Connection Probability Research Articles

Structured multi-view k-means clustering

K-means is a very efficient clustering method and many multi-view k-means clustering methods have been proposed for multi-view clustering during the past decade. However, since k-means have trouble uncovering clusters of varying sizes and densities, these methods suffer from the same performance issues as k-means. Improving the clustering performance of multi-view k-means has become a challenging problem. In this paper, we propose a new multi-view k-means clustering method that is able to uncover clusters in arbitrary sizes and densities. The new method simultaneously performs three tasks, i.e., sparse connection probability matrices learning, prototypes aligning, and cluster structure learning. We evaluate the proposed new method by 5 benchmark datasets and compare it with 11 multi-view clustering methods. The experimental results on both synthetic and real-world experiments show the superiority of our proposed method.

Assessing Climate Change Impact on Habitat Suitability and Ecological Connectivity of Wych Elm (Ulmus glabra Huds.) in Türkiye

Understanding how climate change influences the geographical distribution of species within an ecological niche is essential for predicting habitat shifts and informing conservation efforts. This study evaluates the impact of climate change on habitat suitability and ecological connectivity of wych elm (Ulmus glabra Huds.) in Türkiye. The study explores the future distribution of U. glabra and how its connectivity is affected by habitat fragmentation arising from changing climatic conditions. Contextually, this paper aims to achieve two primary objectives: estimating the potential geographical ranges of U. glabra under different climate scenarios and assessing alterations in ecological connections between current and future habitats. The maximum entropy (MaxEnt) model was used along with Morphological Spatial Pattern Analysis (MSPA), and the Probability of Connectivity (PC) index was applied to show possible transformations in distribution patterns of U. glabra over time. The findings suggest that there will be a reduction in the suitability of locations for the species. Moreover, it is expected that under future climate scenarios, ecological connectivity will decline, especially from 2061 to 2100 in the SSP585 scenario. Notably, significant alterations are anticipated during the latter half of the twenty-first century, mainly outside the coastal areas of the Black Sea, where extensive regions would become unsuitable. Additionally, the species is projected to shift its range, decreasing its presence in inland regions while expanding along the coasts. The results show the vulnerability of this species against climate change, thereby demanding adaptive conservation measures to preserve it within the forest ecosystems of Türkiye.

Topology sensing of FANET under missing data

The topological structure of a flying ad hoc network (FANET) is crucial to understand, explain, and predict the behavior of unmanned aerial vehicle (UAV) swarms. Most studies focusing on topology sensing use perfect observations and complete datasets. However, the received signal dataset, being non-cooperative, commonly encounters instances of missing data, causing the performance of the existing algorithms to degrade. We investigate the issue of topology sensing of FANET based on external observations and propose a topology sensing method for FANET with missing data while introducing link-prediction methods to correct the topology inference results. First, we employ multi-dimensional Hawkes processes to model the communication event sequence in the network. Subsequently, to solve the problem in which the binary decision threshold is difficult to determine and cannot be adapted to the application scenario, we propose an extended multi-dimensional Hawkes model suitable for FANET and use the maximum likelihood estimation method for topology inference. Finally, to solve the problem of the low accuracy of inference results owing to missing data, we perform community detection on the observation network and combine the community detection and inference results to construct a mixed connection probability matrix, based on which we perform topology correction. The results of the analysis show that the topology sensing method proposed in this study is robust against missing data, indicating that it is an effective solution for solving this problem.

Contrasting topologies of synchronous and asynchronous functional brain networks

ABSTRACT We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy and compared aFN topology with the correlation-based synchronous functional networks (sFNs), which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and Neurodevelopment in Adolescence study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks. After adjusting for differences in network density, aFNs had higher global efficiency but lower local efficiency than the sFNs. In the aFNs, nodes with the highest outgoing global efficiency tended to be in the brainstem and orbitofrontal cortex. aFN nodes with the highest incoming global efficiency tended to be members of the default mode network in sFNs. Age interacted with node global efficiency in aFNs and node local efficiency in sFNs to influence connection probability. We conclude that the sFN and aFN both offer information about functional brain connectivity, which the other type of network does not.

Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space.

Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity of one hundred or more individual neurons. The interpretation of the recorded data calls for multiscale computational models with corresponding spatial dimensions and signal predictions. Multi-layer spiking neuron network models of local cortical circuits covering about $1\,{\text{mm}^{2}}$ have been developed, integrating experimentally obtained neuron-type-specific connectivity data and reproducing features of observed in-vivo spiking statistics. Local field potentials can be computed from the simulated spiking activity. We here extend a local network and local field potential model to an area of $4\times 4\,{\text{mm}^{2}}$, preserving the neuron density and introducing distance-dependent connection probabilities and conduction delays. We find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike-train correlations in agreement with experimental recordings from sensory cortex. Also compatible with experimental observations, the correlation of local field potential signals is strong and decays over a distance of several hundred micrometers. Enhanced spatial coherence in the low-gamma band around $50\,\text{Hz}$ may explain the recent report of an apparent band-pass filter effect in the spatial reach of the local field potential.

Optogenetic estimation of synaptic connections in brain slices

BackgroundDetection of synaptic connections is essential for understanding neural circuits. By using optogenetics, current injection, and glutamate uncaging to activate presynaptic cells and simultaneously recording the subsequent response of postsynaptic cells, the presence of synaptic connections can be confirmed. However, these methods present throughput challenges, such as the need for simultaneous multicellular patch-clamp recording and two-photon microscopy. These challenges lead to a trade-off between sacrificing resolution and experimental throughput. New methodWe adopted the laser, typically used for local field ablation, and combined this with post hoc analysis. We successfully approximated the synaptic connection probabilities using only an epi-fluorescence microscope and single-cell recordings. ResultsWe sequentially stimulated the channelrhodopsin 2-expressing cells surrounding the recorded cell and approximated the synaptic connection probabilities. This probability value was comparable to that obtained from simultaneous multi-cell patch-clamp recordings, which included more than 600 pairs. Comparison with existing methodsOur setup allows us to estimate connection probabilities within 100 s, outperforming existing methods. We successfully estimated synaptic connection probabilities using only the optical path typically used by an epi-fluorescence microscope and single-cell recordings. It may also be suitable for dendritic ablation experiments. ConclusionsThe proposed method simplifies the estimation of connection probabilities, which is expected to advance the study of neural circuits in conditions such as autism and schizophrenia where connection probabilities vary. Furthermore, this approach is applicable not only to local circuits but also to long-range connections, thus increasing experimental throughput.

SROR: A Secure and Reliable Opportunistic Routing for VANETs

In Vehicular Ad Hoc Networks (VANETs), high mobility of vehicles issues a huge challenge to the reliability and security of transmitting packets. Therefore, a Secure and Reliable Opportunistic Routing (SROR) is proposed in this paper. During construction of Candidate Forwarding Nodes (CFNs) set, the relative velocity, connectivity probability, and packet forwarding ratio are taken into consideration. The aim of SROR is to maximally improve the packet delivery ratio as well as reduce the end-to-end delay. The selection of a relay node from CFNs is formalized as a Markov Decision Process (MDP) optimization. The SROR algorithm extracts useful knowledge from historical behavior of nodes by interacting with the environment. This useful knowledge are utilized to select the relay node as well as to prevent the malicious nodes from forwarding packets. In addition, the influence of different learning rate and exploratory factor policy on rewards of agents are analyzed. The experimental results show that the performance of SROR outperforms the benchmarks in terms of the packet delivery ratio, end-to-end delay, and attack success ratio. As vehicle density ranges from 10 to 50 and percentage of malicious vehicles is fixed at 10%, the average of packet delivery ratio, end-to-end delay, and attack success ratio are 0.82, 0.26s, and 0.37, respectively, outperforming benchmark protocols.

Stochastic block hypergraph model.

The stochastic block model is widely used to generate graphs with a community structure, but no simple alternative currently exists for hypergraphs, in which more than two nodes can be connected together through a hyperedge. We discuss here such a hypergraph generalization, based on the clustering connection probability P_{ij} between nodes of communities i and j, and that uses an explicit and modulable hyperedge formation process. We focus on the standard case where P_{ij}=pδ_{ij}+q(1-δ_{ij}) when 0≤q≤p (δ_{ij} is the Kronecker symbol). We propose a simple model that satisfies three criteria: it should be as simple as possible, when p=q the model should be equivalent to the standard hypergraph random model, and it should use an explicit and modulable hyperedge formation process so that the model is intuitive and can easily express different real-world formation processes. We first show that for such a model the degree distribution and hyperedge size distribution can be approximated by binomial distributions with effective parameters that depend on the number of communities and q/p. Also, the composition of hyperedges goes for q=0 from 'pure' hyperedges (comprising nodes belonging to the same community) to 'mixed' hyperedges that comprise nodes from different communities for q=p. We test various formation processes and our results suggest that when they depend on the composition of the hyperedge, they tend to favor the dominant community and lead to hyperedges with a smaller diversity. In contrast, for formation processes that are independent from the hyperedge structure, we obtain hyperedges comprising a larger diversity of communities. The advantages of the model proposed here are its simplicity and flexibility that make it a good candidate for testing community-related problems, such as their detection, impact on various dynamics, and visualization.

