Complex Graph Research Articles

On p -Group Isomorphism: Search-to-Decision, Counting-to-Decision, and Nilpotency Class Reductions via Tensors

In this article, we study some classical complexity-theoretic questions regarding Group Isomorphism ( GpI ). We focus on p -groups (groups of prime power order) with odd p , which are believed to be a bottleneckcase for GpI , and work in the model of matrix groups over finite fields. Our main results are as follows: • Although search-to-decision and counting-to-decision reductions have been known for more than four decades for Graph Isomorphism , they had remained open for GpI , explicitly asked by Arvind and Torán ( EATCS Bull. , 2005). Extending methods from Tensor Isomorphism (TI) (Grochow and Qiao, ITCS 2021), we show moderately exponential-time such reductions within p -groups of class 2 and exponent p . • Despite the widely held belief that p -groups of class 2 and exponent p are the hardest cases of GpI , there was no reduction to these groups from any larger class of groups. Again using methods from TI (ibid.), we show the first such reduction, namely from isomorphismtesting of p -groups of “small” class and exponent p to those of class two and exponent p . For the first results, our main innovation is to develop linear-algebraic analogues of classical graph coloring gadgets, a key technique in studying the structural complexity of Graph Isomorphism . Unlike the graph coloring gadgets, which support restricting to various subgroups of the symmetric group, the problems we study require restricting to various subgroups of the general linear group, which entails significantly different and more complicated gadgets. The analysis of one of our gadgets relies on a classical result from group theory regarding random generation of classical groups (Kantor and Lubotzky, Geom. Dedicata , 1990). For the nilpotency class reduction, we combine a runtime analysis of the Lazard correspondence with TI-completeness results (Grochow and Qiao, ibid.).

Graph-theoretical prediction of biological modules in quaternary structures of large protein complexes.

The functional complexity of biochemical processes is strongly related to the interplay of proteins and their assembly into protein complexes. In recent years, the discovery and characterization of protein complexes have substantially progressed through advances in cryo-electron microscopy, proteomics, and computational structure prediction. This development results in a strong need for computational approaches to analyse the data of large protein complexes for structural and functional characterization. Here, we aim to provide a suitable approach, which processes the growing number of large protein complexes, to obtain biologically meaningful information on the hierarchical organization of the structures of protein complexes. We modelled the quaternary structure of protein complexes as undirected, labelled graphs called complex graphs. In complex graphs, the vertices represent protein chains and the edges spatial chain-chain contacts. We hypothesized that clusters based on the complex graph correspond to functional biological modules. To compute the clusters, we applied the Leiden clustering algorithm. To evaluate our approach, we chose the human respiratory complex I, which has been extensively investigated and exhibits a known biological module structure experimentally validated. Additionally, we characterized a eukaryotic group II chaperonin TRiC/CCT and the head of the bacteriophage Φ29. The analysis of the protein complexes correlated with experimental findings and indicated known functional, biological modules. Using our approach enables not only to predict functional biological modules in large protein complexes with characteristic features but also to investigate the flexibility of specific regions and coformational changes. The predicted modules can aid in the planning and analysis of experiments. Jupyter notebooks to reproduce the examples are available on our public GitHub repository: https://github.com/MolBIFFM/PTGLtools/tree/main/PTGLmodulePrediction.

