Search Results (11)

Visible-Infrared Person Re-Identification (VI-ReID) is of crucial importance in applications such as monitoring and security. However, challenges faced from intra-class variations and cross-modal differences are often exacerbated by inaccurate infrared analysis and insufficient structural modeling. To address these issues, we propose Parsing-guided Differential Enhancement Graph Learning (PDEGL), a novel framework that learns discriminative representations through a dual-branch architecture synergizing global feature refinement with part-based structural graph analysis. In particular, we introduce a Differential Infrared Part Enhancement (DIPE) module to correct infrared parsing errors and a Parsing Structural Graph (PSG) module to model high-order topological relationships between body parts for structural consistency matching. Furthermore, we design a Position-sensitive Spatial-Channel Attention (PSCA) module to enhance global feature discriminability. Extensive evaluations on the SYSU-MM01, RegDB, and LLCM datasets demonstrate that our PDEGL method achieves competitive performance. Full article

Ventricular arrhythmias (VAs) are critical cardiovascular diseases that require rapid and accurate detection. Conventional approaches relying on multi-lead ECG or deep learning models have limitations in computational cost, interpretability, and real-time applicability on wearable devices. To address these issues, a lightweight and interpretable framework based on multiple complex networks was proposed for the detection of life-threatening VAs using short-term single-lead ECG signals. The input signals were decomposed using the fixed-frequency-range empirical wavelet transform, and sub-bands were subsequently analyzed through multiscale visibility graphs, recurrence networks, cross-recurrence networks, and joint recurrence networks. Eight topological features were extracted and input into an XGBoost classifier for VA identification. Ten-fold cross-validation results on the MIT-BIH VFDB and CUDB databases demonstrated that the proposed method achieved a sensitivity of 99.02 ± 0.53%, a specificity of 98.44 ± 0.43%, and an accuracy of 98.73 ± 0.02% for 10 s ECG segments. The model also maintained robust performance on shorter segments, with 97.23 ± 0.76% sensitivity, 98.85 ± 0.95% specificity, and 96.62 ± 0.02% accuracy on 2 s segments. The results outperformed existing feature-based and deep learning approaches while preserving model interpretability. Furthermore, the proposed method supports mobile deployment, facilitating real-time use in wearable healthcare applications. Full article

Based on some typical complex network analysis methods and machine learning techniques, a general multiple-measures composited strategy-guided epilepsy diagnosis analysis framework is proposed in this brief paper. Five typical network analysis methods for biology time series analysis are utilized for real applications, including the classical visibility graph (VG), horizontal visibility graph (HVG), the limited penetrable visibility graph (LPVG), the modified frequency degree method (MFDM), and the quantity graph (QG). By using the aforementioned typical transformation methods, the EEG signal sets to be classified are transferred into graph network object sets. The main network features and related indicators are calculated and extracted as features for classification tasks. Some key features are selected via variance analysis, and the eXtreme Gradient Boosting (XGBOOST) machine learning algorithm is used for related binary and five-class classification tasks for electroencephalographic time series. Numerical experiments demonstrate that, through ten-fold cross-validation on the entire dataset, the classification accuracy for two-class classification consistently reaches 97.8% (with a specificity of 97.5%), while for five-class classification, the accuracy stably reaches 82.4% (with a specificity of 95.6%). Therefore, our classification framework can be effectively used to assist hospital doctors and medical specialists in diagnosing related diseases, especially to help accelerate the treatment of epilepsy patients. Full article

Single-resolution homography estimation of infrared and visible images is a significant and challenging research area within the field of computing, which has attracted a great deal of attention. However, due to the large modal differences between infrared and visible images, existing methods are difficult to stably and accurately extract and match features between the two image types at a single resolution, which results in poor performance on the homography estimation task. To address this issue, this paper proposes an end-to-end unsupervised single-resolution infrared and visible image homography estimation method based on graph neural network (GNN), homoViG. Firstly, the method employs a triple attention shallow feature extractor to capture cross-dimensional feature dependencies and enhance feature representation effectively. Secondly, Vision GNN (ViG) is utilized as the backbone network to transform the feature point matching problem into a graph node matching problem. Finally, this paper proposes a new homography estimator, residual fusion vision graph neural network (RFViG), to reduce the feature redundancy caused by the frequent residual operations of ViG. Meanwhile, RFViG replaces the residual connections with an attention feature fusion module, highlighting the important features in the low-level feature graph. Furthermore, this model introduces detail feature loss and feature identity loss in the optimization phase, facilitating network optimization. Through extensive experimentation, we demonstrate the efficacy of all proposed components. The experimental results demonstrate that homoViG outperforms existing methods on synthetic benchmark datasets in both qualitative and quantitative comparisons. Full article