Geometric Scaling Law in Real Neuronal Networks.

We investigate the synapse-resolution connectomes of fruit flies across different developmental stages, revealing a consistent scaling law in neuronal connection probability relative to spatial distance. This power-law behavior significantly differs from the exponential distance rule previously observed in coarse-grained brain networks. We demonstrate that the geometric scaling law carries functional significance, aligning with the maximum entropy of information communication and the functional criticality balancing integration and segregation. Perturbing either the empirical probability model's parameters or its type results in the loss of these advantageous properties. Furthermore, we derive an explicit quantitative predictor for neuronal connectivity, incorporating only interneuronal distance and neurons' in and out degrees. Our findings establish a direct link between brain geometry and topology, shedding lights on the understanding of how the brain operates optimally within its confined space.

Reorganization of structural connectivity in the brain supports preservation of cognitive ability in healthy aging.

The global population is aging rapidly, and a research question of critical importance is why some older adults suffer tremendous cognitive decline while others are mostly spared. Past aging research has shown that older adults with spared cognitive ability have better local short-range information processing while global long-range processing is less efficient. We took this research a step further to investigate whether the underlying structural connections, measured in vivo using diffusion magnetic resonance imaging (dMRI), show a similar shift to support cognitive ability. We analyzed the structural connectivity streamline probability (representing the probability of connection between regions) and nodal efficiency and local efficiency regional graph theory metrics to determine whether age and cognitive ability are related to structural network differences. We found that the relationship between structural connectivity and cognitive ability with age was nuanced, with some differences with age that were associated with poorer cognitive outcomes, but other reorganizations that were associated with spared cognitive ability. These positive changes included strengthened local intrahemispheric connectivity and increased nodal efficiency of the ventral occipital-temporal stream, nucleus accumbens, and hippocampus for older adults, and widespread local efficiency primarily for middle-aged individuals.

Euclidean mirrors and dynamics in network time series

– Analyzing changes in network evolution is central to statistical network inference. We consider a dynamic network model in which each node has an associated time-varying low-dimensional latent vector of feature data, and connection probabilities are functions of these vectors. Under mild assumptions, the evolution of latent vectors exhibits low-dimensional manifold structure under a suitable distance. This distance can be approximated by a measure of separation between the observed networks themselves, and there exist Euclidean representations for underlying network structure, as characterized by this distance. These Euclidean representations, called Euclidean mirrors, permit the visualization of network dynamics and lead to methods for change point and anomaly detection in networks. We illustrate our methodology with real and synthetic data, and identify change points corresponding to massive shifts in pandemic policies in a communication network of a large organization.

Association between rheumatoid arthritis and chronic respiratory diseases in a Japanese population: A Mendelian randomization study.

Past observational studies have documented an association between rheumatoid arthritis (RA) and chronic respiratory diseases. Undertaking the approach of Mendelian randomization (MR) analysis, this research aims to delve deeper into the probability of a causal connection between RA and chronic respiratory diseases. Collated genome-wide association study data covering RA with 4199 cases against 208,254 controls, asthma comprising 8216 cases versus 201,592 controls, and chronic obstructive pulmonary disease (COPD) detailing 3315 cases in contrast to 201,592 controls were derived from the repository of the Japanese Biobank. A selection of 10 RA-related, 8 asthma-related, and 4 COPD-related single nucleotide polymorphisms notable for their statistical significance (P < 5 × 10-8) were identified as instrumental variables. The primary analytical technique was the inverse variance-weighted (IVW) method, alongside the MR-Egger protocol, weighted median, and weighted mode to reinforce the validity and solidity of the principal results. For scrutinizing possible implications of horizontal pleiotropy, we harnessed the MR-Egger intercept examination and the Mendelian Randomization Pleiotropy REsidual Sum and Outlier test. Employing the inverse variance-weighted technique, we established a positive correlation between genetic predispositions for RA and actual occurrences of asthma (odds ratios [OR] = 1.14; 95% confidence intervals [CI]: 1.04-1.24; P = .003). This correlation remained strong when testing the results utilizing various methods, including the MR-Egger method (OR = 1.32; 95% CI: 1.09-1.60; P = .023), the weighted median (OR = 1.16; 95% CI: 1.06-1.26; P < .001), and the weighted mode (OR = 1.21; 95% CI: 1.11-1.32; P = .002). Furthermore, our findings from the inverse variance-weighted method also demonstrated a positive association between genetically predicted RA and COPD (OR = 1.12; 95% CI: 1.02-1.29; P = .021). However, no such link was discerned in supplementary analyses. In a shifted perspective-the reverse MR analysis-no correlation was identified between genetically predicted instances of asthma (IVW, P = .717) or COPD (IVW, P = .177) and RA. The findings confirm a causal correlation between genetically predicted RA and an elevated risk of either asthma or COPD. In contrast, our results offer no support to the presumed causal relationship between genetic susceptibility to either asthma or COPD and the subsequent development of RA.