Lower Bounds on the Homology of Vietoris–Rips Complexes of Hypercube Graphs

We provide novel lower bounds on the Betti numbers of Vietoris–Rips complexes of hypercube graphs of all dimensions and at all scales. In more detail, let Qn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_n$$\\end{document} be the vertex set of 2n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2^n$$\\end{document} vertices in the n-dimensional hypercube graph, equipped with the shortest path metric. Let VR(Qn;r)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{VR}(Q_n;r)$$\\end{document} be its Vietoris–Rips complex at scale parameter r≥0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r \\ge 0$$\\end{document}, which has Qn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_n$$\\end{document} as its vertex set, and all subsets of diameter at most r as its simplices. For integers r<r′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r<r'$$\\end{document} the inclusion VR(Qn;r)↪VR(Qn;r′)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{VR}(Q_n;r)\\hookrightarrow \ extrm{VR}(Q_n;r')$$\\end{document} is nullhomotopic, meaning no persistent homology bars have length longer than one, and we therefore focus attention on the individual spaces VR(Qn;r)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{VR}(Q_n;r)$$\\end{document}. We provide lower bounds on the ranks of homology groups of VR(Qn;r)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{VR}(Q_n;r)$$\\end{document}. For example, using cross-polytopal generators, we prove that the rank of H2r-1(VR(Qn;r))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H_{2^r-1}(\ extrm{VR}(Q_n;r))$$\\end{document} is at least 2n-(r+1)nr+1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2^{n-(r+1)}\\left( {\\begin{array}{c}n\\\\ r+1\\end{array}}\\right) $$\\end{document}. We also prove a version of homology propagation: if q≥1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$q\\ge 1$$\\end{document} and if p is the smallest integer for which rankHq(VR(Qp;r))≠0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{rank}H_q(\ extrm{VR}(Q_p;r))\ e 0$$\\end{document}, then rankHq(VR(Qn;r))≥∑i=pn2i-pi-1p-1·rankHq(VR(Qp;r))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{rank}H_q(\ extrm{VR}(Q_n;r)) \\ge \\sum _{i=p}^n 2^{i-p} \\left( {\\begin{array}{c}i-1\\\\ p-1\\end{array}}\\right) \\cdot \ extrm{rank}H_q(\ extrm{VR}(Q_p;r))$$\\end{document} for all n≥p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n \\ge p$$\\end{document}. When r≤3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r\\le 3$$\\end{document}, this result and variants thereof provide tight lower bounds on the rank of Hq(VR(Qn;r))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H_q(\ extrm{VR}(Q_n;r))$$\\end{document} for all n, and for each r≥4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r \\ge 4$$\\end{document} we produce novel lower bounds on the ranks of homology groups. Furthermore, we show that for each r≥2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r\\ge 2$$\\end{document}, the homology groups of VR(Qn;r)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{VR}(Q_n;r)$$\\end{document} for n≥2r+1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n \\ge 2r+1$$\\end{document} contain propagated homology not induced by the initial cross-polytopal generators.

Graph states of atomic ensembles engineered by photon-mediated entanglement

Graph states are a broad family of entangled quantum states, each defined by a graph composed of edges representing the correlations between subsystems. Such states constitute versatile resources for quantum computation and quantum-enhanced measurement. Their generation and engineering require a high level of control over entanglement. Here we report on the generation of continuous-variable graph states of atomic spin ensembles, which form the nodes of the graph. We program the entanglement structure encoded in the graph edges by combining global photon-mediated interactions in an optical cavity with local spin rotations. By tuning the entanglement between two subsystems, we either localize correlations within each subsystem or enable Einstein–Podolsky–Rosen steering—a strong form of entanglement that enables the extraction of precise information from one subsystem through measurements on the other. We further engineer a four-mode square graph state, highlighting the flexibility of our approach. Our method is scalable to larger and more complex graphs, laying groundwork for measurement-based quantum computation and advanced protocols in quantum metrology.

Virtual geo-cyber environments: metaphorical visualization of virtual cyberspace with geographical knowledge

ABSTRACT Cyberspace is a dynamic and complex environment that poses unique challenges in understanding and managing its intricacies. In this study, we developed a framework for ‘Geo-Cyber’ spatial correlation mapping and expression, leveraging geographical information to enhance the understanding of cyberspace. The theory of geographic metaphor is incorporated into the framework to provide a comprehensive and intuitive approach for comprehending the interplay between geographic space and cyberspace. The framework enables the modeling and visual expression of geo-cyber correlations using a layered knowledge-graph representation. To graphically represent the complex knowledge graph within cyberspace, the framework uses an ‘Object-VirtualObject-Process-Decision’ layered modeling approach, which facilitates effective navigation and comprehension of the inherent cyberspace dynamics. Based on the experimental data, we employed a virtual geo-cyber environment system to demonstrate the effectiveness of the framework. The application cases include resource navigation and situational awareness in cyberspace to showcase the practicality and relevance of the proposed framework. The analysis indicated that the framework enhances situational awareness, facilitates effective decision-making, and promotes a holistic comprehension of the interplay between geography and cyberspace by leveraging geographic information and incorporating metaphorical cognition, layered modeling, and visualization techniques.

On connected [formula omitted]-gain graphs with rank equal to girth

Let Φ=(Γ,φ) be a T-gain graph or a complex unit gain graph and A(Φ) be its adjacency matrix. The rank of (Γ,φ) is denoted by r(Γ,φ) which is the rank of its adjacency matrix. If the underlying graph Γ of Φ has at least one cycle, then the girth of Φ is denoted by gr(Γ) or simply by gr, which is the length of the shortest cycle in Γ. In this paper, we prove r(Γ,φ)≥gr−2 for a T-gain graph (Γ,φ). Moreover, we characterize T-gain graphs satisfy r(Γ,φ)=gr−2 and r(Γ,φ)=gr.

