Hierarchical Network Model Research Articles

Dynamic Capacity Planning and Location of Hierarchical Service Networks Under Service Level Constraints

This paper addresses the problem of joint facility location and capacity planning of hierarchical service networks in order to determine when and where to open/close service units, their capacity and the demand-to-facility allocation. We propose a new hierarchical service network model in which both the facilities and customers have nested hierarchies, i.e., a higher level facility provides all services provided by a lower level facility and a customer requiring a certain level of service will additionally require lower level services. Poisson customer arrivals and random service times are assumed. Each service unit is modeled as an Erlang-loss system and its service level, defined as its customer acceptance probability, is given by the so-called Erlang-loss function. A nonlinear programming model is proposed to minimize the total cost, while keeping the service level of all service units above some given level. Different linearization models of the Erlang-loss function and their properties are proposed. Linearization transforms the nonlinear model into compact mixed integer programs solvable to optimality with standard solvers. Application to a real-life perinatal network is then presented.

A hierarchical-network-model based analysis of the market characteristics of China's photovoltaic enterprises

China's photovoltaic (PV) enterprises have experienced difficulties under the influence of the growth slowdown of international market and trade protectionism since 2012. In this paper, from a new perspective of building a hierarchical network model, by using the minimum spanning tree (MST) technique, the market characteristics of China's PV enterprises was researched. The economic distance in the MST was calculated by using PV enterprises' daily closing price of stocks during the years 2007–2013. With the analysis of the topology of hierarchical network, the community structures of the network are derived and some influential enterprises are identified. It is necessary for PV enterprises to deeply connect and cooperate with each other to cope with market risk. What's more, the key factors that affect the hierarchical network were also studied; the result indicates that assets expansion and the core technology competitiveness are important factors for the development of PV enterprises. In the end, some suggestions are put forward to promote sustainable development of China's PV industry.

Performance-optimized hierarchical models predict neural responses in higher visual cortex.

The ventral visual stream underlies key human visual object recognition abilities. However, neural encoding in the higher areas of the ventral stream remains poorly understood. Here, we describe a modeling approach that yields a quantitatively accurate model of inferior temporal (IT) cortex, the highest ventral cortical area. Using high-throughput computational techniques, we discovered that, within a class of biologically plausible hierarchical neural network models, there is a strong correlation between a model's categorization performance and its ability to predict individual IT neural unit response data. To pursue this idea, we then identified a high-performing neural network that matches human performance on a range of recognition tasks. Critically, even though we did not constrain this model to match neural data, its top output layer turns out to be highly predictive of IT spiking responses to complex naturalistic images at both the single site and population levels. Moreover, the model's intermediate layers are highly predictive of neural responses in the V4 cortex, a midlevel visual area that provides the dominant cortical input to IT. These results show that performance optimization--applied in a biologically appropriate model class--can be used to build quantitative predictive models of neural processing.

A hierarchical network model for epidemic spreading. Analysis of A/H1N1 virus spreading in Romania

The research in this paper presents a new approach for the modeling of epidemic spread by using a set of connected social networks. The purpose of this work is to simulate the spreading of the well know A/H1N1 pandemic virus. The case study analyzed in this paper refers to the spreading of A/H1N1 in Romania. The epidemic is followed from its beginning throughout its evolution in Romania, i.e. between May 2009 and February 2010. The evolution is performed in a hierarchical way, taking into account the state divisions, the influences among them, national level as well as influences from abroad (from other infected countries). Numerical experiments performed analyze the monthly evolution of the infection in each county and at the country level and compare the results with the real ones (collected during and at the end of the epidemic spread). The simulations results are closer to the reality than the ones provided by the Health Ministry in Romania.

