Spurious Edges Research Articles

Connecting the dots: using a network approach to study the wellbeing spectrum

In positive psychology, defining the wellbeing construct has been a challenge. We used the psychometric network approach to study the structure of wellbeing. The sample consisted of Dutch adults registered with the Netherlands Twin Register. The variables were measured through standardized surveys. The network was estimated using the Mixed Graphical Models method and least absolute shrinkage and selection operator (LASSO) regularization to limit the number of spurious edges. We estimated a network in a trimming sample (N=1343, 63% females, Mage = 53.18, SDage = 9.45) and in an estimation sample (N=726, 75% females, Mage = 45.27, SDage = 11.12) to examine its performance and accuracy. Our final network consists of a positive cluster including satisfaction with life, subjective happiness, and flourishing items, and a negative cluster including depressive symptoms, loneliness, and neuroticism items. We identified the four most central nodes: one satisfaction with life item, one neuroticism item, and two depression items. This suggests that to get a general sense of the wellbeing construct, these items would serve as most informative. The network approach clearly demonstrates the different, yet connected positive and negative clusters of wellbeing and therefore re-affirms the complex interconnectivity of wellbeing phenotypes. In addition, the network results reject the view of strictly delineated wellbeing domains. Having identified the most central nodes in the network, these can be used in futures studies with limited resources, as they are likely to be the most representative of the wellbeing spectrum.

Dynamic undirected graphical models for time-varying clinical symptom and neuroimaging networks.

In this work, we propose methods to examine how the complex interrelationships between clinical symptoms and, separately, brain imaging biomarkers change over time leading up to the diagnosis of a disease in subjects with a known genetic near-certainty of disease. We propose a time-dependent undirected graphical model that ensures temporal and structural smoothness across time-specific networks to examine the trajectories of interactions between markers aligned at the time of disease onset. Specifically, we anchor subjects relative to the time of disease diagnosis (anchoring time) as in a revival process, and we estimate networks at each time point of interest relative to the anchoring time. To use all available data, we apply kernel weights to borrow information across observations that are close to the time of interest. Adaptive lasso weights are introduced to encourage temporal smoothness in edge strength, while a novel elastic fused- penalty removes spurious edges and encourages temporal smoothness in network structure. Our approach can handle practical complications such as unbalanced visit times. We conduct simulation studies to compare our approach with existing methods. We then apply our method to data from PREDICT-HD, a large prospective observational study of pre-manifest Huntington's disease (HD) patients, to identify symptom and imaging network changes that precede clinical diagnosis of HD.

Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks.

Gene regulatory networks (GRNs) are vital tools for delineating regulatory relationships between transcription factors and their target genes. The boom in computational biology and various biotechnologies has made inferring GRNs from multi-omics data a hot topic. However, when networks are constructed from gene expression data, they often suffer from false-positive problem due to the transitive effects of correlation. The presence of spurious noise edges obscures the real gene interactions, which makes downstream analyses, such as detecting gene function modules and predicting disease-related genes, difficult and inefficient. Therefore, there is an urgent and compelling need to develop network denoising methods to improve the accuracy of GRN inference. In this study, we proposed a novel network denoising method named REverse Network Diffusion On Random walks (RENDOR). RENDOR is designed to enhance the accuracy of GRNs afflicted by indirect effects. RENDOR takes noisy networks as input, models higher-order indirect interactions between genes by transitive closure, eliminates false-positive effects using the inverse network diffusion method, and produces refined networks as output. We conducted a comparative assessment of GRN inference accuracy before and after denoising on simulated networks and real GRNs. Our results emphasized that the network derived from RENDOR more accurately and effectively captures gene interactions. This study demonstrates the significance of removing network indirect noise and highlights the effectiveness of the proposed method in enhancing the signal-to-noise ratio of noisy networks. The R package RENDOR is provided at https://github.com/Wu-Lab/RENDOR and other source code and data are available at https://github.com/Wu-Lab/RENDOR-reproduce.