Attribute Graph Embedding Based on Multi-Order Adjacency Views and Attention Mechanisms

Graph embedding plays an important role in the analysis and study of typical non-Euclidean data, such as graphs. Graph embedding aims to transform complex graph structures into vector representations for further machine learning or data mining tasks. It helps capture relationships and similarities between nodes, providing better representations for various tasks on graphs. Different orders of neighbors have different impacts on the generation of node embedding vectors. Therefore, this paper proposes a multi-order adjacency view encoder to fuse the feature information of neighbors at different orders. We generate different node views for different orders of neighbor information, consider different orders of neighbor information through different views, and then use attention mechanisms to integrate node embeddings from different views. Finally, we evaluate the effectiveness of our model through downstream tasks on the graph. Experimental results demonstrate that our model achieves improvements in attributed graph clustering and link prediction tasks compared to existing methods, indicating that the generated embedding representations have higher expressiveness.

Improving RGB illuminant estimation exploiting spectral average radiance.

We introduce a method that enhances RGB color constancy accuracy by combining neural network and k-means clustering techniques. Our approach stands out from previous works because we combine multispectral and color information together to estimate illuminants. Furthermore, we investigate the combination of the illuminant estimation in the RGB color and in the spectral domains, as a strategy to provide a refined estimation in the RGB color domain. Our investigation can be divided into three main points: (1)identify the spatial resolution for sampling the input image in terms of RGB color and spectral information that brings the highest performance; (2)determine whether it is more effective to predict the illuminant in the spectral or in the RGB color domain, and finally, (3)assuming that the illuminant is in fact predicted in the spectral domain, investigate if it is better to have a loss function defined in the RGB color or spectral domain. Experimental results are carried out on NUS: a standard dataset of multispectral radiance images with an annotated spectral global illuminant. Among the several considered options, the best results are obtained with a model trained to predict the illuminant in the spectral domain using an RGB color loss function. In terms of comparison with the state of the art, this solution improves the recovery angular error metric by 66% compared to the best tested spectral method, and by 41% compared to the best tested RGB method.

PeerG: A P2P botnet detection method based on representation learning and graph contrastive learning

P2P botnets are distributed with complex topology and communication behavior, making them harder to detect and remove. Individuals or organizations can effectively detect P2P botnets by analyzing abnormal behaviors in network traffic. Existing works focus on extracting deterministic traffic interaction features, which are highly dependent on statistical features. Moreover, these methods are mainly aimed at generalized botnet detection and lack specificity for detecting P2P botnets. They ignore the topological information of P2P botnets, resulting in unsatisfactory detection accuracy. Among the few existing methods targeting P2P botnets, they consider the topological information but mainly rely on classical graph theory statistical features, such as degree, feature vector centrality, etc. This limits the generalization ability of the detection model. In this paper, we delve into the topological features of P2P botnets from the perspective of complex graph theory. We propose a P2P botnet detection method that combines representation learning and graph contrastive learning, dubbed PeerG. Specifically, we construct the communication graphs based on the flow interaction behavior between components of the P2P botnets. The Line algorithm is employed to embed the nodes of the communication graph into a low-dimensional representation vector space. Subsequently, the graph contrastive learning approach is utilized to optimize the feature extractor, enabling it to capture more representative node features. PeerG serves as a benchmark detection model for identifying P2P botnet nodes. Additionally, we devise two optimized contrastive detection strategies (PeerG-PreG and PeerG-PreF) based on graph-level and feature-level to boost the performance of PeerG. Extensive experiments demonstrate that PeerG brings significant improvements in detection accuracy over state-of-art detection methods. Furthermore, compared with PeerG, the detection strategies PeerG-PreG and PeerG-PreF have further improved detection performance, and achieve the best detection accuracy among multiple filtered P2P botnet types.