Trapping time of weighted-dependent walks depending on the weight factor

A weighted hierarchical network model is introduced in this paper. We study the trapping problem for weighted-dependent walks taking place on a hierarchical weighted network at a given trap. We concentrate on the average trapping time (ATT) for three cases, i.e., the immobile trap located at the root node, the external nodes and a neighbor of the root with a single connectivity, respectively. The closed-form formulae for the ATT for the three cases are obtained. In different range of the weight factor r, the leading term of ATT grows differently, i.e., superlinearly, linearly and sublinearly with the network size. For all the three cases of trapping problems, the leading scaling of ATT can reach the minimum scaling.

Sequential and Opposing Activities of Wnt and BMP Coordinate Zebrafish Bone Regeneration

SUMMARYZebrafish fully regenerate lost bone, including after fin amputation, through a process mediated by dedifferentiated, lineage-restricted osteoblasts. Mechanisms controlling the osteoblast regenerative program from its initiation through reossification are poorly understood. We show that fin amputation induces a Wnt/β-catenin-dependent epithelial to mesenchymal transformation (EMT) of osteoblasts in order to generate proliferative Runx2+ preosteoblasts. Localized Wnt/β-catenin signaling maintains this progenitor population toward the distal tip of the regenerative blastema. As they become proximally displaced, preosteoblasts upregulate sp7 and subsequently mature into re-epithelialized Runx2−/sp7+ osteoblasts that extend preexisting bone. Auto-crine bone morphogenetic protein (BMP) signaling promotes osteoblast differentiation by activating sp7 expression and counters Wnt by inducing Dickkopf-related Wnt antagonists. As such, opposing activities of Wnt and BMP coordinate the simultaneous demand for growth and differentiation during bone regeneration. This hierarchical signaling network model provides a conceptual framework for understanding innate bone repair and regeneration mechanisms and rationally designing regenerative therapeutics.

A cellular mechanism for system memory consolidation

Event Abstract Back to Event A cellular mechanism for system memory consolidation Michiel W. Remme1*, Urs M. Bergmann1, Susanne Schreiber1, Henning Sprekeler1 and Richard Kempter1 1 Humboldt University Berlin, Institute for Theoretical Biology, Germany Declarative memories initially depend on the hippocampus. Over a period of weeks to years, however, these memories become hippocampus-independent through a process called system memory consolidation. The underlying cellular mechanisms are unclear. Here, we suggest a consolidation mechanism, which is based on STDP and a ubiquitous anatomical network motif. As a first step in the memory consolidation process, we consider pyramidal neurons in the hippocampal CA1 area. These cells receive Schaffer collateral (SC) input from the CA3 area at the proximal dendrites, and perforant path (PP) input from entorhinal cortex at the distal dendrites. Both pathways carry sensory information that has been processed by cortical networks and that enters the hippocampus through the entorhinal cortex. Hence, information from entorhinal cortex reaches CA1 cells through an indirect pathway (via CA3 and SC) and a direct pathway (PP). Memories are assumed to be initially stored in the recurrent CA3 network and the SC synapses during the awake, exploratory state. During a subsequent consolidation phase (during slow-wave sleep) SC-dependent memories are partly transferred to the PP synapses. Through mathematical analysis and numerical simulations we show that this consolidation process occurs as a natural result from the combination of (1) STDP at PP synapses and (2) the temporal correlations between SC and PP activities, since the (indirect) SC input is delayed compared to the (direct) PP input by about 5-10 ms. With a detailed compartmental model we then show that the spatial tuning of a CA1 cell is copied from the proximal SC-synaptic inputs to the distal PP-inputs. Next, we repeated the network motif across many levels in a hierarchical network model: each direct connection at one level is part of the indirect pathway of the next level. Analysis and simulations of this hierarchical system demonstrate that memories gradually move from hippocampus into neocortex. Moreover, the memories show power-law forgetting, as seen with psychophysical forgetting functions. Hence, our work proposes a novel mechanism to underlie system memory consolidation, allowing us to bridge spatial scales from single cells to cortical areas, and time scales from milliseconds to years. Acknowledgements Supported by BMBF grants no. 01GQ0901, 01GQ0972, 01GQ1201, 01GQ1001A, DFG grant no. SFB 618 and the Einstein Stiftung Berlin. Keywords: System memory consolidation, Pyramidal neurons, dendritic integration, stdp learning, Hippocampus, Neocortex Conference: 4th NAMASEN Training Workshop - Dendrites 2014, Heraklion, Greece, 1 Jul - 4 Jul, 2014. Presentation Type: Poster presentation Topic: the role of dendrites in complex processes, including learning/memory and neural computations Citation: Remme MW, Bergmann UM, Schreiber S, Sprekeler H and Kempter R (2014). A cellular mechanism for system memory consolidation. Front. Syst. Neurosci. Conference Abstract: 4th NAMASEN Training Workshop - Dendrites 2014. doi: 10.3389/conf.fnsys.2014.05.00010 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 11 Apr 2014; Published Online: 12 Jun 2014. * Correspondence: Dr. Michiel W Remme, Humboldt University Berlin, Institute for Theoretical Biology, Berlin, 10115, Germany, michiel.remme@hu-berlin.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Michiel W Remme Urs M Bergmann Susanne Schreiber Henning Sprekeler Richard Kempter Google Michiel W Remme Urs M Bergmann Susanne Schreiber Henning Sprekeler Richard Kempter Google Scholar Michiel W Remme Urs M Bergmann Susanne Schreiber Henning Sprekeler Richard Kempter PubMed Michiel W Remme Urs M Bergmann Susanne Schreiber Henning Sprekeler Richard Kempter Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Pushing typists back on the learning curve: contributions of multiple linguistic units in the acquisition of typing skill.