Effects of the area of source habitat and habitat connectivity on soybean pod borer damage: Implications for area-wide management

The soybean pod borer, Leguminivora glycinivorella (Matsumura), is an important soybean pest that reduces both quality and yield in Asia, and chemical control is used irrespective of the damage risk. The pest population density increases at continuously cropped soybean fields (hereafter, source habitat). The present study would contribute to proposing pest management options matched to the damage risk by clarifying the surrounding landscape structures where damage is likely to increase. First, we clarified the relationship between the probability of grain damage and the source habitat within a 350 m radius of the new or continuously cropped soybean fields. The habitat area surrounding continuous soybean fields positively influenced the probability of grain damage but did not detect a significant effect in new soybean fields. Secondly, we clarified the relationship between the probability of grain damage and habitat connectivity in continuous soybean fields. Well-managed fields with high connectivity consistently showed low damage risk. Meanwhile, damage risk varied in fields with low connectivity, indicating that damage is unlikely to occur because soybean pod borers are difficult to invade. However, once invaded, the damage could be extensive. Considering these results, one possible new control system will allow the spatiotemporal variation in the number of insecticide applications. The present study likely contributes to understanding the relationship between soybean pod borer injury and landscape structure, which is important for area-wide pest management.

Incorporating barriers restoration and stepping stones establishment to enhance the connectivity of watershed ecological security patterns

Enhancing the connectivity of watershed ecological security patterns (ESPs) is increasingly emphasized for preserving ecological processes. Yet the importance of small-scale conservation and restoration was ignored and few studies have quantitatively compared the contribution of barriers restoration and stepping stones establishment to landscape connectivity. In this study, taking Dongting Lake Basin as an example, a watershed ESP was constructed based on the minimum cumulative resistance model and optimized through graded barriers restoration and different stepping stones establishment. Then the enhancement effects of landscape connectivity were compared to identify the most cost-effective optimization scheme. The results showed that the average of six schemes only protected and repaired less than 1% of the total area, which could enhance corridor connectivity by about 12% and 16% for average corridor length and corridor cumulative resistance respectively. For the optimal ESP, establishing natural patches near the midpoint of longer corridors as stepping stones, increased the probability of connectivity by 21.05%, and reduced the average corridor length and corridor cumulative resistance of corridor connectivity by 17.99% and 15.48% respectively. It also increased network circuitry index of network connectivity from 0.541 to 0.570, compared with the original ESP, indicating the possibility of successful ecological flow increased effectively. It can be concluded that the connectivity enhancement of stepping stones approach was better than barriers restoration approach. This study highlights the importance of small-scale barriers restoration and stepping stones establishment in enhancing landscape connectivity.

Contrasting topologies of synchronous and asynchronous functional brain networks.

We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy (oCSE) and compared aFN topology to the correlation-based synchronous functional networks (sFNs) which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks. After adjusting for differences in network density, aFNs had higher global efficiency but lower local efficiency than the sFNs. In the aFNs, nodes with the highest outgoing global efficiency tended to be in the brainstem and orbitofrontal cortex. aFN nodes with the highest incoming global efficiency tended to be members of the Default Mode Network (DMN) in sFNs. Age interacted with node global efficiency in aFNs and node local efficiency in sFNs to influence connection probability. We conclude that the sFN and aFN both offer information about functional brain connectivity which the other type of network does not.