A self-stabilizing and auto-provisioning orchestration for microservices in edge-cloud continuum

Modern user-facing services are progressively evolving from large monolithic applications to complex graphs of loosely-coupled microservices. While this shift provides opportunities to offload some microservices of a user-facing service to edge devices that are close to the end users, it also complicates the application deployment and resource provisioning in the edge-cloud continuum, due to complex relationships across microservices and unstable public networks. To reduce resource wastage and improve user experience, this paper presents SMO, a self-managed orchestration system for microservices. SMO leverages a self-stabilizing placement mechanism to optimally deploy microservices through perceiving both interferences and communication overheads. During runtime, it further tailors a multi-agent deep deterministic policy gradient (MADDPG)-based model in combination with attention and prioritized replay, which automatically provisions appropriate resources for each microservice subject to the differentiated tail latency Service Level Objectives (SLOs) of multiple user workloads. Experimental results demonstrate that SMO saves up to 39% of CPU cores and 47% of memory footprint while providing guarantees for heterogeneous tail latency SLOs.

Computation of Structural Descriptors of Pyrene Cored Dendrimers through Quotient Graph Approach and Its Graph Entropy Measures

Dendrimers are highly defined hyperbranched artificial macromolecules, synthesised by convergent or divergent approach with specific applications in various fields. Dendrimers can be represented as graph models, from which a quantitative description can be drawn in relation with their structural properties. The distance-based and the degree-based descriptors have great importance and huge applications in structural chemistry. These indices together with entropy measures are found to be more effective and have found application in scientific fields. The idea of graph entropy is to characterise the complexity of graphs. The use of these graph invariants in quantitative structure property relationship and quantitative structure activity relationship studies has become of major interest in recent years. In this paper, the distance-based molecular descriptors of pyrene cored dendrimers are studied applying the technique of converting original graph into quotient graphs using Θ∗-classes. It is to be noted that, since the pyrene cored dendrimer, Gn is not a partial cube, usual cut method is not applicable. Further, various degree-based descriptors and their corresponding graph entropies of the pyrene cored dendrimers are also studied. Based on the obtained results, a comparative analysis as well as a regression analysis was carried out.

Nighttime Thermal Infrared Image Translation Integrating Visible Images

Nighttime Thermal InfraRed (NTIR) image colorization, also known as the translation of NTIR images into Daytime Color Visible (DCV) images, can facilitate human and intelligent system perception of nighttime scenes under weak lighting conditions. End-to-end neural networks have been used to learn the mapping relationship between temperature and color domains, and translate NTIR images with one channel into DCV images with three channels. However, this mapping relationship is an ill-posed problem with multiple solutions without constraints, resulting in blurred edges, color disorder, and semantic errors. To solve this problem, an NTIR2DCV method that includes two steps is proposed: firstly, fuse Nighttime Color Visible (NCV) images with NTIR images based on an Illumination-Aware, Multilevel Decomposition Latent Low-Rank Representation (IA-MDLatLRR) method, which considers the differences in illumination conditions during image fusion and adjusts the fusion strategy of MDLatLRR accordingly to suppress the adverse effects of nighttime lights; secondly, translate the Nighttime Fused (NF) image to DCV image based on HyperDimensional Computing Generative Adversarial Network (HDC-GAN), which ensures feature-level semantic consistency between the source image (NF image) and the translated image (DCV image) without creating semantic label maps. Extensive comparative experiments and the evaluation metrics values show that the proposed algorithms perform better than other State-Of-The-Art (SOTA) image fusion and translation methods, such as FID and KID, which decreased by 14.1 and 18.9, respectively.

GraphSAGE++: Weighted Multi-scale GNN for Graph Representation Learning

Graph neural networks (GNNs) have emerged as a powerful tool in graph representation learning. However, they are increasingly challenged by over-smoothing as network depth grows, compromising their ability to capture and represent complex graph structures. Additionally, some popular GNN variants only consider local neighbor information during node updating, ignoring the global structural information and leading to inadequate learning and differentiation of graph structures. To address these challenges, we introduce a novel graph neural network framework, GraphSAGE++. Our model extracts the representation of the target node at each layer and then concatenates all layer weighted representations to obtain the final result. In addition, the strategies combining double aggregations with weighted concatenation are proposed, which significantly enhance the model’s discernment and preservation of structural information. Empirical results on various datasets demonstrate that GraphSAGE++ excels in vertex classification, link prediction, and visualization tasks, surpassing existing methods in effectiveness.

