Maximal switchability of centralized networks
Vakulenko, Sergei; Morozov, Ivan; Radulescu, Ovidiu
2016-08-01
We consider continuous time Hopfield-like recurrent networks as dynamical models for gene regulation and neural networks. We are interested in networks that contain n high-degree nodes preferably connected to a large number of N s weakly connected satellites, a property that we call n/N s -centrality. If the hub dynamics is slow, we obtain that the large time network dynamics is completely defined by the hub dynamics. Moreover, such networks are maximally flexible and switchable, in the sense that they can switch from a globally attractive rest state to any structurally stable dynamics when the response time of a special controller hub is changed. In particular, we show that a decrease of the controller hub response time can lead to a sharp variation in the network attractor structure: we can obtain a set of new local attractors, whose number can increase exponentially with N, the total number of nodes of the nework. These new attractors can be periodic or even chaotic. We provide an algorithm, which allows us to design networks with the desired switching properties, or to learn them from time series, by adjusting the interactions between hubs and satellites. Such switchable networks could be used as models for context dependent adaptation in functional genetics or as models for cognitive functions in neuroscience.
Maximal Inequalities for Dependent Random Variables
Hoffmann-Jorgensen, Jorgen
2016-01-01
Maximal inequalities play a crucial role in many probabilistic limit theorem; for instance, the law of large numbers, the law of the iterated logarithm, the martingale limit theorem and the central limit theorem. Let X-1, X-2,... be random variables with partial sums S-k = X-1 + ... + X-k. Then a......Maximal inequalities play a crucial role in many probabilistic limit theorem; for instance, the law of large numbers, the law of the iterated logarithm, the martingale limit theorem and the central limit theorem. Let X-1, X-2,... be random variables with partial sums S-k = X-1 + ... + X...
Maximizing scientific knowledge from randomized clinical trials
Gustafsson, Finn; Atar, Dan; Pitt, Bertram
2010-01-01
Trialists have an ethical and financial responsibility to plan and conduct clinical trials in a manner that will maximize the scientific knowledge gained from the trial. However, the amount of scientific information generated by randomized clinical trials in cardiovascular medicine is highly...
Resistance maximization principle for defending networks against virus attack
Li, Angsheng; Zhang, Xiaohui; Pan, Yicheng
2017-01-01
We investigate the defending of networks against virus attack. We define the resistance of a network to be the maximum number of bits required to determine the code of the module that is accessible from random walk, from which random walk cannot escape. We show that for any network G, R(G) =H1(G) -H2(G) , where R(G) is the resistance of G, H1(G) and H2(G) are the one- and two-dimensional structural information of G, respectively, and that resistance maximization is the principle for defending networks against virus attack. By using the theory, we investigate the defending of real world networks and of the networks generated by the preferential attachment and the security models. We show that there exist networks that are defensible by a small number of controllers from cascading failure of any virus attack. Our theory demonstrates that resistance maximization is the principle for defending networks against virus attacks.
Maximizing scientific knowledge from randomized clinical trials
Gustafsson, Finn; Atar, Dan; Pitt, Bertram;
2010-01-01
Trialists have an ethical and financial responsibility to plan and conduct clinical trials in a manner that will maximize the scientific knowledge gained from the trial. However, the amount of scientific information generated by randomized clinical trials in cardiovascular medicine is highly...... variable. Generation of trial databases and/or biobanks originating in large randomized clinical trials has successfully increased the knowledge obtained from those trials. At the 10th Cardiovascular Trialist Workshop, possibilities and pitfalls in designing and accessing clinical trial databases were......, in particular with respect to collaboration with the trial sponsor and to analytic pitfalls. The advantages of creating screening databases in conjunction with a given clinical trial are described; and finally, the potential for posttrial database studies to become a platform for training young scientists...
Coverage maximization under resource constraints using proliferating random walks
Sudipta Saha; Niloy Ganguly; Abhijit Guria
2015-02-01
Dissemination of information has been one of the prime needs in almost every kind of communication network. The existing algorithms for this service, try to maximize the coverage, i.e., the number of distinct nodes to which a given piece of information could be conveyed under the constraints of time and energy. However, the problem becomes challenging for unstructured and decentralized environments. Due to its simplicity and adaptability, random walk (RW) has been a very useful tool for such environments. Different variants of this technique have been studied. In this paper, we study a history-based non-uniform proliferating random strategy where new walkers are dynamically introduced in the sparse regions of the network. Apart from this, we also study the breadth-first characteristics of the random walk-based algorithms through an appropriately designed metrics.
Lifetime maximization routing with network coding in wireless multihop networks
DING LiangHui; WU Ping; WANG Hao; PAN ZhiWen; YOU XiaoHu
2013-01-01
In this paper, we consider the lifetime maximization routing with network coding in wireless mul- tihop networks. We first show that lifetime maximization with network coding is different from pure routing, throughput maximization with network coding and energy minimization with network coding. Then we formulate lifetime maximization problems in three different cases of （i） no network coding, （ii） two-way network coding, and （iii） overhearing network coding. To solve these problems, we use flow augmenting routing （FA） for the first case, and then extend the FA with network coding （FANC） by using energy minimized one-hop network coding. After that, we investigate the influence of parameters of FANC, evaluate the performance of FANC with two-way and overhearing network coding schemes and compare it with that without network coding under two different power control models, namely, protocol and physical ones. The results show that the lifetime can be improved significantly by using network coding, and the performance gain of network coding decreases with the increase of flow asymmetry and the power control ability.
Network channel allocation and revenue maximization
Hamalainen, Timo; Joutsensalo, Jyrki
2002-09-01
This paper introduces a model that can be used to share link capacity among customers under different kind of traffic conditions. This model is suitable for different kind of networks like the 4G networks (fast wireless access to wired network) to support connections of given duration that requires a certain quality of service. We study different types of network traffic mixed in a same communication link. A single link is considered as a bottleneck and the goal is to find customer traffic profiles that maximizes the revenue of the link. Presented allocation system accepts every calls and there is not absolute blocking, but the offered data rate/user depends on the network load. Data arrival rate depends on the current link utilization, user's payment (selected CoS class) and delay. The arrival rate is (i) increasing with respect to the offered data rate, (ii) decreasing with respect to the price, (iii) decreasing with respect to the network load, and (iv) decreasing with respect to the delay. As an example, explicit formula obeying these conditions is given and analyzed.
Maximal unbordered factors of random strings
Cording, Patrick Hagge; Knudsen, Mathias Bæk Tejs
2016-01-01
A border of a string is a non-empty prefix of the string that is also a suffix of the string, and a string is unbordered if it has no border. Loptev, Kucherov, and Starikovskaya [CPM 2015] conjectured the following: If we pick a string of length n from a fixed alphabet uniformly at random...
Greedy Maximal Scheduling in Wireless Networks
Li, Qiao
2010-01-01
In this paper we consider greedy scheduling algorithms in wireless networks, i.e., the schedules are computed by adding links greedily based on some priority vector. Two special cases are considered: 1) Longest Queue First (LQF) scheduling, where the priorities are computed using queue lengths, and 2) Static Priority (SP) scheduling, where the priorities are pre-assigned. We first propose a closed-form lower bound stability region for LQF scheduling, and discuss the tightness result in some scenarios. We then propose an lower bound stability region for SP scheduling with multiple priority vectors, as well as a heuristic priority assignment algorithm, which is related to the well-known Expectation-Maximization (EM) algorithm. The performance gain of the proposed heuristic algorithm is finally confirmed by simulations.