Seeing All From a Few: Nodes Selection Using Graph Pooling for Graph Clustering.

Recently, there has been considerable research interest in graph clustering aimed at data partition using graph information. However, one limitation of most graph-based methods is that they assume that the graph structure to operate is reliable. However, there are inevitably some edges in the graph that are not conducive to graph clustering, which we call spurious edges. This brief is the first attempt to employ the graph pooling technique for node clustering to the best of our knowledge. In this brief, we propose a novel dual graph embedding network (DGEN), which is designed as a two-step graph encoder connected by a graph pooling layer to learn the graph embedding. In DGEN, we assume that if a node and its nearest neighboring node are close to the same clustering center, this node is informative, and this edge can be considered as a cluster-friendly edge. Based on this assumption, the neighbor cluster pooling (NCPool) is devised to select the most informative subset of nodes and the corresponding edges based on the distance of nodes and their nearest neighbors to the cluster centers. This can effectively alleviate the impact of the spurious edges on the clustering. Finally, to obtain the clustering assignment of all nodes, a classifier is trained using the clustering results of the selected nodes. Experiments on five benchmark graph datasets demonstrate the superiority of the proposed method over state-of-the-art algorithms.

Enhancement of the balanced total horizontal derivative of gravity data using the power law approach

Mapping the boundaries of subsurface geological structures constitutes a fundamental aspect of interpreting gravity data. Various methods relying on gravity derivatives have been introduced to delineate these boundaries. However, these approaches are not without their limitations, often leading to the generation of false edges or resulting in divergent outcomes. This article introduces a novel method that utilizes the power law approach and balanced total horizontal derivative for mapping gravity data edges. To assess the robustness of the proposed technique, we conducted tests on model examples and applied it to a real case study in northwest Vietnam. The results demonstrate that our method can accurately and clearly map edges in comparison to existing methodologies, while also preventing the generation of spurious edges in the output maps.

Evaluating Edge Credibility in Evolving Noisy Social Networks

Despite the massive surge of evolving social network analysis in popularity, existing research usually represent the observed social interactions among individuals as completely credible edges. However, due to information inaccuracy, individual non-response and dropout, and sampling biases in observations, the evolving noisy social network that coexists true edges and spurious edges is pervasive in actual applications, where the ignoration of credibility otherness of observed edges could lead to the wrong estimates of social properties and misleading conclusions. To discover credible edge information to shape correct social interactions among individuals, we propose a universal and explainable multiple-neighbor evolutional filtering method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEFM</i> ) to evaluate how credible of observed edges to ‘truly’ exist in the evolving noisy social network. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEFM</i> consists of an evolutional extractor and a filtering evaluator. To resist the noisy disturbance, the evolutional extractor exploits the evolutional states of edges from the perspective of evolution mechanisms within multiple-neighbor ranges, which applies different link prediction algorithms to fit the evolution mechanism in the formation of each edge. Further, the filtering evaluator reconstructs Kalman filter to predict and refine the evolutional states of edges based on their evolving local structures. As a result, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEFM</i> combines the evolutional extractor and the filtering evaluator to analyze the evolutional fluctuations of the observed edges to evaluate their credibility. Extensive experiments on real-world datasets demonstrate that our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEFM</i> can effectively and reasonably evaluate edge credibility in evolving noisy social networks.

Precise Cadastral Survey of Rural Buildings Based on Wall Segment Topology Analysis from Dense Point Clouds

The renewal and updating of the cadastre of real estate is a long and tedious task for land administration, especially for rural buildings that lack unified design and planning. In order to retain the required accuracy of all points in the register, huge extensive manual editing is often required. In this work, a precise cadastral survey approach is proposed using Unmanned Aerial Vehicle (UAV) imagery-based stereo point clouds. To ensure the accuracy and uniqueness of building outer walls, the non-maximum suppression of wall points that can separate noise and avoid repeated extraction is proposed. Meanwhile, the multiple cue weighted RANSAC, considering both point-to-line distance and normal consistency, is proposed to reduce the influence of building attachments and avoid spurious edges. For a better description of wall topology, the wall line segment topology graph (WLTG), which can guide the connection of adjacent lines and support the searching of closed boundaries through the minimum graph loop analysis, is also built. Experimental results show that the proposed method can effectively detect the building vector contours with high precision and correct topology, and the detection completeness and correctness of the edge corners can reach 84.9% and 93.2% when the mean square error is below 10 cm.