Conversational recommender based on graph sparsification and multi-hop attention

Conversational recommender systems provide users with item recommendations via interactive dialogues. Existing methods using graph neural networks have been proven to be an adequate representation of the learning framework for knowledge graphs. However, the knowledge graph involved in the dialogue context is vast and noisy, especially the noise graph nodes, which restrict the primary node’s aggregation to neighbor nodes. In addition, although the recurrent neural network can encode the local structure of word sequences in a dialogue context, it may still be challenging to remember long-term dependencies. To tackle these problems, we propose a sparse multi-hop conversational recommender model named SMCR, which accurately identifies important edges through matching items, thus reducing the computational complexity of sparse graphs. Specifically, we design a multi-hop attention network to encode dialogue context, which can quickly encode the long dialogue sequences to capture the long-term dependencies. Furthermore, we utilize a variational auto-encoder to learn topic information for capturing syntactic dependencies. Extensive experiments on the travel dialogue dataset show significant improvements in our proposed model over the state-of-the-art methods in evaluating recommendation and dialogue generation.

Mesoscale organization of ventral and dorsal visual pathways in macaque monkey revealed by 7T fMRI

In human and nonhuman primate brains, columnar (mesoscale) organization has been demonstrated to underlie both lower and higher order aspects of visual information processing. Previous studies have focused on identifying functional preferences of mesoscale domains in specific areas; but there has been little understanding of how mesoscale domains may cooperatively respond to single visual stimuli across dorsal and ventral pathways. Here, we have developed ultrahigh-field 7 T fMRI methods to enable simultaneous mapping, in individual macaque monkeys, of response in both dorsal and ventral pathways to single simple color and motion stimuli. We provide the first evidence that anatomical V2 cytochrome oxidase-stained stripes are well aligned with fMRI maps of V2 stripes, settling a long-standing controversy. In the ventral pathway, a systematic array of paired color and luminance processing domains across V4 was revealed, suggesting a novel organization for surface information processing. In the dorsal pathway, in addition to high quality motion direction maps of MT, MST and V3A, alternating color and motion direction domains in V3 are revealed. As well, submillimeter motion domains were observed in peripheral LIPd and LIPv. In sum, our study provides a novel global snapshot of how mesoscale networks in the ventral and dorsal visual pathways form the organizational basis of visual objection recognition and vision for action.

Public Opinion Evolution in Cyberspace: A Case Analysis of Pelosi’s Visit to Taiwan

The dynamics of public opinion on social media affects people’s feeling and minds about international affairs and leads to the reconstruction of societal states for international conflicts. In this article, we analyze the topics’ evolution on social media during the Pelosi visit. Such kind of analysis should help the related departments sense and beware the situation effectively and efficiently, and may provide technical supports for proper policy making and responses. To facilitate this purpose, a new method is proposed and an abbreviated large-graph clustering (ALGC) algorithm has been designed to generate documents and topic representation for alleviating the overhead of high computational complexity of large graphs by reducing the dimensionality of the attention matrix and adjacency matrix. The evolution pattern of topics is also analyzed in and between different time periods. Experiment results show that the proposed method performs well, achieving a high clustering accuracy with lower computational cost. The dataset used in this article is also released for public analysis.

Motion states identification of underwater glider based on complex networks and graph convolutional networks

Underwater glider (UG) plays an important role in ocean observation and exploration for a more efficient and deeper understanding of complex ocean environment. Timely identifying the motion states of UG is conducive for timely attitude adjustment and detection of potential anomalies, thereby improving the working reliability of UG. Combining limited penetrable visibility graph (LPVG) and graph convolutional networks (GCN) with self-attention mechanisms, we propose a novel method for motion states identification of UG, which is called as visibility graph and self-attention mechanism-based graph convolutional network (VGSA-GCN). Based on the actual sea trial data of UG, we chose the attitude angle signals of motion states related sensors collected by the control system of UG as the research object and constructed complex networks based on the LPVG method from pitch angle, roll angle, and heading angle data in diving and climbing states. Then, we build a self-attention mechanism-based GCN framework and classify the graphs under different motion states constructed by a complex network. Compared with support vector machines, convolutional neural network, and GCN without self-attention pooling layer, the proposed VGSA-GCN method can more accurately distinguish the diving and climbing states of UG. Subsequently, we analyze the variation of the transitivity coefficient corresponding to these two motion states. The results suggest that the coordination of the various sensors in the attitude adjustment unit during diving becomes closer and more efficient, which corresponds to the higher network measure of the diving state compared to the climbing state.