Maximizing information exchange between complex networks
West, Bruce J.; Geneston, Elvis L.; Grigolini, Paolo
2008-10-01
Science is not merely the smooth progressive interaction of hypothesis, experiment and theory, although it sometimes has that form. More realistically the scientific study of any given complex phenomenon generates a number of explanations, from a variety of perspectives, that eventually requires synthesis to achieve a deep level of insight and understanding. One such synthesis has created the field of out-of-equilibrium statistical physics as applied to the understanding of complex dynamic networks. Over the past forty years the concept of complexity has undergone a metamorphosis. Complexity was originally seen as a consequence of memory in individual particle trajectories, in full agreement with a Hamiltonian picture of microscopic dynamics and, in principle, macroscopic dynamics could be derived from the microscopic Hamiltonian picture. The main difficulty in deriving macroscopic dynamics from microscopic dynamics is the need to take into account the actions of a very large number of components. The existence of events such as abrupt jumps, considered by the conventional continuous time random walk approach to describing complexity was never perceived as conflicting with the Hamiltonian view. Herein we review many of the reasons why this traditional Hamiltonian view of complexity is unsatisfactory. We show that as a result of technological advances, which make the observation of single elementary events possible, the definition of complexity has shifted from the conventional memory concept towards the action of non-Poisson renewal events. We show that the observation of crucial processes, such as the intermittent fluorescence of blinking quantum dots as well as the brain’s response to music, as monitored by a set of electrodes attached to the scalp, has forced investigators to go beyond the traditional concept of complexity and to establish closer contact with the nascent field of complex networks. Complex networks form one of the most challenging areas of
Maximizing information exchange between complex networks
West, Bruce J. [Mathematical and Information Science, Army Research Office, Research Triangle Park, NC 27708 (United States); Physics Department, Duke University, Durham, NC 27709 (United States)], E-mail: bwest@nc.rr.com; Geneston, Elvis L. [Center for Nonlinear Science, University of North Texas, P.O. Box 311427, Denton, TX 76203-1427 (United States); Physics Department, La Sierra University, 4500 Riverwalk Parkway, Riverside, CA 92515 (United States); Grigolini, Paolo [Center for Nonlinear Science, University of North Texas, P.O. Box 311427, Denton, TX 76203-1427 (United States); Istituto di Processi Chimico Fisici del CNR, Area della Ricerca di Pisa, Via G. Moruzzi, 56124, Pisa (Italy); Dipartimento di Fisica ' E. Fermi' Universita' di Pisa, Largo Pontecorvo 3, 56127 Pisa (Italy)
2008-10-15
Science is not merely the smooth progressive interaction of hypothesis, experiment and theory, although it sometimes has that form. More realistically the scientific study of any given complex phenomenon generates a number of explanations, from a variety of perspectives, that eventually requires synthesis to achieve a deep level of insight and understanding. One such synthesis has created the field of out-of-equilibrium statistical physics as applied to the understanding of complex dynamic networks. Over the past forty years the concept of complexity has undergone a metamorphosis. Complexity was originally seen as a consequence of memory in individual particle trajectories, in full agreement with a Hamiltonian picture of microscopic dynamics and, in principle, macroscopic dynamics could be derived from the microscopic Hamiltonian picture. The main difficulty in deriving macroscopic dynamics from microscopic dynamics is the need to take into account the actions of a very large number of components. The existence of events such as abrupt jumps, considered by the conventional continuous time random walk approach to describing complexity was never perceived as conflicting with the Hamiltonian view. Herein we review many of the reasons why this traditional Hamiltonian view of complexity is unsatisfactory. We show that as a result of technological advances, which make the observation of single elementary events possible, the definition of complexity has shifted from the conventional memory concept towards the action of non-Poisson renewal events. We show that the observation of crucial processes, such as the intermittent fluorescence of blinking quantum dots as well as the brain's response to music, as monitored by a set of electrodes attached to the scalp, has forced investigators to go beyond the traditional concept of complexity and to establish closer contact with the nascent field of complex networks. Complex networks form one of the most challenging areas of
Stochastic coupling of two random Boolean networks
Ho, M.-C. [Department of Physics, National Kaohsiung Normal University, Kaohsiung, Taiwan (China)]. E-mail: t1603@nknucc.nknu.edu.tw; Hung, Y.-C. [Department of Physics, National Sun Yat-sen University, Kaohsiung, Taiwan (China)]. E-mail: d9123801@student.nsysu.edu.tw; Jiang, I-M. [Department of Physics, National Sun Yat-sen University, Kaohsiung, Taiwan (China)
2005-08-29
We study the dynamics of two coupled random Boolean networks. Based on the Boolean model studied by Andrecut and Ali [Int. J. Mod. Phys. B 15 (2001) 17] and the stochastic coupling techniques, the density evolution of networks is precisely described by two deterministic coupled polynomial maps. The iteration results of the model match the real networks well. By using MSE and the maximal Lyapunov exponents, the synchronization phenomena of coupled networks is also under our discussion.
Random graph states, maximal flow and Fuss-Catalan distributions
Collins, BenoIt; Nechita, Ion [Department of Mathematics and Statistics, University of Ottawa, Ontario K1N8M2 (Canada); Zyczkowski, Karol [Institute of Physics, Jagiellonian University, ul. Reymonta 4, 30-059 Krakow (Poland)
2010-07-09
For any graph consisting of k vertices and m edges we construct an ensemble of random pure quantum states which describe a system composed of 2m subsystems. Each edge of the graph represents a bipartite, maximally entangled state. Each vertex represents a random unitary matrix generated according to the Haar measure, which describes the coupling between subsystems. Dividing all subsystems into two parts, one may study entanglement with respect to this partition. A general technique to derive an expression for the average entanglement entropy of random pure states associated with a given graph is presented. Our technique relies on Weingarten calculus and flow problems. We analyze the statistical properties of spectra of such random density matrices and show for which cases they are described by the free Poissonian (Marchenko-Pastur) distribution. We derive a discrete family of generalized, Fuss-Catalan distributions and explicitly construct graphs which lead to ensembles of random states characterized by these novel distributions of eigenvalues.
Random graph states, maximal flow and Fuss-Catalan distributions
Collins, Benoit; Zyczkowski, Karol
2010-01-01
For any graph consisting of $k$ vertices and $m$ edges we construct an ensemble of random pure quantum states which describe a system composed of $2m$ subsystems. Each edge of the graph represents a bi-partite, maximally entangled state. Each vertex represents a random unitary matrix generated according to the Haar measure, which describes the coupling between subsystems. Dividing all subsystems into two parts, one may study entanglement with respect to this partition. A general technique to derive an expression for the average entanglement entropy of random pure states associated to a given graph is presented. Our technique relies on Weingarten calculus and flow problems. We analyze statistical properties of spectra of such random density matrices and show for which cases they are described by the free Poissonian (Marchenko-Pastur) distribution. We derive a discrete family of generalized, Fuss-Catalan distributions and explicitly construct graphs which lead to ensembles of random states characterized by thes...
Malarz, K; Szekfu, B; Kulakowski, K
2006-01-01
We consider the average probability X of being informed on a gossip in a given social network. The network is modeled within the random graph theory of Erdos and Renyi. In this theory, a network is characterized by two parameters: the size N and the link probability p. Our experimental data suggest three levels of social inclusion of friendship. The critical value p_c, for which half of agents are informed, scales with the system size as N^{-\\gamma} with \\gamma\\approx 0.68. Computer simulations show that the probability X varies with p as a sigmoidal curve. Influence of the correlations between neighbors is also evaluated: with increasing clustering coefficient C, X decreases.
Goodput Maximization in Cooperative Networks with ARQ
Chen, Qing
2010-01-01
In this paper, the average successful throughput, i.e., goodput, of a coded 3-node cooperative network is studied in a Rayleigh fading environment. It is assumed that a simple automatic repeat request (ARQ) technique is employed in the network so that erroneously received codeword is retransmitted until successful delivery. The relay is assumed to operate in either amplify-and-forward (AF) or decode-and-forward (DF) mode. Under these assumptions, retransmission mechanisms and protocols are described, and the average time required to send information successfully is determined. Subsequently, the goodput for both AF and DF relaying is formulated. The tradeoffs and interactions between the goodput, transmission rates, and relay location are investigated and optimal strategies are identified.
Neural network approach for solving the maximal common subgraph problem.
Shoukry, A; Aboutabl, M
1996-01-01
A new formulation of the maximal common subgraph problem (MCSP), that is implemented using a two-stage Hopfield neural network, is given. Relative merits of this proposed formulation, with respect to current neural network-based solutions as well as classical sequential-search-based solutions, are discussed.
Maximizing Algebraic Connectivity in Interconnected Networks
Shakeri, Heman; Sahneh, Faryad Darabi; Poggi-Corradini, Pietro; Scoglio, Caterina
2015-01-01
Algebraic connectivity, the second eigenvalue of the Laplacian matrix, is a measure of node and link connectivity on networks. When studying interconnected networks it is useful to consider a multiplex model, where the component networks operate together with inter-layer links among them. In order to have a well-connected multilayer structure, it is necessary to optimally design these inter-layer links considering realistic constraints. In this work, we solve the problem of finding an optimal weight distribution for one-to-one inter-layer links under budget constraint. We show that for the special multiplex configurations with identical layers, the uniform weight distribution is always optimal. On the other hand, when the two layers are arbitrary, increasing the budget reveals the existence of two different regimes. Up to a certain threshold budget, the second eigenvalue of the supra-Laplacian is simple, the optimal weight distribution is uniform, and the Fiedler vector is constant on each layer. Increasing t...
Quantifying randomness in real networks.
Orsini, Chiara; Dankulov, Marija M; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri
2015-10-20
Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.
A Geometric-Structure Theory for Maximally Random Jammed Packings
Tian, Jianxiang; Xu, Yaopengxiao; Jiao, Yang; Torquato, Salvatore
2015-11-01
Maximally random jammed (MRJ) particle packings can be viewed as prototypical glasses in that they are maximally disordered while simultaneously being mechanically rigid. The prediction of the MRJ packing density ϕMRJ, among other packing properties of frictionless particles, still poses many theoretical challenges, even for congruent spheres or disks. Using the geometric-structure approach, we derive for the first time a highly accurate formula for MRJ densities for a very wide class of two-dimensional frictionless packings, namely, binary convex superdisks, with shapes that continuously interpolate between circles and squares. By incorporating specific attributes of MRJ states and a novel organizing principle, our formula yields predictions of ϕMRJ that are in excellent agreement with corresponding computer-simulation estimates in almost the entire α-x plane with semi-axis ratio α and small-particle relative number concentration x. Importantly, in the monodisperse circle limit, the predicted ϕMRJ = 0.834 agrees very well with the very recently numerically discovered MRJ density of 0.827, which distinguishes it from high-density “random-close packing” polycrystalline states and hence provides a stringent test on the theory. Similarly, for non-circular monodisperse superdisks, we predict MRJ states with densities that are appreciably smaller than is conventionally thought to be achievable by standard packing protocols.
Polarity related influence maximization in signed social networks.
