Insect-Inspired Visual Navigation On-Board an Autonomous Robot: Real-World Routes Encoded in a Single Layer Network

Many real-world complex systems have multiple types of relations between their components, and they are popularly modeled as multiplex networks with each type of relation as one layer. Since the fusion analysis of multiplex networks can provide a comprehensive insight, the structural information fusion of multiplex networks has become a crucial issue. However, most of these existing data fusion methods are inappropriate for researchers to apply to complex network analysis directly. The feature-based fusion methods ignore the sharing and complementarity of interlayer structural information. To tackle this problem, we propose a multiplex network structural fusion (MNSF) model, which can construct a network with comprehensive information. It is composed of two modules: the network feature extraction (NFE) module and the network structural fusion (NSF) module. (1) In NFE, MNSF first extracts a low-dimensional vector representation of a node from each layer. Then, we construct a node similarity network based on embedding matrices and K-D tree algorithm. (2) In NSF, we present a nonlinear enhanced iterative fusion (EIF) strategy. EIF can strengthen high-weight edges presented in one (i.e., complementary information) or more (i.e., shared information) networks and weaken low-weight edges (i.e., redundant information). The retention of low-weight edges shared by all layers depends on the tightness of connections of their K-order proximity. The usage of higher-order proximity in EIF alleviates the dependence on the quality of node embedding. Besides, the fused network can be easily exploited by traditional single-layer network analysis methods. Experiments on real-world networks demonstrate that MNSF outperforms the state-of-the-art methods in tasks link prediction and shared community detection.

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Betweenness centrality for temporal multiplexes

Scientific Reports ◽

10.1038/s41598-021-84418-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Silvia Zaoli ◽

Piero Mazzarisi ◽

Fabrizio Lillo

Keyword(s):

Information Flow ◽

Real World ◽

Betweenness Centrality ◽

Temporal Structure ◽

Shortest Paths ◽

Single Layer ◽

Distance Metric ◽

Definition Of

AbstractBetweenness centrality quantifies the importance of a vertex for the information flow in a network. The standard betweenness centrality applies to static single-layer networks, but many real world networks are both dynamic and made of several layers. We propose a definition of betweenness centrality for temporal multiplexes. This definition accounts for the topological and temporal structure and for the duration of paths in the determination of the shortest paths. We propose an algorithm to compute the new metric using a mapping to a static graph. We apply the metric to a dataset of $$\sim 20$$ ∼ 20 k European flights and compare the results with those obtained with static or single-layer metrics. The differences in the airports rankings highlight the importance of considering the temporal multiplex structure and an appropriate distance metric.

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Reliability Models of GSM-R Redundant Network on High-Speed Railway

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.198-199.1783 ◽

2012 ◽

Vol 198-199 ◽

pp. 1783-1788

Author(s):

Jun Ting Lin ◽

Jian Wu Dang

Keyword(s):

Network Architecture ◽

High Speed ◽

Failure Time ◽

Network Reliability ◽

Single Layer ◽

Double Layers ◽

High Speed Railway ◽

Reliability Models ◽

Single Layer Network ◽

Wireless Architectures

As a dedicated digital mobile communication system designed for railway application, GSM-R must provide reliable bidirectional channel for transmitting security data between trackside equipments and on-train computer on high-speed railways. To ensure the safety of running trains, redundant network architecture is commonly used to guarantee the reliability of GSM-R. Because of the rigid demands of railway security, it is important to build reliability mathematical models, predict the network reliability and select a suitable one. Two common GSM-R wireless architectures, co-sited double layers network and intercross single layer network, are modeled and contrasted in this paper. By calculating the reliabilities of each reliable model, it is clear that more redundant the architecture is, more reliable the system will be, the whole system will bear a less failure time per year as the benefit. Meanwhile, as the redundancy of GSM-R system raises, its equipment and maintenance will cost much, but the reliability raise gently. From the standpoint of transmission system interruption and network equipment failure, the reliability of co-sited double layer network architecture is higher than the intercross single layer one, while the viability and cost of the intercross redundant network is better than co-sited one in natural disasters such as flood and lightning. Taking fully into account reliability, viability and cost, we suggest that intercross redundant network should be chosen on high-speed railway.