Graph Entropy for Adjacently and Non-Adjacently Configured Pentagonal Structure of Carbon Nanocones

aims: Carbon nanocones possess exceptional properties compared to other nanomaterials, such as nanotubes, graphene etc. Hence they can be used as an alternative to other nanostructures. This work helps in determining various properties of the nanocone structure through topological descriptors. In this paper, various degree based topological descriptors along with its entropy measures are evaluated for adjacently and non-adjacently configured pentagonal structure of carbon nanocones. background: Carbon nanoparticles are gaining much importance in the contemporary world. Among which carbon nanocones have wide range of acceptance in the field of nanotechnology due to its effective properties and applications. Nanocones, also known as nanohorns are carbon networks which are planar in structure and has majority of hexagonal faces along with some nonhexagonal faces, which are most commonly pentagons. Nanocones which include pentagons in their structure can be referred to as adjacently or non- adjacently configured pentagonal structure of nanocones. Various topological descriptors for few nanocone classes were derived by researchers earlier, but not for the class of nanocones in this paper. Through this work, we try to vanish the research gap in this field. objective: : The degree based descriptors and corresponding graph entropies for the adjacently and non-adjacently configured pentagonal structure of nanocones were determined, which further can be applied in quantitative structure – activity – property relationship studies. The concept of graph entropy is to assign a probability function to the edges in the chemical graph using the topological descriptor and it helps to characterise the complexity of graphs. method: We have employed the degree counting method and edge partition based on the vertices and edges of the adjacently and non-adjacently configured pentagonal nanocone structures to obtain the edge partitions and then using the corresponding mathematical expression, the degree based descriptors and its corresponding entropies were determined. result: Result: The analytically closed formulas to compute the degree based topological descriptors and graph entropies for any generation of the class of nanocone structures were obtained. conclusion: In this work, the degree based descriptors and the corresponding graph entropies for the adjacently and non-adjacently configured pentagonal structure of carbon nanocones are determined applying the degree counting method and edge partition based on the vertices and edges. Also a graphical comparative study was also done with the help of obtained results. other: end.

A distributed tracing pipeline for improving locality awareness of microservices applications

AbstractThe microservices architectural style aims at improving software maintenance and scalability by decomposing applications into independently deployable components. A common criticism about this style is the risk of increasing response times due to communication, especially with very granular entities. Locality‐aware placement of microservices onto the underlying hardware can contribute to keeping response times low. However, the complex graphs of invocations originating from users' calls largely depend on the specific workload (e.g., the length of an invocation chain could depend on the input parameters). Therefore, many existing approaches are not suitable for modern infrastructures where application components can be dynamically redeployed to take into account user expectations. This paper contributes to overcoming the limitations of static or off‐line techniques by presenting a big data pipeline to dynamically collect tracing data from running applications that are used to identify a given number of microservices groups whose deployment allows keeping low the response times of the most critical operations under a defined workload. The results, obtained in different working conditions and with different infrastructure configurations, are presented and discussed to draw the main considerations about the general problem of defining boundary, granularity, and optimal placement of microservices on the underlying execution environment. In particular, they show that knowing how a specific workload impacts the constituent microservices of an application, helps achieve better performance, by effectively lowering response time (e.g., up to a reduction), through the exploitation of locality‐driven clustering strategies for deploying groups of services.

Enhancing medical image analysis with unsupervised domain adaptation approach across microscopes and magnifications

In the domain of medical image analysis, deep learning models are heralding a revolution, especially in detecting complex and nuanced features characteristic of diseases like tumors and cancers. However, the robustness and adaptability of these models across varied imaging conditions and magnifications remain a formidable challenge. This paper introduces the Fourier Adaptive Recognition System (FARS), a pioneering model primarily engineered to address adaptability in malarial parasite recognition. Yet, the foundational principles guiding FARS lend themselves seamlessly to broader applications, including tumor and cancer diagnostics. FARS capitalizes on the untapped potential of transitioning from bounding box labels to richer semantic segmentation labels, enabling a more refined examination of microscopy slides. With the integration of adversarial training and the Color Domain Aware Fourier Domain Adaptation (F2DA), the model ensures consistent feature extraction across diverse microscopy configurations. The further inclusion of category-dependent context attention amplifies FARS’s cross-domain versatility. Evidenced by a substantial elevation in cross-magnification performance from 31.3% mAP to 55.19% mAP and a 15.68% boost in cross-domain adaptability, FARS positions itself as a significant advancement in malarial parasite recognition. Furthermore, the core methodologies of FARS can serve as a blueprint for enhancing precision in other realms of medical image analysis, especially in the complex terrains of tumor and cancer imaging. The code is available at; https://github.com/Mr-TalhaIlyas/FARS.