Dong Li
Full Text Available Influence maximization in social networks has been widely studied motivated by applications like spread of ideas or innovations in a network and viral marketing of products. Current studies focus almost exclusively on unsigned social networks containing only positive relationships (e.g. friend or trust between users. Influence maximization in signed social networks containing both positive relationships and negative relationships (e.g. foe or distrust between users is still a challenging problem that has not been studied. Thus, in this paper, we propose the polarity-related influence maximization (PRIM problem which aims to find the seed node set with maximum positive influence or maximum negative influence in signed social networks. To address the PRIM problem, we first extend the standard Independent Cascade (IC model to the signed social networks and propose a Polarity-related Independent Cascade (named IC-P diffusion model. We prove that the influence function of the PRIM problem under the IC-P model is monotonic and submodular Thus, a greedy algorithm can be used to achieve an approximation ratio of 1-1/e for solving the PRIM problem in signed social networks. Experimental results on two signed social network datasets, Epinions and Slashdot, validate that our approximation algorithm for solving the PRIM problem outperforms state-of-the-art methods.
Polarity related influence maximization in signed social networks.
Li, Dong; Xu, Zhi-Ming; Chakraborty, Nilanjan; Gupta, Anika; Sycara, Katia; Li, Sheng
2014-01-01
Influence maximization in social networks has been widely studied motivated by applications like spread of ideas or innovations in a network and viral marketing of products. Current studies focus almost exclusively on unsigned social networks containing only positive relationships (e.g. friend or trust) between users. Influence maximization in signed social networks containing both positive relationships and negative relationships (e.g. foe or distrust) between users is still a challenging problem that has not been studied. Thus, in this paper, we propose the polarity-related influence maximization (PRIM) problem which aims to find the seed node set with maximum positive influence or maximum negative influence in signed social networks. To address the PRIM problem, we first extend the standard Independent Cascade (IC) model to the signed social networks and propose a Polarity-related Independent Cascade (named IC-P) diffusion model. We prove that the influence function of the PRIM problem under the IC-P model is monotonic and submodular Thus, a greedy algorithm can be used to achieve an approximation ratio of 1-1/e for solving the PRIM problem in signed social networks. Experimental results on two signed social network datasets, Epinions and Slashdot, validate that our approximation algorithm for solving the PRIM problem outperforms state-of-the-art methods.
Biased random walks on multiplex networks
Battiston, Federico; Latora, Vito
2015-01-01
Biased random walks on complex networks are a particular type of walks whose motion is biased on properties of the destination node, such as its degree. In recent years they have been exploited to design efficient strategies to explore a network, for instance by constructing maximally mixing trajectories or by sampling homogeneously the nodes. In multiplex networks, the nodes are related through different types of links (layers or communication channels), and the presence of connections at different layers multiplies the number of possible paths in the graph. In this work we introduce biased random walks on multiplex networks and provide analytical solutions for their long-term properties such as the stationary distribution and the entropy rate. We focus on degree-biased walks and distinguish between two subclasses of random walks: extensive biased walks consider the properties of each node separately at each layer, intensive biased walks deal instead with intrinsically multiplex variables. We study the effec...
How random are complex networks
Orsini, Chiara; Jamakovic, Almerima; Mahadevan, Priya; Colomer-de-Simón, Pol; Vahdat, Amin; Bassler, Kevin E; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri
2015-01-01
Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the $dk$-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks---the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain---and find that many important local and global structural properties of these networks are closely reproduced by $dk$-random graphs whose degree distributions, degree correlations, and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness.
Pan, Indranil; Ghosh, Soumyajit; Gupta, Amitava; 10.1109/PACC.2011.5978958
2012-01-01
Networked Control Systems (NCSs) are often associated with problems like random data losses which might lead to system instability. This paper proposes a method based on the use of variable controller gains to achieve maximum parametric robustness of the plant controlled over a network. Stability using variable controller gains under data loss conditions is analyzed using a suitable Linear Matrix Inequality (LMI) formulation. Also, a Particle Swarm Optimization (PSO) based technique is used to maximize parametric robustness of the plant.
Distinctive features arising in maximally random jammed packings of superballs.
Jiao, Y; Stillinger, F H; Torquato, S
2010-04-01
Dense random packings of hard particles are useful models of granular media and are closely related to the structure of nonequilibrium low-temperature amorphous phases of matter. Most work has been done for random jammed packings of spheres and it is only recently that corresponding packings of nonspherical particles (e.g., ellipsoids) have received attention. Here we report a study of the maximally random jammed (MRJ) packings of binary superdisks and monodispersed superballs whose shapes are defined by |x1|2p+...+|xd|2por=0.5) particles with square symmetry (d=2), or octahedral and cubic symmetry (d=3). In particular, for p=1 the particle is a perfect sphere (circular disk) and for p-->infinity the particle is a perfect cube (square). We find that the MRJ densities of such packings increase dramatically and nonanalytically as one moves away from the circular-disk and sphere point (p=1). Moreover, the disordered packings are hypostatic, i.e., the average number of contacting neighbors is less than twice the total number of degrees of freedom per particle, and yet the packings are mechanically stable. As a result, the local arrangements of particles are necessarily nontrivially correlated to achieve jamming. We term such correlated structures "nongeneric." The degree of "nongenericity" of the packings is quantitatively characterized by determining the fraction of local coordination structures in which the central particles have fewer contacting neighbors than average. We also show that such seemingly "special" packing configurations are counterintuitively not rare. As the anisotropy of the particles increases, the fraction of rattlers decreases while the minimal orientational order as measured by the tetratic and cubatic order parameters increases. These characteristics result from the unique manner in which superballs break their rotational symmetry, which also makes the superdisk and superball packings distinctly different from other known nonspherical hard
Equidistant Linear Network Codes with maximal Error-protection from Veronese Varieties
Hansen, Johan P.
2012-01-01
Linear network coding transmits information in terms of a basis of a vector space and the information is received as a basis of a possible altered vectorspace. Ralf Koetter and Frank R. Kschischang in Coding for errors and erasures in random network coding (IEEE Transactions on Information Theory...... construct explicit families of vector-spaces of constant dimension where any pair of distinct vector-spaces are equidistant in the above metric. The parameters of the resulting linear network codes which have maximal error-protection are determined....
Maximal Displacement for Bridges of Random Walks in a Random Environment
Gantert, Nina
2009-01-01
It is well known that the distribution of simple random walks on $\\bf{Z}$ conditioned on returning to the origin after $2n$ steps does not depend on $p= P(S_1 = 1)$, the probability of moving to the right. Moreover, conditioned on $\\{S_{2n}=0\\}$ the maximal displacement $\\max_{k\\leq 2n} |S_k|$ converges in distribution when scaled by $\\sqrt{n}$ (diffusive scaling). We consider the analogous problem for transient random walks in random environments on $\\bf{Z}$. We show that under the quenched law $P_\\omega$ (conditioned on the environment $\\omega$), the maximal displacement of the random walk when conditioned to return to the origin at time $2n$ is no longer necessarily of the order $\\sqrt{n}$. If the environment is nestling (both positive and negative local drifts exist) then the maximal displacement conditioned on returning to the origin at time $2n$ is of order $n^{\\kappa/(\\kappa+1)}$, where the constant $\\kappa>0$ depends on the law on environment. On the other hand, if the environment is marginally nestli...
Maximizing residual capacity in connection-oriented networks
Krzysztof Walkowiak
2006-07-01
Full Text Available The following problem arises in the study of survivable connection-oriented networks. Given a demand matrix to be routed between nodes, we want to route all demands, so that the residual capacity given by the difference between link capacity and link flow is maximized. Each demand can use only one path. Therefore, the flow is modeled as nonbifurcated multicommodity flow. We call the considered problem nonbifurcated congestion (NBC problem. Solving NBC problem enables robust restoration of failed connections in a case of network failure. We propose a new heuristic algorithm for NBC problem and compare its performance with existing algorithms.
Maximizing the Spread of Cascades Using Network Design
Sheldon, Daniel; Elmachtoub, Adam N; Finseth, Ryan; Sabharwal, Ashish; Conrad, Jon; Gomes, Carla P; Shmoys, David; Allen, William; Amundsen, Ole; Vaughan, William
2012-01-01
We introduce a new optimization framework to maximize the expected spread of cascades in networks. Our model allows a rich set of actions that directly manipulate cascade dy- namics by adding nodes or edges to the net- work. Our motivating application is one in spatial conservation planning, where a cas- cade models the dispersal of wild animals through a fragmented landscape. We propose a mixed integer programming (MIP) formu- lation that combines elements from network design and stochastic optimization. Our ap- proach results in solutions with stochastic op- timality guarantees and points to conserva- tion strategies that are fundamentally dier- ent from naive approaches.
Balance between noise and information flow maximizes set complexity of network dynamics.
Tuomo Mäki-Marttunen
Full Text Available Boolean networks have been used as a discrete model for several biological systems, including metabolic and genetic regulatory networks. Due to their simplicity they offer a firm foundation for generic studies of physical systems. In this work we show, using a measure of context-dependent information, set complexity, that prior to reaching an attractor, random Boolean networks pass through a transient state characterized by high complexity. We justify this finding with a use of another measure of complexity, namely, the statistical complexity. We show that the networks can be tuned to the regime of maximal complexity by adding a suitable amount of noise to the deterministic Boolean dynamics. In fact, we show that for networks with Poisson degree distributions, all networks ranging from subcritical to slightly supercritical can be tuned with noise to reach maximal set complexity in their dynamics. For networks with a fixed number of inputs this is true for near-to-critical networks. This increase in complexity is obtained at the expense of disruption in information flow. For a large ensemble of networks showing maximal complexity, there exists a balance between noise and contracting dynamics in the state space. In networks that are close to critical the intrinsic noise required for the tuning is smaller and thus also has the smallest effect in terms of the information processing in the system. Our results suggest that the maximization of complexity near to the state transition might be a more general phenomenon in physical systems, and that noise present in a system may in fact be useful in retaining the system in a state with high information content.