The present study investigated the way people acquire and control skilled performance in the context of typewriting. Typing skill was degraded by changing the location of a key (target key) while retaining the locations of other keys to disable an association between the letter and the key. We conducted 4 experiments: Experiment 1 demonstrated that disabling a letter-key association affected not only the execution of the target keystroke but also the planning of other keystrokes for words involving the target key. In Experiments 2-4, typists practiced with a new target location and then transferred to a condition in which they typed the practiced words with the original key location (Experiment 2) or typed new words with the practiced key location (Experiments 3 and 4). Experiment 2 showed that the newly acquired letter-key association interfered with the execution of the original keystroke but not planning. Experiments 3 and 4 demonstrated that acquisition of the new letter-key association depended on multiple levels of linguistic units. Experiment 4 demonstrated that acquisition of the new association depended on sequences both before and after the target keystroke. We discuss implications of the results for 2 prominent approaches to modeling sequential behavior: hierarchical control and recurrent network models.

What makes spider silk fibers so strong? From molecular-crystallite network to hierarchical network structures

A Hierarchical Network (HN) model of soft matter was put forward to explain the mechanical properties of animal silk fibers. At the nano-micro level, the silk fibers consist of a bundle of twisted nano-fibrils with strong friction among them. At the nano-fibril level, β-crystallites together with silk molecular chains constitute the molecular networks. According to the model, the influences of different structural parameters, i.e. the ordering, and the density of β-nanocrystallites, on the breaking stress of silk fibers were analyzed quantitatively. It turns out that a better alignment of β-crystallites, a larger number of β-crystallites within the cross-section of a nano-fibril and a smaller effective loading area of a peptide chain will correlatively lead to stronger silk fibers. This is in excellent agreement with our observations for both spider dragline and silkworm silk fibers, and explains the fact that the spider dragline silk fibers having a lower crystallinity are much stronger than silkworm silk fibers. Furthermore, it was found that at the nanofibril scale, the interlock among the adjacent nanofibrils in the nanofibril bundle serves as a crack-stopper, which restricts the propagation of cracks. Such a structure reinforces the silk fibers significantly. The knowledge obtained will shed light on how to obtain ultra-strong fibrous materials from the structural point of view.