Noise Enhances Excitability of a Neuronal Population with Heterogeneous Excitatory Neurons

AbstractCortical excitability is sensitive to noise perturbations despite homeostatic regulation. However, modeling the effect of noise in a heterogeneous neuronal population is a mathematical challenge. Using a mean‐field model of a randomly connected excitatory population, we investigated how noise affects excitability at the resting state. Stable equilibrium analysis revealed that increasing noise intensity enhances excitability, indexed by synaptic conductance. Further, this enhancement disappeared when excitability is sufficiently high due to strong mean input current, but not influenced by the connection probability of the population. Finally, the mechanism of noise‐induced enhancement was explained by eigenvalues moving toward the imaginary axis. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Integrating brain function and structure in the study of the human attentional networks: a functionnectome study

Attention is a heterogeneous function theoretically divided into different systems. While functional magnetic resonance imaging (fMRI) has extensively characterized their functioning, the role of white matter in cognitive function has gained recent interest due to diffusion-weighted imaging advancements. However, most evidence relies on correlations between white matter properties and behavioral or cognitive measures. This study used a new method that combines the signal from distant voxels of fMRI images using the probability of structural connection given by high-resolution normative tractography. We analyzed three fMRI datasets with a visual perceptual task and three attentional manipulations: phasic alerting, spatial orienting, and executive attention. The phasic alerting network engaged temporal areas and their communication with frontal and parietal regions, with left hemisphere dominance. The orienting network involved bilateral fronto-parietal and midline regions communicating by association tracts and interhemispheric fibers. The executive attention network engaged a broad set of brain regions and white matter tracts connecting them, with a particular involvement of frontal areas and their connections with the rest of the brain. These results partially confirm and extend previous knowledge on the neural substrates of the attentional system, offering a more comprehensive understanding through the integration of structure and function.

A reliable cluster-based opportunistic routing protocol for underwater wireless sensor networks

Underwater Acoustic Sensor Networks (UASNs) have gained significant popularity and application in various marine engineering exploration scenarios, driven by the global evolution of the ocean ecosystem. However, UASNs face numerous challenges arising from the unique characteristics of the underwater environment and acoustic channels, such as limited energy resources, unreliable communication, and dynamic network topology in uncertain environments, all of which impact the network lifetime and transmission reliability. To address these challenges and enable efficient underwater data packet transmissions, this paper proposes a reliable cluster-based routing method called RCRP. The RCRP organizes sensor nodes into clusters, where cluster heads transmit the fused data to the sink node through multi-hop forwarding. To overcome routing voids caused by node failures during packet transmission, a prediction model based on the Markov chain is introduced to identify void nodes and inform neighboring nodes about the routing holes. Furthermore, a connection prediction mechanism is developed to tackle changes in the uncertain network topology, taking into account node mobility and the received SNR. For packet transmissions, a waiting mechanism inspired by the opportunistic routing (OR) paradigm is proposed, which determines the optimal forwarding route based on the calculated probability of successful connection between nodes. Simulation results demonstrate that RCRP outperforms existing routing protocols in terms of packet delivery ratio, surviving time, and energy consumption, highlighting its effectiveness in enhancing the performance of UASNs.

Traffic-driven epidemic spreading in community networks

The development of complex network theory provides new perspectives and ideas for studying the spreading of computer viruses and epidemics. Many real networks have community attributes, and research shows that the traffic flow on the network is closely related to the propagation of the virus. Therefore, we mainly analyze the traffic-driven epidemic spreading dynamics in community networks, reveals the rules and influencing factors of epidemic spreading, and provides reference for the immunization strategy. The results show that under a certain network size, the increase of the number of communities, the increase of the average degree within the community and the increase of the connection probability between the communities will lead to the community structure of the network becoming less obvious, thus promoting the spreading of the epidemic.

Effect of network structure on the accuracy of resilience dimension reduction

Dimension reduction is an effective method for system’s resilience analysis. In this paper, we investigate the effect of network structure on the accuracy of resilience dimension reduction. First, we introduce the resilience dimension reduction method and define the evaluation indicator of the resilience dimension reduction method. Then, by adjusting node connections, preferential connection mechanisms, and connection probabilities, we generate artificial networks, small-world networks and social networks with tunable assortativity coefficients, average clustering coefficients, and modularities, respectively. Experimental results for the gene regulatory dynamics show that the network structures with positive assortativity, large clustering coefficient, and significant community can enhance the accuracy of resilience dimension reduction. The result of this paper indicates that optimizing network structure can enhance the accuracy of resilience dimension reduction, which is of great significance for system resilience analysis and provides a new perspective and theoretical basis for selecting dimension reduction methods in system resilience analysis.