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CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning

10.36227/techrxiv.12063582.v1 ◽

2020 ◽

Author(s):

Marvin Chancán

Keyword(s):

Real World ◽

Autonomous Driving ◽

Visual Navigation ◽

Place Recognition ◽

Visual Appearance ◽

Learning Techniques ◽

Benchmark Datasets ◽

Image Representations ◽

Visual Place Recognition ◽

Self Motion

<div>Visual navigation tasks in real-world environments often require both self-motion and place recognition feedback. While deep reinforcement learning has shown success in solving these perception and decision-making problems in an end-to-end manner, these algorithms require large amounts of experience to learn navigation policies from high-dimensional data, which is generally impractical for real robots due to sample complexity. In this paper, we address these problems with two main contributions. We first leverage place recognition and deep learning techniques combined with goal destination feedback to generate compact, bimodal image representations that can then be used to effectively learn control policies from a small amount of experience. Second, we present an interactive framework, CityLearn, that enables for the first time training and deployment of navigation algorithms across city-sized, realistic environments with extreme visual appearance changes. CityLearn features more than 10 benchmark datasets, often used in visual place recognition and autonomous driving research, including over 100 recorded traversals across 60 cities around the world. We evaluate our approach on two CityLearn environments, training our navigation policy on a single traversal. Results show our method can be over 2 orders of magnitude faster than when using raw images, and can also generalize across extreme visual changes including day to night and summer to winter transitions.</div>

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Real World Uniformity Measurement in Nonwoven Coverstock

International Nonwovens Journal ◽

10.1177/1558925001os-1000110 ◽

2001 ◽

Vol os-10 (1) ◽

pp. 1558925001os-10

Author(s):

William H. Pound

Keyword(s):

Real World ◽

Single Layer ◽

Process Change ◽

Gray Scale ◽

Camera Image ◽

Bleed Through

Studies were carried out in a nonwoven roll goods plant to help eliminate subjective formation ratings. Tests were run on single layer samples with instruments measuring color, transmittance, haze and camera image gray scale values. High correlations are shown with air porosity, basis weight and formation ratings. Relationships to diaper glue bleed through, superabsorbent loss, and web tracking are reported. Some effects of process change are noted. Correlations critically depend upon measuring identical sample areas.

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Special features of the endometrium’s structure of the childhood

Vladikavkaz Medico-Biological Bulletin ◽

10.12737/18550 ◽

2016 ◽

Vol 21 (32) ◽

pp. 63-68

Author(s):

Тотоева ◽

Olga Totoeva ◽

Туаева ◽

Zarema Tuaeva

Keyword(s):

Double Layer ◽

Microscopic Examination ◽

Single Layer ◽

Blood Capillaries ◽

Mucous Coat ◽

Vascular Bed ◽

Single Layer Network

The paper presents the results of the macro-microscopic examination of the vascular bed and the structure of the endometrium at 20 specimens of uterus, taken from the corpses of children’s and teenage periods of ontogenesis. It is shown, that the growth of the glands of the mucous coat of the uterus begins in the second infant periodand intensifies in teens. This determines the transformation of single-layer network of lymphatic capillaries to the double-layer one. There were marked transformations of glands and fibrous structures during the cyclical changes of endometrium. Glands increase and lymphatic and blood capillaries and lymphoid elements occur as the primary divisions of the glands and their ducts.

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STRUCTURAL PROPERTIES OF MULTILAYER SOFTWARE NETWORKS: A CASE STUDY IN TOMCAT

Advances in Complex Systems ◽

10.1142/s0219525918500042 ◽

2018 ◽

Vol 21 (02) ◽

pp. 1850004 ◽

Cited By ~ 3

Author(s):

WEIFENG PAN ◽

BO HU ◽

JILEI DONG ◽

KUN LIU ◽

BO JIANG

Keyword(s):

Structural Properties ◽

Path Length ◽

Single Layer ◽

Clustering Coefficient ◽

Software Systems ◽

Software Network ◽

Class Level ◽

Single Layer Network ◽

Coupling Type

Statistical properties of software networks have been extensively studied. However, in the previous works, software networks are usually considered as a single-layer network, which cannot capture the authentic characteristics of software since software in its nature should be multilayer. In this paper, we explore the structural properties of the multilayer software network at the class level by progressively merging layers together, where each coupling type such as inheritance, implements, and method call defines a specific layer. A case study in software Tomcat is conducted using a set of 10 measures widely used in complex network literatures. The results show that some structural properties that are widely observed in software network researches can only emerge when several layers are merged together, such as high clustering coefficient, small value of average shortest path length, and high global efficiency. Our study highlights the importance of taking into consideration the multilayer nature of software systems. The results we found can provide valuable insights to our understanding and modeling of the dynamical processes taking place in the design and development of software systems.