Hierarchical scale-free network is fragile against random failure

We investigate site percolation in a hierarchical scale-free network known as the Dorogovtsev-Goltsev-Mendes network. We use the generating function method to show that the percolation threshold is 1, i.e., the system is not in the percolating phase when the occupation probability is less than 1. The present result is contrasted to bond percolation in the same network of which the percolation threshold is zero. We also show that the percolation threshold of intentional attacks is 1. Our results suggest that this hierarchical scale-free network is very fragile against both random failure and intentional attacks. Such a structural defect is common in many hierarchical network models.

Data Fusion Algorithm of Privacy Protection Based on Qos and Multilayers Hierarchically

Because of the special nature of wireless sensor networks, data fusion process is vulnerable to attacks by the destroyer, which is useful for reducing the network communication overhead and improving the data transmission efficiency. Therefore, it is very essential to provide transmission with high security and low energy consumption for sensitive data fusion in wireless sensor network. In this paper, a data fusion algorithm of privacy protection based on Query of Server (Qos) and hierarchical multilayers was put forward, which divided the required privacy protection levels according to different safety requirements, and set up hierarchical network models. In data fusion process, delay constraint was added to guarantee service quality of Qos, and this would reduce energy consumption overhead. Meanwhile, it would guarantee the accuracy of the data and reduce the probability of the whole network information exposed.

Backhauling in Heterogeneous Cellular Networks: Modeling and Tradeoffs

Consumer demand for data has increased tremendously over the last years, and small cell networks are increasingly being considered as one of the key technologies to cope with this demand. Small cell network deployments within the conventional macro cellular networks are creating a significant amount of heterogeneity compared to traditional cellular networks. Nevertheless, the backhaul link is often the bottleneck in terms of system performance and cost. In this paper, we consider the backhaul issue in heterogeneous cellular networks and propose a hierarchical network model using superimposed independent homogeneous Poisson point processes. We derive the total expected delay by taking into account retransmissions over the wireless link, as well as the backhaul delay incurred from both wired and wireless backhaul. For the total expected deployment cost, we take into account infrastructure cost and the construction cost of wired backhaul deployment. Specifically, we are able to characterize the behavior of delay and deployment cost using our simple and tractable model. Furthermore, we propose a delay-based access control policy, which can provide better latency performance especially in dense deployments. Our theoretical framework provides a fundamental understanding of the tradeoff between wired and wireless backhaul and the effect on deployment cost and system performance in heterogeneous cellular networks.

Hierarchical Neural Network Modeling Approach to Predict Sludge Volume Index of Wastewater Treatment Process

From a practical-theoretic viewpoint, there is a need to develop rigorous design and analysis tools for the control, fault diagnosis, and security of wastewater quality. However, sludge bulking remains a widespread problem in the operation of activated sludge processes, which leads to severe economic and environmental consequences. Sludge volume index (SVI) monitoring is a key challenge that will become even more crucial in the years ahead to quantify sludge bulking. This brief presents a system that consists of online sensors and an SVI predicting plant. The SVI predicting plant uses a hierarchical radial basis function (HRBF) neural network to predict SVI in a wastewater treatment process (WWTP). Then, an approach named extended extreme learning machine (EELM) is proposed for training the weights of HRBF. Unlike conventional single-hidden-layer feedforward networks, this EELM-HRBF is based on the hierarchical structure which is capable of hierarchical learning of sequential information online, and one may only need to adjust the output weights linking the hidden and the output layers. In such EELM-HRBF implementations, the EELM provides better generalization performance during the learning process. Moreover, the convergence of the proposed algorithm is analyzed. To illustrate the methodology, the proposed predicting plant with the EELM-HRBF has been tested and compared with other methods by applying it to the problem of predicting SVI in a simplified and real WWTP. Experimental results show that the EELM-HRBF can be used to predict the wastewater quality online. The results demonstrate its effectiveness.

