scholarly journals Locating Multiple Sources of Contagion in Complex Networks under the SIR Model

2019 ◽  
Vol 9 (20) ◽  
pp. 4472 ◽  
Author(s):  
Xiang Li ◽  
Yangyang Liu ◽  
Chengli Zhao ◽  
Xue Zhang ◽  
Dongyun Yi

Simultaneous outbreaks of contagion are a great threat against human life, resulting in great panic in society. It is urgent for us to find an efficient multiple sources localization method with the aim of studying its pathogenic mechanism and minimizing its harm. However, our ability to locate multiple sources is strictly limited by incomplete information about nodes and the inescapable randomness of the propagation process. In this paper, we present a valid approach, namely the Potential Concentration Label method, which helps locate multiple sources of contagion faster and more accurately in complex networks under the SIR(Susceptible-Infected-Recovered) model. Through label assignment in each node, our aim is to find the nodes with maximal value after several iterations. The experiments demonstrate that the accuracy of our multiple sources localization method is high enough. With the number of sources increasing, the accuracy of our method declines gradually. However, the accuracy remains at a slight fluctuation when average degree and network scale make a change. Moreover, our method still keeps a high multiple sources localization accuracy with noise of various intensities, which shows its strong anti-noise ability. I believe that our method provides a new perspective for accurate and fast multi-sources localization in complex networks.

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1570 ◽  
Author(s):  
Jingcheng Zhu ◽  
Lunwen Wang

Identifying influential nodes in complex networks is of great significance for clearly understanding network structure and maintaining network stability. Researchers have proposed many classical methods to evaluate the propagation impact of nodes, but there is still some room for improvement in the identification accuracy. Degree centrality is widely used because of its simplicity and convenience, but it has certain limitations. We divide the nodes into neighbor layers according to the distance between the surrounding nodes and the measured node. Considering that the node’s neighbor layer information directly affects the identification result, we propose a new node influence identification method by combining degree centrality information about itself and neighbor layer nodes. This method first superimposes the degree centrality of the node itself with neighbor layer nodes to quantify the effect of neighbor nodes, and then takes the nearest neighborhood several times to characterize node influence. In order to evaluate the efficiency of the proposed method, the susceptible–infected–recovered (SIR) model was used to simulate the propagation process of nodes on multiple real networks. These networks are unweighted and undirected networks, and the adjacency matrix of these networks is symmetric. Comparing the calculation results of each method with the results obtained by SIR model, the experimental results show that the proposed method is more effective in determining the node influence than seven other identification methods.


2022 ◽  
Vol 9 ◽  
Author(s):  
Li Tao ◽  
Mutong Liu ◽  
Zili Zhang ◽  
Liang Luo

Identifying multiple influential spreaders, which relates to finding k (k > 1) nodes with the most significant influence, is of great importance both in theoretical and practical applications. It is usually formulated as a node-ranking problem and addressed by sorting spreaders’ influence as measured based on the topological structure of interactions or propagation process of spreaders. However, ranking-based algorithms may not guarantee that the selected spreaders have the maximum influence, as these nodes may be adjacent, and thus play redundant roles in the propagation process. We propose three new algorithms to select multiple spreaders by taking into account the dispersion of nodes in the following ways: (1) improving a well-performed local index rank (LIR) algorithm by extending its key concept of the local index (an index measures how many of a node’s neighbors have a higher degree) from first-to second-order neighbors; (2) combining the LIR and independent set (IS) methods, which is a generalization of the coloring problem for complex networks and can ensure the selected nodes are non-adjacent if they have the same color; (3) combining the improved second-order LIR method and IS method so as to make the selected spreaders more disperse. We evaluate the proposed methods against six baseline methods on 10 synthetic networks and five real networks based on the classic susceptible-infected-recovered (SIR) model. The experimental results show that our proposed methods can identify nodes that are more influential. This suggests that taking into account the distances between nodes may aid in the identification of multiple influential spreaders.


Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2468
Author(s):  
Ri Lin ◽  
Feng Zhang ◽  
Dejun Li ◽  
Mingwei Lin ◽  
Gengli Zhou ◽  
...  