Hierarchical Graph Neural Network Based on Semi-Implicit Variational Inference

Graph neural network(GNN) has obtained outstanding achievements in relational data. However, these data have uncertain properties, for example, spurious edges may be included. Recently, Variational graph autoencoder(VGAE) has been proposed to solve this problem. However, the distributional assumptions in the variational family restrict the variational inference (VI) flexibility and they define variational families using mean-field, which can not capture complex posterior distributional. To solve the above question, in this paper, we proposed a novel GNN model based on semi-implicit variational inference (SIVI), which can embed the node to the latent space to improve VI flexibility and enhance VI expressiveness with mixing distribution. Specifically, to approximate the true posterior, a variational posterior was given utilizing a semi-implicit hierarchical variational framework, which can model complex posterior. Moreover, an iterative decoder is used to better capture graph properties. Besides, due to the hierarchical structure in our model, it can incorporation neighbour information between nodes. Experiments on multiple data sets, our method has achieved state-of-the-art results compared to other similar methods. Particularly, on the citation dataset Citeseer without features, our method outperforms VGAE by nine percentage.

High-Order Topology-Enhanced Graph Convolutional Networks for Dynamic Graphs

Understanding the evolutionary mechanisms of dynamic graphs is crucial since dynamic is a basic characteristic of real-world networks. The challenges of modeling dynamic graphs are as follows: (1) Real-world dynamics are frequently characterized by group effects, which essentially emerge from high-order interactions involving groups of entities. Therefore, the pairwise interactions revealed by the edges of graphs are insufficient to describe complex systems. (2) The graph data obtained from real systems are often noisy, and the spurious edges can interfere with the stability and efficiency of models. To address these issues, we propose a high-order topology-enhanced graph convolutional network for modeling dynamic graphs. The rationale behind it is that the symmetric substructure in a graph, called the maximal clique, can reflect group impacts from high-order interactions on the one hand, while not being readily disturbed by spurious links on the other hand. Then, we utilize two independent branches to model the distinct influence mechanisms of the two effects. Learnable parameters are used to tune the relative importance of the two effects during the process. We conduct link predictions on real-world datasets, including one social network and two citation networks. Results show that the average improvements of the high-order enhanced methods are 68%, 15%, and 280% over the corresponding backbones across datasets. The ablation study and perturbation analysis validate the effectiveness and robustness of the proposed method. Our research reveals that high-order structures provide new perspectives for studying the dynamics of graphs and highlight the necessity of employing higher-order topologies in the future.

Lifelong Topological Visual Navigation

Commonly, learning-based topological navigation approaches produce a local policy while preserving some loose connectivity of the space through a topological map. Nevertheless, spurious or missing edges in the topological graph often lead to navigation failure. In this work, we propose a sampling-based graph building method, which results in sparser graphs yet with higher navigation performance compared to baseline methods. We also propose graph maintenance strategies that eliminate spurious edges and expand the graph as needed, which improves lifelong navigation performance. Unlike controllers that learn from fixed training environments, we show that our model can be fine-tuned using only a small number of collected trajectory images from a real-world environment where the agent is deployed. We demonstrate successful navigation after fine-tuning on real-world environments, and notably show significant navigation improvements over time by applying our lifelong graph maintenance strategies.

Dynamic Bayesian Network Learning to Infer Sparse Models From Time Series Gene Expression Data.

