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Xiong Li

Xiong Li appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Computer Visioncond-mat.supr-conmath.CAmath.DScond-mat.mtrl-scieess.IVMachine Learningmath.STPopulations and EvolutionStatistics Theory

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Published work

10 published item(s)

preprint2026arXiv

Polygon-mamba: Retinal vessel segmentation using polygon scanning mamba and space-frequency collaborative attention

Retinal vessel segmentation is crucial for diagnosis and assessment of ocular diseases. Notably, segmentation of small retinal vessels has been consistently recognized as a challenging and complex task. To tackle this challenge, we design a hybrid CNN-Mamba fusion network that integrates polygon scanning mamba and space-frequency collaborative attention mechanism for the detection of small vessels. Considering that the traditional mamba architecture with horizontal-vertical scanning may compromise the topological integrity of target structures and result in local discontinuities in small retinal vessels, we present a polygon scanning visual state space model (PS-VSS) to identify small vessel structural features by multi-layer reverse scanning way. Which effectively preserves pixels connectivity, thereby substantially mitigating the loss of information pertaining to small vessels. Furthermore, as we all known that the spatial domain prioritizes positional and structural information, while the frequency domain emphasizes global perception and local detail components, a space-frequency collaborative attention mechanism (SFCAM) is introduced within the skip connection to extract efficient features from the spatial and frequency domains. This strategy empowers the model to dynamically enhance the key features while effectively suppressing clutters. To assess the efficacy of our model, it was tested on three publicly available datasets: DRIVE, STARE, and CHASE_DB1. Compared to manual annotations, our model demonstrated F1 scores of 0.8283, 0.8282, and 0.8251, Area Under Curve (AUC) values of 0.9806, 0.9840, and 0.9866, and Sensitivity (SE) values of of 0.8268, 0.8314, and 0.8484 across three datasets, respectively. The effectiveness of our model was validated through both visual inspection and quantitative analysis.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Pulmonary Fissure Segmentation in CT Images Based on ODoS Filter and Shape Features

Priori knowledge of pulmonary anatomy plays a vital role in diagnosis of lung diseases. In CT images, pulmonary fissure segmentation is a formidable mission due to various of factors. To address the challenge, an useful approach based on ODoS filter and shape features is presented for pulmonary fissure segmentation. Here, we adopt an ODoS filter by merging the orientation information and magnitude information to highlight structure features for fissure enhancement, which can effectively distinguish between pulmonary fissures and clutters. Motivated by the fact that pulmonary fissures appear as linear structures in 2D space and planar structures in 3D space in orientation field, an orientation curvature criterion and an orientation partition scheme are fused to separate fissure patches and other structures in different orientation partition, which can suppress parts of clutters. Considering the shape difference between pulmonary fissures and tubular structures in magnitude field, a shape measure approach and a 3D skeletonization model are combined to segment pulmonary fissures for clutters removal. When applying our scheme to 55 chest CT scans which acquired from a publicly available LOLA11 datasets, the median F1-score, False Discovery Rate (FDR), and False Negative Rate (FNR) respectively are 0.896, 0.109, and 0.100, which indicates that the presented method has a satisfactory pulmonary fissure segmentation performance.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Two-dimensional InSe/WS$_2$ heterostructure with enhanced optoelectronic performance in the visible region

Two-dimensional (2D) InSe and WS$_2$ exhibit promising characteristics for optoelectronic and photoelectrochemical applications, e.g. photodetection and photocatalytic water splitting. However, both of them have poor absorption of visible light due to wide band gaps. 2D InSe has high electron mobility but low hole mobility, while 2D WS$_2$ is on the opposite. Here, we design a 2D heterostructure composed of their monolayers and study its optoelectronic properties by first-principles calculations. Our results show that the heterostructure has a direct band gap of 2.19 eV, which is much smaller than those of the monolayers mainly due to a type-II band alignment: the valence band maximum and the conduction band minimum of monolayer InSe are lower than those of monolayer WS$_2$, respectively. The visible-light absorption is enhanced considerably, e.g. about fivefold (threefold) increase at the wavelength of 490 nm in comparison to monolayer InSe (WS$_2$). The type-II band alignment also facilitates the spatial separation of photogenerated electron-hole pairs, i.e., electrons (holes) reside preferably in the InSe (WS$_2$) layer. The two layers complement each other in carrier mobilities of the heterostructure: the photogenerated electrons and holes inherit the large mobilities from the InSe and WS$_2$ monolayers, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Anisotropic Superconductivity of Ca1-xLaxFeAs2 (x ~ 0.18) Single Crystal