Docking technology for autonomous underwater vehicles (AUVs) involves energy supply, data exchange and navigation, and plays an important role to extend the endurance of the AUVs. The navigation method used in the transition between AUV homing and docking influences subsequent tasks. How to improve the accuracy of the navigation in this stage is important. However, when using ultra-short baseline (USBL), outliers and slow localization updating rates could possibly cause localization errors. Optical navigation methods using underwater lights and cameras are easily affected by the ambient light. All these may reduce the rate of successful docking. In this paper, research on an improved localization method based on multi-sensor information fusion is carried out. To improve the localization performance of AUVs under motion mutation and light variation conditions, an improved underwater simultaneous localization and mapping algorithm based on ORB features (IU-ORBSALM) is proposed. A nonlinear optimization method is proposed to optimize the scale of monocular visual odometry in IU-ORBSLAM and the AUV pose. Localization tests and five docking missions are executed in a swimming pool. The localization results indicate that the localization accuracy and update rate are both improved. The 100% successful docking rate achieved verifies the feasibility of the proposed localization method.


2006 ◽  
Vol 29 (3) ◽  
pp. 259-280 ◽  
Author(s):  
John L. Locke ◽  
Barry Bogin

It has long been claimed that Homo sapiens is the only species that has language, but only recently has it been recognized that humans also have an unusual pattern of growth and development. Social mammals have two stages of pre-adult development: infancy and juvenility. Humans have two additional prolonged and pronounced life history stages: childhood, an interval of four years extending between infancy and the juvenile period that follows, and adolescence, a stage of about eight years that stretches from juvenility to adulthood. We begin by reviewing the primary biological and linguistic changes occurring in each of the four pre-adult ontogenetic stages in human life history. Then we attempt to trace the evolution of childhood and juvenility in our hominin ancestors. We propose that several different forms of selection applied in infancy and childhood; and that, in adolescence, elaborated vocal behaviors played a role in courtship and intrasexual competition, enhancing fitness and ultimately integrating performative and pragmatic skills with linguistic knowledge in a broad faculty of language. A theoretical consequence of our proposal is that fossil evidence of the uniquely human stages may be used, with other findings, to date the emergence of language. If important aspects of language cannot appear until sexual maturity, as we propose, then a second consequence is that the development of language requires the whole of modern human ontogeny. Our life history model thus offers new ways of investigating, and thinking about, the evolution, development, and ultimately the nature of human language.


2011 ◽  
Vol 268-270 ◽  
pp. 934-939
Author(s):  
Xue Wen He ◽  
Gui Xiong Liu ◽  
Hai Bing Zhu ◽  
Xiao Ping Zhang

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.