One of the key challenges in systems biology is to derive gene regulatory networks (GRNs) from complex high-dimensional sparse data. Bayesian networks (BNs) and dynamic Bayesian networks (DBNs) have been widely applied to infer GRNs from gene expression data. GRNs are typically sparse but traditional approaches of BN structure learning to elucidate GRNs often produce many spurious (false positive) edges. We present two new BN scoring functions, which are extensions to the Bayesian Information Criterion (BIC) score, with additional penalty terms and use them in conjunction with DBN structure search methods to find a graph structure that maximises the proposed scores. Our BN scoring functions offer better solutions for inferring networks with fewer spurious edges compared to the BIC score. The proposed methods are evaluated extensively on auto regressive and DREAM4 benchmarks. We found that they significantly improve the precision of the learned graphs, relative to the BIC score. The proposed methods are also evaluated on three real time series gene expression datasets. The results demonstrate that our algorithms are able to learn sparse graphs from high-dimensional time series data. The implementation of these algorithms is open source and is available in form of an R package on GitHub at https://github.com/HamdaBinteAjmal/DBN4GRN, along with the documentation and tutorials.

Image Interpolation Based on 2D-DWT with Novel Regularity-Preserving Algorithm Using RLS Adaptive Filters

This paper proposes a novel method for the image interpolation problem based on two-dimensional discrete wavelet transform (DWT) with the edge preserving approach. The purpose of this method is to consider two contrasting issues of over-smoothing and creation of spurious edges at the same time, and offer a novel solution based on statistical dependencies of image sub-bands, and noise behavior. The offered method has a multi-faceted approach for the problem; by sub-band coding, it handles each 2D-DWT image sub-band with a different solution. For LH and HL sub-bands, two algorithms work together in order to preserve regularity. Area_Check algorithm is a four-phase edge-preserving algorithm that aims to recognize and interpolate separating lines of environments and edgy regions in the best possible way. On the other hand, RLS_AVG algorithm interpolates smooth surfaces of the image by keeping the regularity of the image without over-smoothing. In this regard, the offered algorithm has a great power to counter jaggies and annoying artifacts. In the end, in order to demonstrate the capability, and performance of the proposed method, the final results in various metrics are compared with the results of the most famous and the newest image interpolation methods.

Detection of turner syndrome using hand X-ray using anchor based links segmentation method.

Turner Syndrome (TS) is a chromosomal disorder, wherein the female's growth is impacted. Immature ovaries, low stature, and heart abnormalities are a range of developmental and medical issues due to TS. The condition of TS might be detected prior to birth, throughout infancy or in the early years of life. The diagnosis of TS in girls with modest symptoms and indications is sometimes deferred until they reach adolescence or become young adults. This study presents an algorithm to segment the hand digital X-ray image in children with TS. In medical image and computer vision examination, image segmentation is demanding, and very crucial. Prevailing segmentation algorithms even now suffer from common segmentation issues including under-segmentation, over-segmentation, and spurious or non-closed edges, regardless of the several years of studies. In this paper, Anchor Based Link (ABL) segmentation approach is proposed to detect TS based on fourth Metacarpal bone from left hand X-ray images. The detection of TS is demonstrated based upon the comparison of proposed approach with existing watershed segmentation and Gaussian-Mixture-Model-based Hidden-Markov-Random-Field (GMM-HMRF) method. The proposed approach attains better segmentation based on the ratio of height and width of left fourth finger that is analyzed for normal children and children having TS with the help of edge pixel present in the metacarpal bone that has been segmented. The suggested method is verified on fifty (50) sample X-ray hand images of carpal bones, providing 0.60 ± 0.02 as an average Dice coefficient.

Learning representation for multiple biological networks via a robust graph regularized integration approach.