Characterization of maximally random jammed sphere packings: Voronoi correlation functions.
Klatt, Michael A; Torquato, Salvatore
2014-11-01
We characterize the structure of maximally random jammed (MRJ) sphere packings by computing the Minkowski functionals (volume, surface area, and integrated mean curvature) of their associated Voronoi cells. The probability distribution functions of these functionals of Voronoi cells in MRJ sphere packings are qualitatively similar to those of an equilibrium hard-sphere liquid and partly even to the uncorrelated Poisson point process, implying that such local statistics are relatively structurally insensitive. This is not surprising because the Minkowski functionals of a single Voronoi cell incorporate only local information and are insensitive to global structural information. To improve upon this, we introduce descriptors that incorporate nonlocal information via the correlation functions of the Minkowski functionals of two cells at a given distance as well as certain cell-cell probability density functions. We evaluate these higher-order functions for our MRJ packings as well as equilibrium hard spheres and the Poisson point process. It is shown that these Minkowski correlation and density functions contain visibly more information than the corresponding standard pair-correlation functions. We find strong anticorrelations in the Voronoi volumes for the hyperuniform MRJ packings, consistent with previous findings for other pair correlations [A. Donev et al., Phys. Rev. Lett. 95, 090604 (2005)PRLTAO0031-900710.1103/PhysRevLett.95.090604], indicating that large-scale volume fluctuations are suppressed by accompanying large Voronoi cells with small cells, and vice versa. In contrast to the aforementioned local Voronoi statistics, the correlation functions of the Voronoi cells qualitatively distinguish the structure of MRJ sphere packings (prototypical glasses) from that of not only the Poisson point process but also the correlated equilibrium hard-sphere liquids. Moreover, while we did not find any perfect icosahedra (the locally densest possible structure in which a
Maximizing Information Diffusion in the Cyber-physical Integrated Network
Hongliang Lu
2015-11-01
Full Text Available Nowadays, our living environment has been embedded with smart objects, such as smart sensors, smart watches and smart phones. They make cyberspace and physical space integrated by their abundant abilities of sensing, communication and computation, forming a cyber-physical integrated network. In order to maximize information diffusion in such a network, a group of objects are selected as the forwarding points. To optimize the selection, a minimum connected dominating set (CDS strategy is adopted. However, existing approaches focus on minimizing the size of the CDS, neglecting an important factor: the weight of links. In this paper, we propose a distributed maximizing the probability of information diffusion (DMPID algorithm in the cyber-physical integrated network. Unlike previous approaches that only consider the size of CDS selection, DMPID also considers the information spread probability that depends on the weight of links. To weaken the effects of excessively-weighted links, we also present an optimization strategy that can properly balance the two factors. The results of extensive simulation show that DMPID can nearly double the information diffusion probability, while keeping a reasonable size of selection with low overhead in different distributed networks.
Maximizing Information Diffusion in the Cyber-physical Integrated Network.
Lu, Hongliang; Lv, Shaohe; Jiao, Xianlong; Wang, Xiaodong; Liu, Juan
2015-11-11
Nowadays, our living environment has been embedded with smart objects, such as smart sensors, smart watches and smart phones. They make cyberspace and physical space integrated by their abundant abilities of sensing, communication and computation, forming a cyber-physical integrated network. In order to maximize information diffusion in such a network, a group of objects are selected as the forwarding points. To optimize the selection, a minimum connected dominating set (CDS) strategy is adopted. However, existing approaches focus on minimizing the size of the CDS, neglecting an important factor: the weight of links. In this paper, we propose a distributed maximizing the probability of information diffusion (DMPID) algorithm in the cyber-physical integrated network. Unlike previous approaches that only consider the size of CDS selection, DMPID also considers the information spread probability that depends on the weight of links. To weaken the effects of excessively-weighted links, we also present an optimization strategy that can properly balance the two factors. The results of extensive simulation show that DMPID can nearly double the information diffusion probability, while keeping a reasonable size of selection with low overhead in different distributed networks.
Competition between Homophily and Information Entropy Maximization in Social Networks.
Zhao, Jichang; Liang, Xiao; Xu, Ke
2015-01-01
In social networks, it is conventionally thought that two individuals with more overlapped friends tend to establish a new friendship, which could be stated as homophily breeding new connections. While the recent hypothesis of maximum information entropy is presented as the possible origin of effective navigation in small-world networks. We find there exists a competition between information entropy maximization and homophily in local structure through both theoretical and experimental analysis. This competition suggests that a newly built relationship between two individuals with more common friends would lead to less information entropy gain for them. We demonstrate that in the evolution of the social network, both of the two assumptions coexist. The rule of maximum information entropy produces weak ties in the network, while the law of homophily makes the network highly clustered locally and the individuals would obtain strong and trust ties. A toy model is also presented to demonstrate the competition and evaluate the roles of different rules in the evolution of real networks. Our findings could shed light on the social network modeling from a new perspective.
Maximal Neighbor Similarity Reveals Real Communities in Networks
Žalik, Krista Rizman
2015-12-01
An important problem in the analysis of network data is the detection of groups of densely interconnected nodes also called modules or communities. Community structure reveals functions and organizations of networks. Currently used algorithms for community detection in large-scale real-world networks are computationally expensive or require a priori information such as the number or sizes of communities or are not able to give the same resulting partition in multiple runs. In this paper we investigate a simple and fast algorithm that uses the network structure alone and requires neither optimization of pre-defined objective function nor information about number of communities. We propose a bottom up community detection algorithm in which starting from communities consisting of adjacent pairs of nodes and their maximal similar neighbors we find real communities. We show that the overall advantage of the proposed algorithm compared to the other community detection algorithms is its simple nature, low computational cost and its very high accuracy in detection communities of different sizes also in networks with blurred modularity structure consisting of poorly separated communities. All communities identified by the proposed method for facebook network and E-Coli transcriptional regulatory network have strong structural and functional coherence.
Budget Allocation for Maximizing Viral Advertising in Social Networks
Bo-Lei Zhang; Zhu-Zhong Qian; Wen-Zhong Li; Bin Tang; Xiaoming Fu
2016-01-01
Viral advertising in social networks has arisen as one of the most promising ways to increase brand awareness and product sales. By distributing a limited budget, we can incentivize a set of users as initial adopters so that the advertising can start from the initial adopters and spread via social links to become viral. Despite extensive researches in how to target the most influential users, a key issue is often neglected: how to incentivize the initial adopters. In the problem of influence maximization, the assumption is that each user has a fixed cost for being initial adopters, while in practice, user decisions for accepting the budget to be initial adopters are often probabilistic rather than deterministic. In this paper, we study optimal budget allocation in social networks to maximize the spread of viral advertising. In particular, a concave probability model is introduced to characterize each user’s utility for being an initial adopter. Under this model, we show that it is NP-hard to find an optimal budget allocation for maximizing the spread of viral advertising. We then present a novel discrete greedy algorithm with near optimal performance, and further propose scaling-up techniques to improve the time-eﬃciency of our algorithm. Extensive experiments on real-world social graphs are implemented to validate the effectiveness of our algorithm in practice. The results show that our algorithm can outperform other intuitive heuristics significantly in almost all cases.
Influence Maximization in Social Networks: Towards an Optimal Algorithmic Solution
Borgs, Christian; Chayes, Jennifer; Lucier, Brendan
2012-01-01
Diffusion is a fundamental graph process, underpinning such phenomena as epidemic disease contagion and the spread of innovation by word-of-mouth. We address the algorithmic problem of finding a set of k initial seed nodes in a network so that the expected size of the resulting cascade is maximized, under the standard independent cascade model of network diffusion. Our main result is an algorithm for the influence maximization problem that obtains the near-optimal approximation factor of (1 - 1/e - epsilon), for any epsilon > 0, in time O((m+n)log(n) / epsilon^3) where n and m are the number of vertices and edges in the network. Our algorithm is nearly runtime-optimal (up to a logarithmic factor) as we establish a lower bound of Omega(m+n) on the runtime required to obtain a constant approximation. Our method also allows a provable tradeoff between solution quality and runtime: we obtain an O(1/beta)-approximation in time O(n log^3(n) * a(G) / beta) for any beta > 1, where a(G) denotes the arboricity of the d...
Maximizing Lifetime of Wireless Sensor Networks with Mobile Sink Nodes
Yourong Chen
2014-01-01
Full Text Available In order to maximize network lifetime and balance energy consumption when sink nodes can move, maximizing lifetime of wireless sensor networks with mobile sink nodes (MLMS is researched. The movement path selection method of sink nodes is proposed. Modified subtractive clustering method, k-means method, and nearest neighbor interpolation method are used to obtain the movement paths. The lifetime optimization model is established under flow constraint, energy consumption constraint, link transmission constraint, and other constraints. The model is solved from the perspective of static and mobile data gathering of sink nodes. Subgradient method is used to solve the lifetime optimization model when one sink node stays at one anchor location. Geometric method is used to evaluate the amount of gathering data when sink nodes are moving. Finally, all sensor nodes transmit data according to the optimal data transmission scheme. Sink nodes gather the data along the shortest movement paths. Simulation results show that MLMS can prolong network lifetime, balance node energy consumption, and reduce data gathering latency under appropriate parameters. Under certain conditions, it outperforms Ratio_w, TPGF, RCC, and GRND.