The Properties of Tree-Like Hierarchical Networks

Tree-like hierarchical structure is one of the key features in many real complex systems. To investigate its properties, a deterministic tree-like hierarchical network model is proposed. Next, the static properties of the tree-like hierarchical networks are discussed. The simulation and theoretical results prove that the artificial tr ee-like networks conformably possess small-world and scale- free properties well. Furthermore, the dynamic cascading failures in the tree-like hierarchical networks are investigated. A weighted tree-like cascading failure model is proposed, and the impacts of topological structure on the network robustness are investigated by a set of illustrative simulations.

An access authentication protocol for trusted handoff in wireless mesh networks

WMNs (Wireless Mesh Networks) are a new wireless broadband network structure based completely on IP technologies and have rapidly become a broadband access measure to offer high capacity, high speed and wide coverage. Trusted handoff in WMNs requires that mobile nodes complete access authentication not only with a short delay, but also with the security protection for the mobile nodes as well as the handoff network. In this paper, we propose a trusted handoff protocol based on several technologies, such as hierarchical network model, ECC (Elliptic Curve Cryptography), trust evaluation and gray relevance analysis. In the protocol, the mobile platform's configuration must be measured before access to the handoff network can proceed and only those platforms whose configuration meets the security requirements can be allowed to access the network. We also verify the security properties through formal analysis based on an enhanced Strand model and evaluate the performance of the proposed protocol through simulation to show that our protocol is more advantageous than the EMSA (Efficient Mesh Security Association) authentication scheme in terms of success rate and average delay.

The Property of Hierarchical Structure in Manufacturing

Hierarchical structure is one of the most ubiquitous structures in various networked complex systems. In order to investigate the properties of the hierarchical structure, a deterministic hierarchical network model is first proposed. The statistical properties of the constructed networks are also discussed. The simulation results prove that the artificial networks simultaneously possess the small-world and scale-free properties well. This may be useful in furthering study of the topology properties of the hierarchical networks in real life.

Stimulus detection rate and latency, firing rates and 1–40 Hz oscillatory power are modulated by infra-slow fluctuations in a bistable attractor network model

Recordings of membrane and field potentials, firing rates, and oscillation amplitude dynamics show that neuronal activity levels in cortical and subcortical structures exhibit infra-slow fluctuations (ISFs) on time scales from seconds to hundreds of seconds. Similar ISFs are salient also in blood-oxygenation-level dependent (BOLD) signals as well as in psychophysical time series. Functional consequences of ISFs are not fully understood. Here, they were investigated along with dynamical implications of ISFs in large-scale simulations of cortical network activity. For this purpose, a biophysically detailed hierarchical attractor network model displaying bistability and operating in an oscillatory regime was used. ISFs were imposed as slow fluctuations in either the amplitude or frequency of fast synaptic noise. We found that both mechanisms produced an ISF component in the synthetic local field potentials (LFPs) and modulated the power of 1–40Hz oscillations. Crucially, in a simulated threshold-stimulus detection task (TSDT), these ISFs were strongly correlated with stimulus detection probabilities and latencies. The results thus show that several phenomena observed in many empirical studies emerge concurrently in the model dynamics, which yields mechanistic insight into how infra-slow excitability fluctuations in large-scale neuronal networks may modulate fast oscillations and perceptual processing. The model also makes several novel predictions that can be experimentally tested in future studies.

Self-sustained activity in neural networks: influence of network topology and cell types