Learning node representation is a fundamental problem in biological network analysis, as compact representation features reveal complicated network structures and carry useful information for downstream tasks such as link prediction and node classification. Recently, multiple networks that profile objects from different aspects are increasingly accumulated, providing the opportunity to learn objects from multiple perspectives. However, the complex common and specific information across different networks pose challenges to node representation methods. Moreover, ubiquitous noise in networks calls for more robust representation. To deal with these problems, we present a representation learning method for multiple biological networks. First, we accommodate the noise and spurious edges in networks using denoised diffusion, providing robust connectivity structures for the subsequent representation learning. Then, we introduce a graph regularized integration model to combine refined networks and compute common representation features. By using the regularized decomposition technique, the proposed model can effectively preserve the common structural property of different networks and simultaneously accommodate their specific information, leading to a consistent representation. A simulation study shows the superiority of the proposed method on different levels of noisy networks. Three network-based inference tasks, including drug-target interaction prediction, gene function identification and fine-grained species categorization, are conducted using representation features learned from our method. Biological networks at different scales and levels of sparsity are involved. Experimental results on real-world data show that the proposed method has robust performance compared with alternatives. Overall, by eliminating noise and integrating effectively, the proposed method is able to learn useful representations from multiple biological networks.

Delineation of structural lineaments of the Southwest Sub-basin (East Vietnam Sea) using global marine gravity model from CryoSat-2 and Jason-1 satellites

In recent years, the analysis of satellite gravity data is used as a powerful tool for geologic mapping. This study is based on various filtered maps of Bouguer gravity anomaly data of the Southwest Sub-basin (East Vietnam Sea) to delineate its main structural lineaments. The effectiveness of modern filtering methods such as normalized analytical signal, improved version of tilt angle and enhanced horizontal gradient amplitude has been estimated with synthetic gravity anomalies before applying to the gravity data of the Southwest Sub-basin. For synthetic examples, the results show that the enhanced horizontal gradient amplitude not only provides edges with high resolution but also prevents spurious edges in the output maps. For real case, the obtained structural map is consistent with many faults already recognized in the Sub-basin, and the results provide new information leading to a better understanding of the structural framework and tectonic setting of the area.

Combining First- and Second-Order Continuity Constraints in Ultrasound Elastography.

Ultrasound elastography is a prominent noninvasive medical imaging technique that estimates tissue elastic properties to detect abnormalities in an organ. A common approximation to tissue elastic modulus is tissue strain induced after mechanical stimulation. To compute tissue strain, ultrasound radio frequency (RF) data can be processed using energy-based algorithms. These algorithms suffer from ill-posedness to tackle. A continuity constraint along with the data amplitude similarity is imposed to obtain a unique solution to the time-delay estimation (TDE) problem. Existing energy-based methods exploit the first-order spatial derivative of the displacement field to construct a regularizer. This first-order regularization scheme alone is not fully consistent with the mechanics of tissue deformation while perturbed with an external force. As a consequence, state-of-the-art techniques suffer from two crucial drawbacks. First, the strain map is not sufficiently smooth in uniform tissue regions. Second, the edges of the hard or soft inclusions are not well-defined in the image. Herein, we address these issues by formulating a novel regularizer taking both first- and second-order derivatives of the displacement field into account. The second-order constraint, which is the principal novelty of this work, contributes both to background continuity and edge sharpness by suppressing spurious noisy edges and enhancing strong boundaries. We name the proposed technique: Second-Order Ultrasound eLastography (SOUL). Comparative assessment of qualitative and quantitative results shows that SOUL substantially outperforms three recently developed TDE algorithms called Hybrid, GLUE, and MPWC-Net++. SOUL yields 27.72%, 62.56%, and 81.37% improvements of the signal-to-noise ratio (SNR) and 72.35%, 54.03%, and 65.17% improvements of the contrast-to-noise ratio (CNR) over GLUE with data pertaining to simulation, phantom, and in vivo tissue, respectively. The SOUL code can be downloaded from code.sonography.ai.