Limiting distribution of maximal crossing and nesting of Poissonized random matchings
Baik, Jinho
2011-01-01
The notion of r-crossing and r-nesting of a complete match- ing was introduced and a symmetry property was proved by Chen, Deng, Du, Stanley and Yan in 2007. We consider random matchings of large size and study the maximal crossing and the maximal nesting. It is known that the marginal distribution of each of them converges to the GOE Tracy-Widom distribution. We show that the maximal crossing and the maximal nesting becomes independent asymptotically, and eval- uate the joint distribution for the Poissonized random matchings explic- itly to the first correction term. This leads to an evaluation of the asymptotic of the covariance. Furthermore, we compute the explicit second correction term of the distributions function of two ob jects: (a) the length of the longest increasing subsequence of Poissonized random permutation and (b) the maximal crossing, and hence also the maximal nesting, of Poissonized random matching.
Maximization of Extractable Randomness in a Quantum Random-Number Generator
Haw, J. Y.; Assad, S. M.; Lance, A. M.; Ng, N. H. Y.; Sharma, V.; Lam, P. K.; Symul, T.
2015-05-01
The generation of random numbers via quantum processes is an efficient and reliable method to obtain true indeterministic random numbers that are of vital importance to cryptographic communication and large-scale computer modeling. However, in realistic scenarios, the raw output of a quantum random-number generator is inevitably tainted by classical technical noise. The integrity of the device can be compromised if this noise is tampered with or even controlled by some malicious party. To safeguard against this, we propose and experimentally demonstrate an approach that produces side-information-independent randomness that is quantified by min-entropy conditioned on this classical noise. We present a method for maximizing the conditional min entropy of the number sequence generated from a given quantum-to-classical-noise ratio. The detected photocurrent in our experiment is shown to have a real-time random-number generation rate of 14 (Mb i t /s )/MHz . The spectral response of the detection system shows the potential to deliver more than 70 Gbit /s of random numbers in our experimental setup.
Accurate and efficient maximal ball algorithm for pore network extraction
Arand, Frederick; Hesser, Jürgen
2017-04-01
The maximal ball (MB) algorithm is a well established method for the morphological analysis of porous media. It extracts a network of pores and throats from volumetric data. This paper describes structural modifications to the algorithm, while the basic concepts are preserved. Substantial improvements to accuracy and efficiency are achieved as follows: First, all calculations are performed on a subvoxel accurate distance field, and no approximations to discretize balls are made. Second, data structures are simplified to keep memory usage low and improve algorithmic speed. Third, small and reasonable adjustments increase speed significantly. In volumes with high porosity, memory usage is improved compared to classic MB algorithms. Furthermore, processing is accelerated more than three times. Finally, the modified MB algorithm is verified by extracting several network properties from reference as well as real data sets. Runtimes are measured and compared to literature.
Universality in random quantum networks
Novotný, Jaroslav; Alber, Gernot; Jex, Igor
2015-12-01
Networks constitute efficient tools for assessing universal features of complex systems. In physical contexts, classical as well as quantum networks are used to describe a wide range of phenomena, such as phase transitions, intricate aspects of many-body quantum systems, or even characteristic features of a future quantum internet. Random quantum networks and their associated directed graphs are employed for capturing statistically dominant features of complex quantum systems. Here, we develop an efficient iterative method capable of evaluating the probability of a graph being strongly connected. It is proven that random directed graphs with constant edge-establishing probability are typically strongly connected, i.e., any ordered pair of vertices is connected by a directed path. This typical topological property of directed random graphs is exploited to demonstrate universal features of the asymptotic evolution of large random qubit networks. These results are independent of our knowledge of the details of the network topology. These findings suggest that other highly complex networks, such as a future quantum internet, may also exhibit similar universal properties.
Generating random networks and graphs
Coolen, Ton; Roberts, Ekaterina
2017-01-01
This book supports researchers who need to generate random networks, or who are interested in the theoretical study of random graphs. The coverage includes exponential random graphs (where the targeted probability of each network appearing in the ensemble is specified), growth algorithms (i.e. preferential attachment and the stub-joining configuration model), special constructions (e.g. geometric graphs and Watts Strogatz models) and graphs on structured spaces (e.g. multiplex networks). The presentation aims to be a complete starting point, including details of both theory and implementation, as well as discussions of the main strengths and weaknesses of each approach. It includes extensive references for readers wishing to go further. The material is carefully structured to be accessible to researchers from all disciplines while also containing rigorous mathematical analysis (largely based on the techniques of statistical mechanics) to support those wishing to further develop or implement the theory of rand...
Network architecture underlying maximal separation of neuronal representations
Ron A Jortner
2013-01-01
Full Text Available One of the most basic and general tasks faced by all nervous systems is extracting relevant information from the organism’s surrounding world. While physical signals available to sensory systems are often continuous, variable, overlapping and noisy, high-level neuronal representations used for decision-making tend to be discrete, specific, invariant, and highly separable. This study addresses the question of how neuronal specificity is generated. Inspired by experimental findings on network architecture in the olfactory system of the locust, I construct a highly simplified theoretical framework which allows for analytic solution of its key properties. For generalized feed-forward systems, I show that an intermediate range of connectivity values between source- and target-populations leads to a combinatorial explosion of wiring possibilities, resulting in input spaces which are, by their very nature, exquisitely sparsely populated. In particular, connection probability ½, as found in the locust antennal-lobe–mushroom-body circuit, serves to maximize separation of neuronal representations across the target Kenyon-cells, and explains their specific and reliable responses. This analysis yields a function expressing response specificity in terms of lower network-parameters; together with appropriate gain control this leads to a simple neuronal algorithm for generating arbitrarily sparse and selective codes and linking network architecture and neural coding. I suggest a way to easily construct ecologically meaningful representations from this code.
Moving multiple sinks through wireless sensor networks for lifetime maximization.
Petrioli, Chiara (Universita di Roma); Carosi, Alessio (Universita di Roma); Basagni, Stefano (Northeastern University); Phillips, Cynthia Ann
2008-01-01
Unattended sensor networks typically watch for some phenomena such as volcanic events, forest fires, pollution, or movements in animal populations. Sensors report to a collection point periodically or when they observe reportable events. When sensors are too far from the collection point to communicate directly, other sensors relay messages for them. If the collection point location is static, sensor nodes that are closer to the collection point relay far more messages than those on the periphery. Assuming all sensor nodes have roughly the same capabilities, those with high relay burden experience battery failure much faster than the rest of the network. However, since their death disconnects the live nodes from the collection point, the whole network is then dead. We consider the problem of moving a set of collectors (sinks) through a wireless sensor network to balance the energy used for relaying messages, maximizing the lifetime of the network. We show how to compute an upper bound on the lifetime for any instance using linear and integer programming. We present a centralized heuristic that produces sink movement schedules that produce network lifetimes within 1.4% of the upper bound for realistic settings. We also present a distributed heuristic that produces lifetimes at most 25:3% below the upper bound. More specifically, we formulate a linear program (LP) that is a relaxation of the scheduling problem. The variables are naturally continuous, but the LP relaxes some constraints. The LP has an exponential number of constraints, but we can satisfy them all by enforcing only a polynomial number using a separation algorithm. This separation algorithm is a p-median facility location problem, which we can solve efficiently in practice for huge instances using integer programming technology. This LP selects a set of good sensor configurations. Given the solution to the LP, we can find a feasible schedule by selecting a subset of these configurations, ordering them
Influence maximization in complex networks through optimal percolation
Morone, Flaviano
2015-01-01
The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network [1]; or, if immunized, would prevent the diffusion of a large scale epidemic [2,3]. Localizing this optimal, i.e. minimal, set of structural nodes, called influencers, is one of the most important problems in network science [4,5]. Despite the vast use of heuristic strategies to identify influential spreaders [6-14], the problem remains unsolved. Here, we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix [15] of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected we...
Influence maximization in complex networks through optimal percolation
Morone, Flaviano; Makse, Hernan; CUNY Collaboration; CUNY Collaboration
The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic. Localizing this optimal, that is, minimal, set of structural nodes, called influencers, is one of the most important problems in network science. Here we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected weakly connected nodes emerges among the optimal influencers. Reference: F. Morone, H. A. Makse, Nature 524,65-68 (2015)
On Random Linear Network Coding for Butterfly Network
Guang, Xuan
2010-01-01
Random linear network coding is a feasible encoding tool for network coding, specially for the non-coherent network, and its performance is important in theory and application. In this letter, we study the performance of random linear network coding for the well-known butterfly network by analyzing the failure probabilities. We determine the failure probabilities of random linear network coding for the well-known butterfly network and the butterfly network with channel failure probability p.
Maximizing Networking Capacity in Multi-Channel Multi-Radio Wireless Networks
万鹏俊; 万志国
2014-01-01
Providing each node with one or more multi-channel radios offers a promising avenue for enhancing the network capacity by simultaneously exploiting multiple non-overlapping channels through different radio interfaces and mitigating interferences through proper channel assignment. However, it is quite challenging to effectively utilize multiple channels and/or multiple radios to maximize throughput capacity. The National Natural Science Foundation of China (NSFC) Pro ject 61128005 conducted comprehensive algorithmic-theoretic and queuing-theoretic studies of maximizing wireless networking capacity in multi-channel multi-radio (MC-MR) wireless networks under the protocol interference model and fundamentally advanced the state of the art. In addition, under the notoriously hard physical interference model, this project has taken initial algorithmic studies on maximizing the network capacity, with or without power control. We expect the new techniques and tools developed in this project will have wide applications in capacity planning, resource allocation and sharing, and protocol design for wireless networks, and will serve as the basis for future algorithm developments in wireless networks with advanced features, such as multi-input multi-output (MIMO) wireless networks.