2018 ◽  
Vol 12 (2) ◽  
pp. 260-278
Author(s):  
Christoph Demmerling

Abstract The following article argues that fictional texts can be distinguished from non-fictional texts in a prototypical way, even if the concept of the fictional cannot be defined in classical terms. In order to be able to characterize fictional texts, semantic, pragmatic, and reader-conditioned factors have to be taken into account. With reference to Frege, Searle, and Gabriel, the article recalls some proposals for how we might define fictional speech. Underscored in particular is the role of reception for the classification of a text as fictional. I make the case, from a philosophical perspective, for the view that fictional texts represent worlds that do not exist even though these worlds obviously can, and de facto do, contain many elements that are familiar to us from our world. I call these worlds reading worlds and explain the relationship between reading worlds and the life world of readers. This will help support the argument that the encounter with fictional literature can invoke real feelings and that such feelings are by no means irrational, as some defenders of the paradox of fiction would like us to believe. It is the exemplary character of fictional texts that enables us to make connections between the reading worlds and the life world. First and foremost, the article discusses the question of what it is that readers’ feelings are in fact related to. The widespread view that these feelings are primarily related to the characters or events represented in a text proves too simple and needs to be amended. Whoever is sad because of the fate of a fictive character imagines how he or she would fare if in a similar situation. He or she would feel sad as it relates to his or her own situation. And it is this feeling on behalf of one’s self that is the presupposition of sympathy for a fictive character. While reading, the feelings related to fictive characters and content are intertwined with the feelings related to one’s own personal concerns. The feelings one has on his or her own behalf belong to the feelings related to fictive characters; the former are the presupposition of the latter. If we look at the matter in this way, a new perspective opens up on the paradox of fiction. Generally speaking, the discussion surrounding the paradox of fiction is really about readers’ feelings as they relate to fictive persons or content. The question is then how it is possible to have them, since fictive persons and situations do not exist. If, however, the emotional relation to fictive characters and situations is conceived of as mediated by the feelings one has on one’s own behalf, the paradox loses its confusing effect since the imputation of existence no longer plays a central role. Instead, the conjecture that the events in a fictional story could have happened in one’s own life is important. The reader imagines that a story had or could have happened to him or herself. Readers are therefore often moved by a fictive event because they relate what happened in a story to themselves. They have understood the literary event as something that is humanly relevant in a general sense, and they see it as exemplary for human life as such. This is the decisive factor which gives rise to a connection between fiction and reality. The emotional relation to fictive characters happens on the basis of emotions that we would have for our own sake were we confronted with an occurrence like the one being narrated. What happens to the characters in a fictional text could also happen to readers. This is enough to stimulate corresponding feelings. We neither have to assume the existence of fictive characters nor do we have to suspend our knowledge about the fictive character of events or take part in a game of make-believe. But we do have to be able to regard the events in a fictional text as exemplary for human life. The representation of an occurrence in a novel exhibits a number of commonalities with the representation of something that could happen in the future. Consciousness of the future would seem to be a presupposition for developing feelings for something that is only represented. This requires the power of imagination. One has to be able to imagine what is happening to the characters involved in the occurrence being narrated in a fictional text, ›empathize‹ with them, and ultimately one has to be able to imagine that he or she could also be entangled in the same event and what it would be like. Without the use of these skills, it would remain a mystery how reading a fictional text can lead to feelings and how fictive occurrences can be related to reality. The fate of Anna Karenina can move us, we can sympathize with her, because reading the novel confronts us with possibilities that could affect our own lives. The imagination of such possibilities stimulates feelings that are related to us and to our lives. On that basis, we can participate in the fate of fictive characters without having to imagine that they really exist.


2021 ◽  
Vol 118 (31) ◽  
pp. e2103272118
Author(s):  
Nicholas J. Irons ◽  
Adrian E. Raftery

There are multiple sources of data giving information about the number of SARS-CoV-2 infections in the population, but all have major drawbacks, including biases and delayed reporting. For example, the number of confirmed cases largely underestimates the number of infections, and deaths lag infections substantially, while test positivity rates tend to greatly overestimate prevalence. Representative random prevalence surveys, the only putatively unbiased source, are sparse in time and space, and the results can come with big delays. Reliable estimates of population prevalence are necessary for understanding the spread of the virus and the effectiveness of mitigation strategies. We develop a simple Bayesian framework to estimate viral prevalence by combining several of the main available data sources. It is based on a discrete-time Susceptible–Infected–Removed (SIR) model with time-varying reproductive parameter. Our model includes likelihood components that incorporate data on deaths due to the virus, confirmed cases, and the number of tests administered on each day. We anchor our inference with data from random-sample testing surveys in Indiana and Ohio. We use the results from these two states to calibrate the model on positive test counts and proceed to estimate the infection fatality rate and the number of new infections on each day in each state in the United States. We estimate the extent to which reported COVID cases have underestimated true infection counts, which was large, especially in the first months of the pandemic. We explore the implications of our results for progress toward herd immunity.


2022 ◽  
Vol 19 (1) ◽  
pp. 707-737
Author(s):  
Xueyi Ye ◽  
◽  
Yuzhong Shen ◽  
Maosheng Zeng ◽  
Yirui Liu ◽  
...  

<abstract> <p>Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples.</p> </abstract>


2018 ◽  
Vol 32 (06) ◽  
pp. 1850118 ◽  
Author(s):  
Mengtian Li ◽  
Ruisheng Zhang ◽  
Rongjing Hu ◽  
Fan Yang ◽  
Yabing Yao ◽  
...  

Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible–infectious–recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.


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