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Cell localization in medical image analysis is a challenging task due to the significant variation in cell shape, size and color. Existing localization methods continue to tackle these challenges separately, frequently facing complications where these difficulties intersect and adversely impact model performance. In this paper, these challenges are first reframed as issues of feature misalignment between cell images and location maps, which are then collectively addressed. Specifically, we propose a feature alignment model based on a multi-scale hypergraph attention network. The model considers local regions in the feature map as nodes and utilizes a learnable similarity metric to construct hypergraphs at various scales. We then utilize a hypergraph convolutional network to aggregate the features associated with the nodes and achieve feature alignment between the cell images and location maps. Furthermore, we introduce a stepwise adaptive fusion module to fuse features at different levels effectively and adaptively. The comprehensive experimental results demonstrate the effectiveness of our proposed multi-scale hypergraph attention module in addressing the issue of feature misalignment, and our model achieves state-of-the-art performance across various cell localization datasets. • This paper innovatively addresses the challenges stemming from significant variations in cell shape, scale, and color by reframing them as a feature misalignment problem between cell images and location maps, thereby presenting a unified solution to these complexities. • We propose an innovative multi-scale hypergraph attention module that achieves feature alignment through the adaptive aggregation of features across various scale ranges. • The proposed model achieves state-of-the-art performance on multiple cell localization datasets and reveals great potential. [ABSTRACT FROM AUTHOR]

How did cooperation emerge and persist while betrayal was beneficial for individuals? Previous studies suggested that network reciprocity was a promising explanation. However, these studies usually analyzed cooperation performances of different types of networks separately and/or statically, thus failing to fully capture the co-evolution and interaction features in our history. We proposed a mechanism that analyzed the cooperation level of different types of networks under co-evolutionary circumstances of cooperation and heterogeneous networks. The paper adopted the prisoner's dilemma game and analyzed Erdos-Renyi (ER) random networks, Watts Strogatz (WS) small-world networks, and Barabasi-Albert (BA) scale-free networks. In this study, these networks were interconnected and continuously grew after gaming evolutions were stable. And the growth versions of WS and ER respectively were proposed that maintained their topology features. Comprehensive experimental results showed that high-level cooperation emerged and was maintained in scale-free networks. This advantage was strengthened with networks grew from a small scale to a middle scale. Moreover, once most nodes of a scale-free network evolved to be cooperators, the group cooperation they formed were able to survive in betrayer-prevailing and high betrayal temptation environments. These findings may deepen our understanding of the relationship between how cooperation evolved and why many social networks exhibited scale-free properties. [ABSTRACT FROM AUTHOR]

Link prediction is an axial field in complex network analysis as it aims to infer new connections between nodes in a given network. Many applications of this task include: the reference system, the suggestion of friends in the social network and the prediction of interactions between proteins in biological networks. Several methods have been developed in the link prediction task, especially the similarity-based methods which are widely used due to their low complexity and good performance. In this paper, we propose a novel link prediction approach that combines the PageRank algorithm with local information-based methods to improve performance while retaining the advantage of low complexity of local methods. We conducted a series of experimental studies on eleven data sets where we compared our new combined methods with six well-known local methods. The results obtained show a significant gain in terms of performance in almost all data sets. In addition to this and to confirm the superiority of the proposed methods, another comparative study is performed, formed of nine local and global methods. According to the experimental results, our approach outperforms all other compared methods with linear complexity. • The analysis of networks evolution brings us to the problem of link prediction. • Similarity-based methods are used as a local method to predict new links. • New similarity measures between unconnected nodes that do not have shared neighbors. • Our method offers higher precision than some well-known local and global methods. • Our method offers linear computational complexity. [ABSTRACT FROM AUTHOR]

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning. [ABSTRACT FROM AUTHOR]

Role discovery is an emerging research area in the analysis of social networks, biological networks, and neural networks. The fundamental idea of role discovery is partitioning the vertices based on their structural features in a graph. However, most existing studies construct the role representations by learning the pairwise connectivity structures, thus weak to represent the relations among multiple vertices, namely higher-order structures. To this effect, this paper proposes a novel approach termed Low-Rank Persistent Probability representation (LRPP) for higher-order role discovery. In detail, we define local Dowker filtration to compute the persistent homology of the neighbor subgraph, which models the connection patterns among vertices and their neighbors. Then, we propose the Persistent Probability (PP) based on Dirichlet equivalence to vectorize the obtained features. Since the persistent probability matrix is sparse, we finally construct the low-rank representation to cluster the higher-order roles by embedding PP vectors into a series of linear subspaces. We conduct experiments of role discovery on both labeled and unlabeled datasets. The experimental results show that the proposed LRPP outperforms the baseline methods in role discovery and demonstrate that LRPP can effectively represent the higher-order role in graphs. • Define a local filtration to extract higher-order role features. • Propose a fast and interpretable method to vectorize role features. • Model the subspace structure of role vectors for clustering. [ABSTRACT FROM AUTHOR]

In current Optical Coherence Tomography (OCT)-based fingerprint recognition systems, Presentation Attack Detection (PAD) and subsurface fingerprint reconstruction are treated as two independent branches, resulting in high computation and system building complexity. Therefore, this paper proposes a uniform representation model for simultaneous PAD and subsurface fingerprint reconstruction. A novel semantic segmentation network using attention mechanisms was designed to extract and segment multiple subsurface structures from real finger slices (i.e., B-scans). The latent codes derived from the network are directly used to effectively detect PA because they contain abundant subsurface biological information, which is independent of PA materials and has strong robustness for unknown PAs. Segmented subsurface structures were adopted to reconstruct multiple subsurface 2D fingerprints. Extensive experiments were carried out on an in-house database, which is the largest public OCT-based fingerprint database with 2449 volumes. PAD performance was evaluated by comparing the results of the existing methods with those of other segmentation networks. The proposed uniform representation model can obtain an accuracy (Acc) of 96.63%, which achieves a state-of-the-art performance. The effectiveness of subsurface reconstruction was evaluated from the segmentation and recognition results. In the segmentation experiments, the proposed method achieved the best results with an Intersection of Union (mIOU) of 0.834 and a Pixel Accuracy of 0.937. By comparing the recognition performance on surface 2D fingerprints (e.g., commercial and high-resolution), the lowest results with an Equal Error Rate (EER) of 2.25% by minutiae matching and an EER of 5.42% by pore matching are achieved, which indicates the excellent reconstruction capability of the proposed uniform representation model. • A uniform representation model for OCT-based fingerprint PAD and reconstruction. • A novel semantic segmentation network applying biological structure features. • High security PAD is achieved without using any presentation attacks for training. • Reliable reconstruction result is obtained with subsurface biological information. [ABSTRACT FROM AUTHOR]