Large deviations of the maximal eigenvalue of random matrices
Borot, Gaëtan; Majumdar, Satya; Nadal, Céline
2011-01-01
We present detailed computations of the 'at least finite' terms (three dominant orders) of the free energy in a one-cut matrix model with a hard edge a, in beta-ensembles, with any polynomial potential. beta is a positive number, so not restricted to the standard values beta = 1 (hermitian matrices), beta = 1/2 (symmetric matrices), beta = 2 (quaternionic self-dual matrices). This model allows to study the statistic of the maximum eigenvalue of random matrices. We compute the large deviation function to the left of the expected maximum. We specialize our results to the gaussian beta-ensembles and check them numerically. Our method is based on general results and procedures already developed in the literature to solve the Pastur equations (also called "loop equations"). It allows to compute the left tail of the analog of Tracy-Widom laws for any beta, including the constant term.
Jellyfish: Networking Data Centers Randomly
Singla, Ankit; Popa, Lucian; Godfrey, P Brighten
2011-01-01
Industry experience indicates that the ability to incrementally expand data centers is essential. However, existing high-bandwidth network designs have rigid structure that interferes with incremental expansion. We present Jellyfish, a high-capacity network interconnect, which, by adopting a random graph topology, yields itself naturally to incremental expansion. Somewhat surprisingly, Jellyfish is more cost-efficient than a fat-tree: A Jellyfish interconnect built using the same equipment as a fat-tree, supports as many as 25% more servers at full capacity at the scale of a few thousand nodes, and this advantage improves with scale. Jellyfish also allows great flexibility in building networks with different degrees of oversubscription. However, Jellyfish's unstructured design brings new challenges in routing, physical layout, and wiring. We describe and evaluate approaches that resolve these challenges effectively, indicating that Jellyfish could be deployed in today's data centers.
On Characterizing the Local Pooling Factor of Greedy Maximal Scheduling in Random Graphs
Wildman, Jeffrey; Weber, Steven
2014-01-01
The study of the optimality of low-complexity greedy scheduling techniques in wireless communications networks is a very complex problem. The Local Pooling (LoP) factor provides a single-parameter means of expressing the achievable capacity region (and optimality) of one such scheme, greedy maximal scheduling (GMS). The exact LoP factor for an arbitrary network graph is generally difficult to obtain, but may be evaluated or bounded based on the network graph's particular structure. In this pa...
Kerner, Boris S
2016-01-01
We show that the minimization of travel times in a network as generally accepted in classical traffic and transportation theories deteriorates the traffic system through a considerable increase in the probability of traffic breakdown in the network. We introduce a network characteristic {\\it minimum network capacity} that shows that rather than the minimization of travel times in the network, the minimization of the probability of traffic breakdown in the network maximizes the network throughput at which free flow persists in the whole network.
Organization of growing random networks
Krapivsky, P. L.; Redner, S.
2001-06-01
The organizational development of growing random networks is investigated. These growing networks are built by adding nodes successively, and linking each to an earlier node of degree k with an attachment probability A{sub k}. When A{sub k} grows more slowly than linearly with k, the number of nodes with k links, N{sub k}(t), decays faster than a power law in k, while for A{sub k} growing faster than linearly in k, a single node emerges which connects to nearly all other nodes. When A{sub k} is asymptotically linear, N{sub k}(t){similar_to}tk{sup {minus}{nu}}, with {nu} dependent on details of the attachment probability, but in the range 2{lt}{nu}{lt}{infinity}. The combined age and degree distribution of nodes shows that old nodes typically have a large degree. There is also a significant correlation in the degrees of neighboring nodes, so that nodes of similar degree are more likely to be connected. The size distributions of the in and out components of the network with respect to a given node{emdash}namely, its {open_quotes}descendants{close_quotes} and {open_quotes}ancestors{close_quotes}{emdash}are also determined. The in component exhibits a robust s{sup {minus}2} power-law tail, where s is the component size. The out component has a typical size of order lnt, and it provides basic insights into the genealogy of the network.
Random networks and mixing laws
Madden, T.R.
1976-12-01
Random networks are investigated as models of heterogeneous media. A general approximate structure is used where the networks are described as a system of embedded networks, and the critical behavior and averaging behavior of such networks are developed. These results are applied to a study of the electrical conductivity of porous media, with special attention to an Archie's law behavior. It appears that the wide range of crack and pore widths in rocks makes the resulting conductivity relatively insensitive to the topology of their interconnections and allows one to make reasonable predictions of rock conductivities, given the distribution of crack and pore widths. It also appears that with low porosity rocks the conductivity is controlled by the microcrack population which only accounts for a fraction of the total porosity. It would seem, therefore, that Archie's law is a feature of some general trend between porosity and crack and pore width distributions rather than a fundamental property of porous media. The law of the geometric mean is an accurate predictor of the physical properties of a mixture of different materials. This mixing law can result from an equal balance of series and parallel arrangements which can be produced by an appropriate distribution of shapes. A brief look is given to problems of anisotropic distributions for the conductivity problem, and it is shown how the averaging process greatly dilutes the microscopic anisotropy in producing the macroscopic properties.
Clique percolation in random networks.
Derényi, Imre; Palla, Gergely; Vicsek, Tamás
2005-04-29
The notion of k-clique percolation in random graphs is introduced, where k is the size of the complete subgraphs whose large scale organizations are analytically and numerically investigated. For the Erdos-Rényi graph of N vertices we obtain that the percolation transition of k-cliques takes place when the probability of two vertices being connected by an edge reaches the threshold p(c) (k) = [(k - 1)N](-1/(k - 1)). At the transition point the scaling of the giant component with N is highly nontrivial and depends on k. We discuss why clique percolation is a novel and efficient approach to the identification of overlapping communities in large real networks.
Handbook of Large-Scale Random Networks
Bollobas, Bela; Miklos, Dezso
2008-01-01
Covers various aspects of large-scale networks, including mathematical foundations and rigorous results of random graph theory, modeling and computational aspects of large-scale networks, as well as areas in physics, biology, neuroscience, sociology and technical areas
Maximally random jamming of one-component and binary hard-disk fluids in two dimensions.
Xu, Xinliang; Rice, Stuart A
2011-02-01
We report calculations of the density of maximally random jamming of one-component and binary hard-disk fluids. The theoretical structure used provides a common framework for description of the hard-disk liquid-to-hexatic, the liquid-to-hexagonal crystal, and the liquid to maximally random jammed state transitions. Our analysis is based on locating a particular bifurcation of the solutions of the integral equation for the inhomogeneous single-particle density at the transition between different spatial structures. The bifurcation of solutions we study is initiated from the dense metastable fluid, and we associate it with the limit of stability of the fluid, which we identify with the transition from the metastable fluid to a maximally random jammed state. For the one-component hard-disk fluid the predicted packing fraction at which the metastable fluid to maximally random jammed state transition occurs is 0.84, in excellent agreement with the experimental value 0.84 ± 0.02. The corresponding analysis of the limit of stability of a binary hard-disk fluid with specified disk-diameter ratio and disk composition requires extra approximations in the representations of the direct correlation function, the equation of state, and the number of order parameters accounted for. Keeping only the order parameter identified with the largest peak in the structure factor of the highest-density regular lattice with the same disk- diameter ratio and disk composition as the binary fluid, the predicted density of maximally random jamming is found to be 0.84-0.87, depending on the equation of state used, and very weakly dependent on the ratio of disk diameters and the fluid composition, in agreement with both experimental data and computer simulation data.
Least Cost Influence Maximization Across Multiple Social Networks
Zhang, Huiyuan; Das, Soham; Zhang, Huiling; Thai, My T
2016-01-01
Recently in Online Social Networks (OSNs), the Least Cost Influence (LCI) problem has become one of the central research topics. It aims at identifying a minimum number of seed users who can trigger a wide cascade of information propagation. Most of existing literature investigated the LCI problem only based on an individual network. However, nowadays users often join several OSNs such that information could be spread across different networks simultaneously. Therefore, in order to obtain the best set of seed users, it is crucial to consider the role of overlapping users under this circumstances. In this article, we propose a unified framework to represent and analyze the influence diffusion in multiplex networks. More specifically, we tackle the LCI problem by mapping a set of networks into a single one via lossless and lossy coupling schemes. The lossless coupling scheme preserves all properties of original networks to achieve high quality solutions, while the lossy coupling scheme offers an attractive alte...
Perturbing transient Random Walk in a Random Environment with cookies of maximal strength
Bauernschubert, Elisabeth
2011-01-01
We consider a left-transient random walk in a random environment on Z that will be disturbed by cookies inducing a drift to the right of strength 1. The number of cookies per site is i.i.d. and independent of the environment. Criteria for recurrence and transience of the random walk are obtained. For this purpose we use subcritical branching processes in random environments with immigration and formulate criteria for recurrence and transience for these processes.
Universality in complex networks: random matrix analysis.
Bandyopadhyay, Jayendra N; Jalan, Sarika
2007-08-01
We apply random matrix theory to complex networks. We show that nearest neighbor spacing distribution of the eigenvalues of the adjacency matrices of various model networks, namely scale-free, small-world, and random networks follow universal Gaussian orthogonal ensemble statistics of random matrix theory. Second, we show an analogy between the onset of small-world behavior, quantified by the structural properties of networks, and the transition from Poisson to Gaussian orthogonal ensemble statistics, quantified by Brody parameter characterizing a spectral property. We also present our analysis for a protein-protein interaction network in budding yeast.