A contour property based approach to segment nuclei in cervical cytology images

BackgroundSegmentation of nuclei in cervical cytology pap smear images is a crucial stage in automated cervical cancer screening. The task itself is challenging due to the presence of cervical cells with spurious edges, overlapping cells, neutrophils, and artifacts.MethodsAfter the initial preprocessing steps of adaptive thresholding, in our approach, the image passes through a convolution filter to filter out some noise. Then, contours from the resultant image are filtered by their distinctive contour properties followed by a nucleus size recovery procedure based on contour average intensity value.ResultsWe evaluate our method on a public (benchmark) dataset collected from ISBI and also a private real dataset. The results show that our algorithm outperforms other state-of-the-art methods in nucleus segmentation on the ISBI dataset with a precision of 0.978 and recall of 0.933. A promising precision of 0.770 and a formidable recall of 0.886 on the private real dataset indicate that our algorithm can effectively detect and segment nuclei on real cervical cytology images. Tuning various parameters, the precision could be increased to as high as 0.949 with an acceptable decrease of recall to 0.759. Our method also managed an Aggregated Jaccard Index of 0.681 outperforming other state-of-the-art methods on the real dataset.ConclusionWe have proposed a contour property-based approach for segmentation of nuclei. Our algorithm has several tunable parameters and is flexible enough to adapt to real practical scenarios and requirements.

Automatic Ear Localization Using Entropy-Based Binary Jaya Algorithm and Weighted Hausdorff Distance

This paper presents an automatic human ear localization technique for handling uncontrolled scenarios such as illumination variation, poor contrast, partial occlusion, pose variation, ear ornaments, and background noise. The authors developed entropy-based binary Jaya algorithm (EBJA) and weighted doubly modified Hausdorff distance (W-MHD) to use edge information rather than pixels intensity values of the side face image. First, it embodies skin segmentation procedure using skin color model and successively remove spurious and non-ear edges which reduces the search space of the skin regions. Secondly, EBJA is proposed to trace dense edge regions as probable ear candidates. Thirdly, this paper developed an edge based weight function to represent the ear shape along with for the edge based template matching using W-MHD to identify true ear from a set of probable ear candidates. Experimental results using publicly available benchmark datasets demonstrate the competitiveness of the proposed technique in comparison to the state-of-the-art methods.

Multiscale Adaptive Edge Detector for Images Based on a Novel Standard Deviation Map

Edge detection plays an important role in many applications, such as industrial inspection and automatic driving. However, it is difficult to effectively distinguish between faint edges and noise, which may result in losing effective edges or generating spurious edges. This will reduce the accuracy of edge detection. In addition, some parameters need to be set artificially. In the case of the fixed parameters, the overall performance of edge detection on different images is not high. The adaptivity of edge detection needs to be improved further. To solve these problems, this article proposes a multiscale adaptive edge detector for images. First, multiscale pyramid images are constructed from an input image to provide multiscale features for edge detection. At each scale, a gradient map and a novel standard deviation map are calculated based on the gradients and the statistical characteristics of the local gradient differences, respectively, to accurately distinguish the edges from the background and noise. By using these two feature maps, candidate edges are adaptively identified from the image by using pixel-by-pixel detection. Then, candidate edges at different scales are thinned and fused together based on a novel voting mechanism. Finally, a binarized edge map is obtained by using adaptive hysteresis linking. These steps make the proposed edge detector accurate and adaptive. Experiments demonstrate that the proposed edge detector achieves good performance, which is beneficial to measurement applications.

A new high resolution filter for source edge detection of potential field data

Determining the source edges is a frequently requested task in the analysis of potential fields. However, the edge detection methods have some drawbacks or shortcomings, for example, blurred responses to weak amplitude anomalies, the estimated peaks over the edges are low in gradient or bring out overestimated edges in the resulting maps. In this paper, a new method based on an arcsine function using the ratio of the vertical gradient to the total gradient of the amplitude of the horizontal gradient field is introduced for outlining the source edges. The validity of the method is tested on both synthetic and real data and the edge detection results are compared with those detected from some other existing methods. The results from the proposed filter provide more accurate and higher resolution edges, and can avoid producing the spurious edges in the results.