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Link prediction in real-world multiplex networks via layer reconstruction method

Royal Society Open Science ◽

10.1098/rsos.191928 ◽

2020 ◽

Vol 7 (7) ◽

pp. 191928

Author(s):

Amir Mahdi Abdolhosseini-Qomi ◽

Seyed Hossein Jafari ◽

Amirheckmat Taghizadeh ◽

Naser Yazdani ◽

Masoud Asadpour ◽

...

Keyword(s):

Real World ◽

Link Prediction ◽

Large Body ◽

Single Layer ◽

Structural Features ◽

Prediction Performance ◽

Reconstruction Method ◽

Multiplex Networks ◽

Technological Complex ◽

Different Types

Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not confine to one but to a variety of types. Multiplex networks encode this complexity with a set of nodes which are connected in different layers via different types of links. A large body of research on link prediction problem is devoted to finding missing links in single-layer (simplex) networks. In recent years, the problem of link prediction in multiplex networks has gained the attention of researchers from different scientific communities. Although most of these studies suggest that prediction performance can be enhanced by using the information contained in different layers of the network, the exact source of this enhancement remains obscure. Here, it is shown that similarity w.r.t. structural features (eigenvectors) is a major source of enhancements for link prediction task in multiplex networks using the proposed layer reconstruction method and experiments on real-world multiplex networks from different disciplines. Moreover, we characterize how low values of similarity w.r.t. structural features result in cases where improving prediction performance is substantially hard.

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Price Linkage Rumors in the Stock Market and Investor Risk Contagion on Bilayer-Coupled Networks

Complexity ◽

10.1155/2019/4727868 ◽

2019 ◽

Vol 2019 ◽

pp. 1-21 ◽

Cited By ~ 5

Author(s):

Yue Dong ◽

Jiepeng Wang ◽

Tingqiang Chen

Keyword(s):

Stock Market ◽

Stock Price ◽

Single Layer ◽

Local Effect ◽

Information Value ◽

Enhancement Effect ◽

Global Effect ◽

Coupled Networks ◽

Contagion Model ◽

Single Layer Network

Investor heterogeneities include investor risk preference, investor risk cognitive level, information value, and investor influence. From the perspective of the stock price linkage, this article constructs an SCIR contagion model of investor risk on a single-layer network. It digs out the investor risk caused by rumors in the stock market under the stock price linkage and its contagion mechanism. The function and influence of different mechanism probabilities and investor heterogeneities on the effects of risk contagion in the stock market are explored through computer simulation. Based on the SCIR contagion model of investor risk on single-layer network, we construct an SCI1I2R contagion model of investor risk on bilayer-coupled networks. Initially, the evolution mechanisms of investor risk contagion in the stock market are compared in single-layer and bilayer-coupled networks. Thereafter, the evolution characteristics and rules of investor risk contagion under different connection modes and heterogeneous mechanism probabilities are compared on bilayer-coupled networks. The results corroborate the following. (1) In the SCIR contagion model of investor risk on a single-layer network, immune failure probability and immune probability have the “global effect”. (2) Investor heterogeneities both have “global effect” and “local effect” on investor risk contagion. (3) Compared with the investor risk contagion on a single-layer network, bilayer-coupled networks can expand the investor risk contagion and have a “global enhancement” effect. (4) Among the three interlayer connection modes of the SCI1I2R model of investor risk contagion on bilayer-coupled networks, the assortative link has the effect of “local enhancement”, while the disassortative link has the effect of “local inhibition”. (5) In the SCI1I2R model of investor risk contagion on bilayer-coupled networks, heterogeneous mechanism probabilities have “global effect” and “local effect”. The research conclusion provides a theoretical basis for regulators to prevent financial risks from spreading among different investors, which is of high theoretical value and practical significance.

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