach is linear in the number of news reports. It is also amenable to efficient parallelization. To demonstrate these features, the inference algorithm is parallelized for message passing paradigm and tested on RPI Advanced Multiprocessing Optimized System (AMOS), one of the fastest Blue Gene/Q supercomputers in the world. Thanks to the community-level features of the early adopters, the model gains an improvement of 20% in the early detection of the most massively reported events compared to the feature-based machine learning algorithm. Its parallelization scheme achieves orders of magnitude speedup.

cs.SI↗

arXiv2017

Adaptive Modularity Maximization via Edge Weighting Scheme

Modularity maximization is one of the state-of-the-art methods for community detection that has gained popularity in the last decade. Yet it suffers from the resolution limit problem by preferring under certain conditions large communities over small ones. To solve this problem, we propose to expand the meaning of the edges that are currently used to indicate propensity of nodes for sharing the same community. In our approach this is the role of edges with positive weights while edges with negative weights indicate aversion for putting their end-nodes into one community. We also present a novel regression model which assigns weights to the edges of a graph according to their local topological features to enhance the accuracy of modularity maximization algorithms. We construct artificial graphs based on the parameters sampled from a given unweighted network and train the regression model on ground truth communities of these artificial graphs in a supervised fashion. The extraction of local topological edge features can be done in linear time, making this process efficient. Experimental results on real and synthetic networks show that the state-of-the-art community detection algorithms improve their performance significantly by finding communities in the weighted graphs produced by our model.

cs.SI↗

arXiv2016

Supporting novel biomedical research via multilayer collaboration networks

The value of research containing novel combinations of molecules can be seen in many innovative and award-winning research programs. Despite calls to use innovative approaches to address common diseases, an increasing majority of research funding goes toward "safe" incremental research. Counteracting this trend by nurturing novel and potentially transformative scientific research is challenging, it must be supported in competition with established research programs. Therefore, we propose a tool that helps to resolve the tension between safe but fundable research vs. high-risk but potentially transformational research. It does this by identifying hidden overlapping interest around novel molecular research topics. Specifically, it identifies paths of molecular interactions that connect research topics and hypotheses that would not typically be associated, as the basis for scientific collaboration. Because these collaborations are related to the scientists' present trajectory, they are low risk and can be initiated rapidly. Unlike most incremental steps, these collaborations have the potential for leaps in understanding, as they reposition research for novel disease applications. We demonstrate the use of this tool to identify scientists who could contribute to understanding the cellular role of genes with novel associations with Alzheimer's disease, which have not been thoroughly characterized, in part due to the funding emphasis on established research.

cs.SI↗

arXiv2016

Towards Limited Scale-free Topology with Dynamic Peer Participation

Growth models have been proposed for constructing the scale-free overlay topology to improve the performance of unstructured peer-to-peer (P2P) networks. However, previous growth models are able to maintain the limited scale-free topology when nodes only join but do not leave the network; the case of nodes leaving the network while preserving a precise scaling parameter is not included in the solution. Thus, the full dynamic of node participation, inherent in P2P networks, is not considered in these models. In order to handle both nodes joining and leaving the network, we propose a robust growth model E-SRA, which is capable of producing the perfect limited scale-free overlay topology with user-defined scaling parameter and hard cut-offs. Scalability of our approach is ensured since no global information is required to add or remove a node. E-SRA is also tolerant to individual node failure caused by errors or attacks. Simulations have shown that E-SRA outperforms other growth models by producing topologies with high adherence to the desired scale-free property. Search algorithms, including flooding and normalized flooding, achieve higher efficiency over the topologies produced by E-SRA.

physics.soc-ph↗