Correlated multiplexity induces unusual connectivity in multiplex random networks
Lee, Kyu-Min; Cho, Won-kuk; Goh, K -I; Kim, I -M
2011-01-01
Nodes in a complex networked system often engage in more than one type of interactions among them; they form a multiplex network with multiple types of links. In real-world complex systems, a node's degree for one type of links and that for the other are not randomly distributed but correlated, which we term correlated multiplexity. In this paper we study a simple model of multiplex random networks and show that the correlated multiplexity can induce unusual properties of giant component in the network. Specifically, when the degrees of a node for different interactions in a duplex Erdos-Renyi network are maximally correlated, the network contains the giant component for any nonzero link densities. On the contrary, when the degrees of a node are maximally anti-correlated, the emergence of giant component is significantly delayed, yet the entire network becomes connected into a single component at a finite link density. We also discuss the mixing patterns and the cases with imperfect correlated multiplexity.
Gaussian Networks Generated by Random Walks
Javarone, Marco Alberto
2014-01-01
We propose a random walks based model to generate complex networks. Many authors studied and developed different methods and tools to analyze complex networks by random walk processes. Just to cite a few, random walks have been adopted to perform community detection, exploration tasks and to study temporal networks. Moreover, they have been used also to generate scale-free networks. In this work, we define a random walker that plays the role of "edges-generator". In particular, the random walker generates new connections and uses these ones to visit each node of a network. As result, the proposed model allows to achieve networks provided with a Gaussian degree distribution, and moreover, some features as the clustering coefficient and the assortativity show a critical behavior. Finally, we performed numerical simulations to study the behavior and the properties of the cited model.
Location Based Throughput Maximization Routing in Energy Constrained Mobile Ad-hoc Network
V. Sumathy
2006-01-01
Full Text Available In wireless Ad-hoc network, power consumption becomes an important issue due to limited battery power. One of the reasons for energy expenditure in this network is irregularly distributed node pattern, which impose large interference range in certain area. To maximize the lifetime of ad-hoc mobile network, the power consumption rate of each node must be evenly distributed and the over all transmission range of each node must be minimized. Our protocol, Location based throughput maximization routing in energy constrained Ad-hoc network finds routing paths, which maximize the lifetime of individual nodes and minimize the total transmission energy consumption. The life of the entire network is increased and the network throughput is also increased. The reliability of the path is also increased. Location based energy constrained routing finds the distance between the nodes. Based on the distance the transmission power required is calculated and dynamically reduces the total transmission energy.
Multiple Sink Positioning and Routing to Maximize the Lifetime of Sensor Networks
Kim, Haeyong; Kwon, Taekyoung; Mah, Pyeongsoo
In wireless sensor networks, the sensor nodes collect data, which are routed to a sink node. Most of the existing proposals address the routing problem to maximize network lifetime in the case of a single sink node. In this paper, we extend this problem into the case of multiple sink nodes. To maximize network lifetime, we consider the two problems: (i) how to position multiple sink nodes in the area, and (ii) how to route traffic flows from sensor nodes to sink nodes. In this paper, the solutions to these problems are formulated into a Mixed Integer Linear Programming (MILP) model. However, it is computationally difficult to solve the MILP formulation as the size of sensor network grows because MILP is NP-hard. Thus, we propose a heuristic algorithm, which produces a solution in polynomial time. From our experiments, we found out that the proposed heuristic algorithm provides a near-optimal solution for maximizing network lifetime in dense sensor networks.
MAXIMIZING NETWORK INTERRUPTION IN WIRELESS SENSOR NETWORK: AN INTRUDER’S PERSPECTIVE
Mohammad Mahfuzul Islam
2015-03-01
Full Text Available With the colossal growth of wireless sensor networks (WSNs in different applications starting from home automation to military affairs, the pressure on ensuring security in such a network is paramount. Considering the security challenges, it is really a hard-hitting effort to develop a secured WSN system. Moreover, as the information technology is getting popular, the intruders are also planning new ideas to break the system security, to harm the network and to make the system quality down with the target of taking the control of the network to corrupt it or to get benefits from it anyway. The intruders corrupt the system only when the security breaking cost (SBC is lower compared with the benefits they attained or the harm it can make to others. In this paper, the authors define the term “maximizing network interruption problem” and propose a technique, called the grid point approximation algorithm, to estimate the SBC of a multi-hop WSN so that it can be made tougher for an intruder to break the system security. It is assumed that the intruder has the complete picture of the entire network. The technique is designed from the intruder’s point of view for completely jamming all the sensor nodes in the network through placing jammers or malicious nodes strategically and at the same time keeping the number of jammer nodes to minimum or near minimum. To the best of the authors’ knowledge, there is no work proposed so far of the same kind. Experimental results with the changes of the different network parameters show that the proposed algorithm is able to provide excellent performances to achieve the targets.
Importance of randomness in biological networks: A random matrix analysis
Sarika Jalan
2015-02-01
Random matrix theory, initially proposed to understand the complex interactions in nuclear spectra, has demonstrated its success in diverse domains of science ranging from quantum chaos to galaxies. We demonstrate the applicability of random matrix theory for networks by providing a new dimension to complex systems research. We show that in spite of huge differences these interaction networks, representing real-world systems, posses from random matrix models, the spectral properties of the underlying matrices of these networks follow random matrix theory bringing them into the same universality class. We further demonstrate the importance of randomness in interactions for deducing crucial properties of the underlying system. This paper provides an overview of the importance of random matrix framework in complex systems research with biological systems as examples.
Throughput Maximization for Wireless Multimedia Sensor Networks in Industrial Applications
M.Markco
2014-06-01
Full Text Available n recent years, there has been growing interests in wireless sensor networks. Wireless sensor network is an autonomous system of sensor connected by wireless devices without any fixed infrastructure support. To meet the challenge paradigms of wireless sensor networks like Energy efficiency, Delay constraints, Reliability and adaptive mechanis m the sensor nodes are enhanced with multimedia support. The Wireless multimedia sensor nodes (WMSN enable to streamline the data that will control and monitor the industrial activities within the sensing area. The adaptive sleepless protocol will address the following issues: First, this protocol mainly designed for desired packet delivery and delay probabilities while reducing the energy consumption of the network. Second, this protocol is based on demand based dynamic sleep scheduling scheme for data communication. In this packets are transmitted through the cross layer interaction. In this cross layer interaction enables to reach a maximum efficiency.
Wireless multi-hop network scenario emulation by controlling maximal error
Huizhou ZHAO; Xiaoming LI; Wei YAN
2009-01-01
A fundamental problem about Ad-hoc network emulation is how to emulate a wireless multi-hop network scenario in a fixed testbed, which, however, is scarcely discussed in depth by existing radio frequency (RF)control wireless network emulators. Therefore, in this article, we first formulate the wireless multi-hop scenario emulation problem based on RF-control, take controlling the maximal error as the solution mentality, and present an algorithm called the matching exact-solution condition algorithm. The experiment results show that the algorithm can solve the wireless multi-hop network scenario emulation problem and maximal error can be achieved.
Maximizing the Effective Lifetime of Mobile Ad Hoc Networks
Hossain, M. Julius; Dewan, M. Ali Akber; Chae, Oksam
This paper presents a new routing approach to extend the effective lifetime of mobile ad hoc networks (MANET) considering both residual battery energy of the participating nodes and routing cost. As the nodes in ad hoc networks are limited in power, a power failure occurs if a node has insufficient remaining energy to send, receive or forward a message. So, it is important to minimize the energy expenditure as well as to balance the remaining battery power among the nodes. Cost effective routing algorithms attempt to minimize the total power needed to transmit a packet which causes a large number of nodes to loose energy quickly and die. On the other hand, lifetime prediction based routing algorithms try to balance the remaining energies among the nodes in the networks and ignore the transmission cost. These approaches extend the lifetime of first few individual nodes. But as nodes spend more energy for packet transfer, power failures occurs, within short interval resulting more number of total dead node earlier. This reduces the effective lifetime of the network, as at this stage successful communication is not possible due to the lack of forwarding node. The proposed method keeps the transmission power in modest range and at the same time tries to reduce the variance of the residual energy of the nodes more effectively to obtain the highest useful lifetime of the networks in the long run. Nonetheless, movement of nodes frequently creates network topology changes via link breaks and link creation and thus effects on the stability of the network. So, the pattern of the node movement is also incorporated in our route selection procedure.
Random matrix analysis of complex networks.
Jalan, Sarika; Bandyopadhyay, Jayendra N
2007-10-01
We study complex networks under random matrix theory (RMT) framework. Using nearest-neighbor and next-nearest-neighbor spacing distributions we analyze the eigenvalues of the adjacency matrix of various model networks, namely, random, scale-free, and small-world networks. These distributions follow the Gaussian orthogonal ensemble statistic of RMT. To probe long-range correlations in the eigenvalues we study spectral rigidity via the Delta_{3} statistic of RMT as well. It follows RMT prediction of linear behavior in semilogarithmic scale with the slope being approximately 1pi;{2} . Random and scale-free networks follow RMT prediction for very large scale. A small-world network follows it for sufficiently large scale, but much less than the random and scale-free networks.