Anisotropic superconducting properties including the upper critical field Hc2, thermal activation energy U0, and critical current density Jc are systematically studied in a large Ca1-xLaxFeAs2 single crystal (x ~ 0.18). The obtained Hc2 bears a moderate anisotropy gamma of approximately 2-4.2, located between those of '122' Ba1-xKxFe2As2 (1 < gamma < 2) and '1111' NdFeAsO1-xFx (5 < gamma < 9.2). Both the magnitude of U0 and its field dependence are very similar to those of NdFeAsO1-xFx, also indicating anisotropic superconductivity. Moreover, high and anisotropic Jc's exceeding 10^5 A/cm2 have been calculated from the magnetization hysteresis loops, indicating the existence of strong bulk-dominated pinning in the present superconducting material.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Bulk Superconductivity in Fe1+yTe0.6Se0.4 Induced by Removal of Excess Fe

Experimental evidences from transport, magnetic, and magneto-optical (MO) image measurements confirmed that arsenic (As) vapor annealing was another effective way to induce bulk superconductivity with isotropic, large, and homogenous superconducting critical current density (Jc) in Fe1+yTe0.6Se0.4 single crystal. Since As is an exotic and easily detectable heavy element to Fe1+yTe0.6Se0.4 single crystal, As vapor annealing is very advantageous for the study of annealing mechanism. Detailed micro-structural and elemental analyses exclude the possibility that intercalating or doping effect may happen in the other post-annealing methods, proving that Fe reacts with As on the surface of the crystal and the reaction itself acts as a driving force to drag excess Fe out. The removal of excess Fe results in the good superconductivity performance.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Moment Boundedness of Linear Stochastic Delay Differential Equation with Distributed Delay

This paper studies the moment boundedness of solutions of linear stochastic delay differential equations with distributed delay. For a linear stochastic delay differential equation, the first moment stability is known to be identical to that of the corresponding deterministic delay differential equation. However, boundedness of the second moment is complicated and depends on the stochastic terms. In this paper, the characteristic function of the equation is obtained through techniques of Laplace transform. From the characteristic equation, sufficient conditions for the second moment to be bounded or unbounded are proposed.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Second Moment Boundedness of Linear Stochastic Delay Differential Equations

This paper studies the second moment boundedness of solutions of linear stochastic delay differential equations. First, we give a framework, for general $\mathrm{N}$-dimensional linear stochastic differential equations with a single discrete delay, of calculating the characteristic function for the second moment boundedness. Next, we apply the proposed framework to a special case of a type of 2-dimensional equation that the stochastic terms are decoupled. For the 2-dimensional equation, we obtain the characteristic function explicitly given by equation coefficients, the characteristic function gives sufficient conditions for the second moment to be bounded or unbounded.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Stochastic Feature Mapping for PAC-Bayes Classification

Probabilistic generative modeling of data distributions can potentially exploit hidden information which is useful for discriminative classification. This observation has motivated the development of approaches that couple generative and discriminative models for classification. In this paper, we propose a new approach to couple generative and discriminative models in an unified framework based on PAC-Bayes risk theory. We first derive the model-parameter-independent stochastic feature mapping from a practical MAP classifier operating on generative models. Then we construct a linear stochastic classifier equipped with the feature mapping, and derive the explicit PAC-Bayes risk bounds for such classifier for both supervised and semi-supervised learning. Minimizing the risk bound, using an EM-like iterative procedure, results in a new posterior over hidden variables (E-step) and the update rules of model parameters (M-step). The derivation of the posterior is always feasible due to the way of equipping feature mapping and the explicit form of bounding risk. The derived posterior allows the tuning of generative models and subsequently the feature mappings for better classification. The derived update rules of the model parameters are same to those of the uncoupled models as the feature mapping is model-parameter-independent. Our experiments show that the coupling between data modeling generative model and the discriminative classifier via a stochastic feature mapping in this framework leads to a general classification tool with state-of-the-art performance.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

A stoichiometrically derived algal growth model and its global analysis

Organisms are composed of multiple chemical elements such as carbon, nitrogen, and phosphorus. The scarcity of any of these elements can severely restrict organismal and population growth. However, many trophic interaction models only consider carbon limitation via energy flow. In this paper, we construct an algal growth model with the explicit incorporation of light and nutrient availability to characterize both carbon and phosphorus limitations. We provide a global analysis of this model to illustrate how light and nutrient availability regulate algal dynamics.

0 citations0 reviewsNo code linkedOpen access