Electroencephalogram (EEG) is the most preferred and credible source for emotion recognition, where long-short range features and a multichannel relationship are crucial for performance because numerous physiological components function at various time scales and on different channels. We propose a cascade scale-aware adaptive graph convolutional network and cross-EEG transformer (SAG-CET) to explore the comprehensive interaction between multiscale and multichannel EEG signals with two novel ideas. First, to model the relationship of multichannel EEG signals and enhance signal representation ability, the multiscale EEG signals are fed into a scale-aware adaptive graph convolutional network (SAG) before the CET model. Second, the cross-EEG transformer (CET), is used to explicitly capture multiscale features as well as their correlations. The CET consists of two self-attention encoders for gathering features from long-short time series and a cross-attention module to integrate multiscale class tokens. Our experiments show that CET significantly outperforms a vanilla unitary transformer, and the SAG module brings visible gains. Our methods also outperform state-of-the-art methods in subject-dependent tasks with 98.89%/98.92% in accuracy for valence/arousal on DEAP and 99.08%/99.21% on DREAMER. Full article

In contemporary society, a notable trend of diminishing family sizes has led to an increasing number of elderly individuals living in solitude, often facing the end of life alone. This phenomenon underscores a critical challenge: addressing the pervasive loneliness experienced by many seniors. In response to this pressing issue, the concept of “cross-generational co-living” emerges as a potential solution. By exploring and implementing cross-generational co-living models, this research contributes to the development of more inclusive, supportive, and adaptable environments. The investigation involved an extensive field study and comprehensive data analysis of twenty-four instances of cross-generational co-living spaces in China and Italy. This analysis utilized space syntax as a fundamental theoretical framework, incorporating convex graphical topological relationship extraction and visibility graph analysis models. The outcomes of the study indicate that the configuration of cross-generational co-living spaces include spatial form, type, location, and the proportion of areas. Spaces arranged in a cluster form are most effective in promoting mutual communication. Spatial types and locations characterized by elevated integration values demonstrate a heightened potential for cross-generational communication. Space possessing a higher integration value typically correlates with a reduced ratio of area discreteness. These findings are instrumental in understanding how cultural and societal variances shape the design and utilization of cross-generational co-living spaces. Consequently, this study provides valuable guidelines for improving environments that are essential for advancing the principles of age-friendly design, which aims to enhance the quality of life for the elderly and foster a more harmonious and interconnected society across all generations. Full article

With the increasing demand for person re-identification (Re-ID) tasks, the need for all-day retrieval has become an inevitable trend. Nevertheless, single-modal Re-ID is no longer sufficient to meet this requirement, making Multi-Modal Data crucial in Re-ID. Consequently, a Visible-Infrared Person Re-Identification (VI Re-ID) task is proposed, which aims to match pairs of person images from the visible and infrared modalities. The significant modality discrepancy between the modalities poses a major challenge. Existing VI Re-ID methods focus on cross-modal feature learning and modal transformation to alleviate the discrepancy but overlook the impact of person contour information. Contours exhibit modality invariance, which is vital for learning effective identity representations and cross-modal matching. In addition, due to the low intra-modal diversity in the visible modality, it is difficult to distinguish the boundaries between some hard samples. To address these issues, we propose the Graph Sampling-based Multi-stream Enhancement Network (GSMEN). Firstly, the Contour Expansion Module (CEM) incorporates the contour information of a person into the original samples, further reducing the modality discrepancy and leading to improved matching stability between image pairs of different modalities. Additionally, to better distinguish cross-modal hard sample pairs during the training process, an innovative Cross-modality Graph Sampler (CGS) is designed for sample selection before training. The CGS calculates the feature distance between samples from different modalities and groups similar samples into the same batch during the training process, effectively exploring the boundary relationships between hard classes in the cross-modal setting. Some experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate the superiority of our proposed method. Specifically, in the VIS→IR task, the experimental results on the RegDB dataset achieve 93.69% for Rank-1 and 92.56% for mAP. Full article