The cerebral cortex exhibits spontaneous and structured collective activity patterns even in the absence of external stimuli [1]. A possible question that can be made given this evidence is: how does the topology of the cortical network together with the individual properties of the neuronal types that populate it affect this self-sustained neural activity? Anatomical evidence suggests that cortical architecture has modular and hierarchical design [2]. Based on their response patterns to intracellular current injection, cortical neurons may be classified into 5 main electrophysiological classes [3]: regular spiking (RS), intrinsically bursting (IB), chattering (CH), fast spiking (FS) and with low threshold spikes (LTS). Cells from the first 3 types are excitatory and cells from latter 2 are inhibitory [3]. In this work we used a hierarchical and modular network model composed of excitatory and inhibitory neurons to study the joint effect of (i) modularity level and (ii) combination of excitatory and inhibitory cell types on self-sustained network activity. Our hierarchical and modular network was constructed using a top-down method [4] starting with a random network of 1024 cells with connection probability of 0.01. The ratio of excitatory to inhibitory neurons was 4:1. Neurons were modeled using Izhikevich's neuron model [5] with parameters adjusted to reproduce the firing behaviors of the 5 cell types. Our model has 2 synaptic conductances (ge, gi), representing excitatory and inhibitory synapses. After a pre-synaptic event, these synaptic conductances are increased by constants Δge, Δgi. Otherwise, they decay according to first-order linear kinetics with characteristic times τe = 5 ms and τi = 6 ms. Using the modularization method of [4] (with rewiring probabilities Ri = 1 and Re = 0.9), we generated networks with 4 modularity levels (0-3). For each level we generated 6 networks given by the possible combinations of the 3 excitatory neuron types with the 2 inhibitory types. Our experimental protocol consisted in stimulating a network by applying white noise to all neurons for 200 ms. After the noise was switched off the simulation was allowed to run for an extra 4800 ms. We performed this experiment for 50 realizations of each network (to calculate averages) and for different combinations of the parameters (Δge, Δgi). From the resulting data we constructed, for each network configuration, a Δge-Δgi diagram plotting the time of last network spike. We defined a threshold for this time (~4500 ms) beyond which we assumed the network as having self-sustained activity. For each diagram, we measured the fraction of its total area for which self-sustained activity existed according to our criterion. This fraction, called FTA, was our measure of self-sustained network activity. Our results show that (1) FTA increases with the modularity level; (2) networks with RS excitatory cells have the highest FTA variability with modularity level; and (3) networks with CH excitatory neurons have the smallest FTA variability with modularity. For these latter networks, FTA reached maximum value for level-1 modularity. Our results show a strong dependency of self-sustained activity on both the modularity level and types of excitatory and inhibitory cells. They suggest that modular architecture favors self-sustained activity and that networks with most of excitatory cells of the RS class exhibit the widest range of self-sustained regimes.

Hierarchical Network Models for Education Research

Intervention studies in school systems are sometimes aimed not at changing curriculum or classroom technique, but rather at changing the way that teachers, teaching coaches, and administrators in schools work with one another—in short, changing the professional social networks of educators. Current methods of social network analysis are ill-suited to modeling the multiple partially exchangeable networks that arise in randomized field trials and observational studies in which multiple classrooms, schools, or districts are involved, and to detecting the effect of an intervention on the social network itself. To address these needs, we introduce a new modeling framework, the Hierarchical Network Models (HNM) framework. The HNM framework can be used to extend single-network statistical network models to multiple networks, using a hierarchical modeling approach. We show how to generalize the latent space model for a single network to the HNM/multiple-network setting, and illustrate our approach with real and simulated social network data among education professionals.

A Multi‐criteria Usability Assessment of Similar Types of Touch Screen Mobile Phones

ABSTRACTDesigning products possessing only technical perfectness does not seem to be enough in today's market. New expectations adding value to the product such as product usability and feelings have become more important. Consequently, product usability has become a major focus of manufacturing companies. In this study, touch screen cell phones are examined, and various specifications that affect the usability are researched. Usability tests were carried out with the participation of 15 subjects for four different brands of touch screen phones having the highest market share. The usability factors regarding questionnaires were evaluated by taking into consideration the relationships between them in a three‐stage (strategic, tactical and operational) hierarchical network model. Analytical network process method was used in order to analyse the network for end consumer. The results were evaluated within the general framework of the hierarchy, and the most important factors in respect of usability were calculated. Moreover, the usability levels of four brands were ranked evaluating the usability scores. Copyright © 2013 John Wiley & Sons, Ltd.