Generalized stereo matching faces the radiation difference and small ground feature difference brought by different satellites and different time phases, while the texture-less and disparity discontinuity phenomenon seriously affects the correspondence between matching points. To address the above problems, a novel generalized stereo matching method based on the iterative optimization of hierarchical graph structure consistency cost is proposed for urban 3D scene reconstruction. First, the self-similarity of images is used to construct k-nearest neighbor graphs. The left-view and right-view graph structures are mapped to the same neighborhood, and the graph structure consistency (GSC) cost is proposed to evaluate the similarity of the graph structures. Then, cross-scale cost aggregation is used to adaptively weight and combine multi-scale GSC costs. Next, object-based iterative optimization is proposed to optimize outliers in pixel-wise matching and mismatches in disparity discontinuity regions. The visibility term and the disparity discontinuity term are iterated to continuously detect occlusions and optimize the boundary disparity. Finally, fractal net evolution is used to optimize the disparity map. This paper verifies the effectiveness of the proposed method on a public US3D dataset and a self-made dataset, and compares it with state-of-the-art stereo matching methods. Full article

The permeability and inflammatory tissue reaction to Mucomaix^® matrix (MM), a non- cross-linked collagen-based matrix was evaluated in both ex vivo and in vivo settings. Liquid platelet rich fibrin (PRF), a blood concentrate system, was used to assess its capacity to absorb human proteins and interact with blood cells ex vivo. In the in vivo aspect, 12 Wister rats had MM implanted subcutaneously, whereas another 12 rats (control) were sham-operated without biomaterial implantation. On days 3, 15 and 30, explantation was completed (four rats per time-point) to evaluate the tissue reactions to the matrix. Data collected were statistically analyzed using analysis of variance (ANOVA) and Tukey multiple comparisons tests (GraphPad Prism 8). The matrix absorbed the liquid PRF in the ex vivo study. Day 3 post-implantation revealed mild tissue inflammatory reaction with presence of mononuclear cells in the implantation site and on the biomaterial surface (mostly CD68-positive macrophages). The control group at this stage had more mononuclear cells than the test group. From day 15, multinucleated giant cells (MNGCs) were seen in the implantation site and the outer third of the matrix with marked increase on day 30 and spread to the matrix core. The presence of these CD68-positive MNGCs was associated with significant matrix vascularization. The matrix degraded significantly over the study period, but its core was still visible as of day 30 post-implantation. The high permeability and fast degradation properties of MM were highlighted. Full article

Malaria, a disease with major health and socio-economic impacts, is driven by multiple factors, including a complex interaction with various climatic variables. In this paper, five methods developed for inferring causal relations between dynamic processes based on the information encapsulated in time series are applied on cases previously studied in literature by means of statistical methods. The causality detection techniques investigated in the paper are: a version of the kernel Granger causality, transfer entropy, recurrence plot, causal decomposition and complex networks. The methods provide coherent results giving a quite good confidence in the conclusions. Full article

A new measure for the characterization of interconnected dynamical systems coupling is proposed. The method is based on the representation of time series as weighted cross-visibility networks. The weights are introduced as the metric distance between connected nodes. The structure of the networks, depending on the coupling strength, is quantified via the entropy of the weighted adjacency matrix. The method has been tested on several coupled model systems with different individual properties. The results show that the proposed measure is able to distinguish the degree of coupling of the studied dynamical systems. The original use of the geodesic distance on Gaussian manifolds as a metric distance, which is able to take into account the noise inherently superimposed on the experimental data, provides significantly better results in the calculation of the entropy, improving the reliability of the coupling estimates. The application to the interaction between the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole and to the influence of ENSO on influenza pandemic occurrence illustrates the potential of the method for real-life problems. Full article