Levy random walks on multiplex networks
Guo, Quantong; Zheng, Zhiming; Moreno, Yamir
2016-01-01
Random walks constitute a fundamental mechanism for many dynamics taking place on complex networks. Besides, as a more realistic description of our society, multiplex networks have been receiving a growing interest, as well as the dynamical processes that occur on top of them. Here, inspired by one specific model of random walks that seems to be ubiquitous across many scientific fields, the Levy flight, we study a new navigation strategy on top of multiplex networks. Capitalizing on spectral graph and stochastic matrix theories, we derive analytical expressions for the mean first passage time and the average time to reach a node on these networks. Moreover, we also explore the efficiency of Levy random walks, which we found to be very different as compared to the single layer scenario, accounting for the structure and dynamics inherent to the multiplex network. Finally, by comparing with some other important random walk processes defined on multiplex networks, we find that in some region of the parameters, a ...
Entropy Maximization in the Force Network Ensemble for Granular Solids
Tighe, B.P.; Van Eerd, A.R.T.; Vlugt, T.J.H.
2008-01-01
A long-standing issue in the area of granular media is the tail of the force distribution, in particular, whether this is exponential, Gaussian, or even some other form. Here we resolve the issue for the case of the force network ensemble in two dimensions. We demonstrate that conservation of the to
Maximizing lifetime of wireless sensor networks using genetic approach
Wagh, Sanjeev; Prasad, Ramjee
2014-01-01
the cluster head intelligently using auction data of node i.e. its local battery power, topology strength and external battery support. The network lifetime is the centre focus of the research paper which explores intelligently selection of cluster head using auction based approach. The multi......-objective parameters are considered to solve the problem using genetic algorithm of evolutionary approach....
Thermodynamics of random reaction networks.
Jakob Fischer
Full Text Available Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa -1.5 for linear and -1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks.
The Convergence Scheme on Network Utility Maximization in Wireless Multicast Networks
Y. Chen
2013-08-01
Full Text Available With the ever-increasing wireless data application recently, considerable efforts have been focused on the designof distributed explicit rate scheme based on Network Utility Maximization (NUM or wireless multi-hop meshnetworks. This paper describes a novel wireless multi-hop multicast flow control scheme for wireless meshnetworks via 802.11, which is based on the distributed self-turning Optimal Proportional plus Second-orderDifferential (OPSD controller. The control scheme, which is located at the sources in the wireless multicastnetworks, can ensure short convergence time by regulating the transmission rate. We further analyze thetheoretical aspects of the proposed algorithm. Simulation results demonstrate the efficiency of the proposedscheme in terms of fast response time, low packet loss and error ration.
Random vibrational networks and the renormalization group.
Hastings, M B
2003-04-11
We consider the properties of vibrational dynamics on random networks, with random masses and spring constants. The localization properties of the eigenstates contrast greatly with the Laplacian case on these networks. We introduce several real-space renormalization techniques which can be used to describe this dynamics on general networks, drawing on strong disorder techniques developed for regular lattices. The renormalization group is capable of elucidating the localization properties, and provides, even for specific network instances, a fast approximation technique for determining the spectra which compares well with exact results.
Autoregressive cascades on random networks
Iyer, Srikanth K.; Vaze, Rahul; Narasimha, Dheeraj
2016-04-01
A network cascade model that captures many real-life correlated node failures in large networks via load redistribution is studied. The considered model is well suited for networks where physical quantities are transmitted, e.g., studying large scale outages in electrical power grids, gridlocks in road networks, and connectivity breakdown in communication networks, etc. For this model, a phase transition is established, i.e., existence of critical thresholds above or below which a small number of node failures lead to a global cascade of network failures or not. Theoretical bounds are obtained for the phase transition on the critical capacity parameter that determines the threshold above and below which cascade appears or disappears, respectively, that are shown to closely follow numerical simulation results.
Wireless Network Security Using Randomness
2012-06-19
decision, unless so designated by other documentation. 12. DISTRIBUTION AVAILIBILITY STATEMENT Approved for public release; distribution is unlimited. UU...connectivity with the wireless network and the highly dynamic connections between nodes rule out the use of complex key distribution methods and make...Protected Access ( WPA and WPA2). WEP is a scheme used to secure IEEE 802.11 wireless networks, and is part of the IEEE 802.11 wireless networking standard
Structure and rigidity in maximally random jammed packings of hard particles
Atkinson, Steven Donald
Packings of hard particles have served as a powerful, yet simple model for a wide variety of physical systems. One particularly interesting subset of these packings are so-called maximally random jammed (MRJ) packings, which constitute the most disordered packings that exist subject to the constraint of jamming (mechanical stability). In this dissertation, we first investigate the consequences of recently-discovered sequential linear programming (SLP) techniques to present previously-unknown possibilities for MRJ packings of two- and three-dimensional hard disks and spheres, respectively. We then turn our focus away from the limit of jamming and identify some structural signatures accompanying various compression processes towards jammed states that are indicative of an incipient rigid structure. In Chapter 2, we utilize the Torquato-Jiao SLP algorithm to construct MRJ packings of equal-sized spheres in three dimensions that possess substantial qualitative differences from previous putative MRJ states. We turn towards two dimensions in Chapter 3 and establish the existence of highly disordered, jammed packings of equal-sized disks that were previously thought not to exist. We discuss the implications that these findings have for our understanding of disorder in packing problems. In Chapter 4, we utilize a novel SLP algorithm we call the "pop test" to scrutinize the conjectured link between jamming and hyperuniformity. We uncover deficiencies in standard protocols' abilities to construct truly-jammed states accompanied by a correlated deficiency in exact hyperuniform behavior, suggesting that precise jamming is a particularly subtle matter in probing this connection. In Chapter 5, we consider the direct correlation function as a means of identifying various static signatures of jamming as we compress packings towards both ordered and disordered jammed states with particular attention paid to the growing suppression of long-ranged density fluctuations
Topological properties of random wireless networks
Srikanth K Iyer; D Manjunath
2006-04-01
Wireless networks in which the node locations are random are best modelled as random geometric graphs (RGGs). In addition to their extensive application in the modelling of wireless networks, RGGs ﬁnd many new applications and are being studied in their own right. In this paper we ﬁrst provide a brief introduction to the issues of interest in random wireless networks. We then discuss some recent results for one-dimensional networks with the nodes distributed uniformly in $(0, z)$.We then discuss some asymptotic results for networks in higher dimensions when the nodes are distributed in a ﬁnite volume. Finally we discuss some recent generalisations in considering non uniform transmission ranges and non uniform node distributions. An annotated bibliography of some of the recent literature is also provided.
Statistical mechanics of fuzzy random polymer networks
陈晓红
1995-01-01
A statistical mechanics framework of fuzzy random polymer networks is established based on the theories of fuzzy systems. The entanglement effect is manifested quantitatively by introducing an entanglement tensor and membership function and the amorphous structure is treated as the fuzzy random network made up of macromolecular coils entangled randomly. A random tetrahedral entangled-crosslinked cell is chosen as an average representative unit of the fuzzy random polymer network structure. By making use of the theory of fuzzy probability and statistical mechanics, the expression for the free energy of deformation is given, which fits well with the experimental data on rubber elasticity under various deformation modes. Both classical statistical theory and Mooney-Rivlin equation can be taken as its special cases.
Navigability of interconnected networks under random failures
De Domenico, Manlio; Solé-Ribalta, Albert; Gómez, Sergio; Arenas, Alex
2014-01-01
Assessing the navigability of interconnected networks (transporting information, people, or goods) under eventual random failures is of utmost importance to design and protect critical infrastructures. Random walks are a good proxy to determine this navigability, specifically the coverage time of random walks, which is a measure of the dynamical functionality of the network. Here, we introduce the theoretical tools required to describe random walks in interconnected networks accounting for structure and dynamics inherent to real systems. We develop an analytical approach for the covering time of random walks in interconnected networks and compare it with extensive Monte Carlo simulations. Generally speaking, interconnected networks are more resilient to random failures than their individual layers per se, and we are able to quantify this effect. As an application––which we illustrate by considering the public transport of London––we show how the efficiency in exploring the multiplex critically depends on layers’ topology, interconnection strengths, and walk strategy. Our findings are corroborated by data-driven simulations, where the empirical distribution of check-ins and checks-out is considered and passengers travel along fastest paths in a network affected by real disruptions. These findings are fundamental for further development of searching and navigability strategies in real interconnected systems. PMID:24912174
2012-01-01
Background Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA. PMID:22462685
Dong Chao-Yi
2012-03-01
Full Text Available Abstract Background Synchronized bursting activity (SBA is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i In a network with an excitatory ratio (ER of 80-90%, its connective ratio (CR was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30% optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA.
Random walk centrality for temporal networks
Rocha, Luis Enrique Correa
2014-01-01
Nodes can be ranked according to their relative importance within the network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, as for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks which we call TempoRank. While in a static network, the stationary density of the random walk is proportional to the degree or the strength of a node, we find that in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network. The stationary density also depends on the sojourn probability q which regulates the tendency of the walker to stay in the node. We apply our method to human interaction networks and show that although it is important for a node ...
Connectivity of Random 1-Dimensional Networks
Kurlin, V.; Mihaylova, L.
2007-01-01
An important problem in wireless sensor networks is to find the minimal number of randomly deployed sensors making a network connected with a given probability. In practice sensors are often deployed one by one along a trajectory of a vehicle, so it is natural to assume that arbitrary probability density functions of distances between successive sensors in a segment are given. The paper computes the probability of connectivity and coverage of 1-dimensional networks and gives estimates for a m...
Maximizing Entropy of Pickard Random Fields for 2x2 Binary Constraints
Søgaard, Jacob; Forchhammer, Søren
2014-01-01
This paper considers the problem of maximizing the entropy of two-dimensional (2D) Pickard Random Fields (PRF) subject to constraints. We consider binary Pickard Random Fields, which provides a 2D causal finite context model and use it to define stationary probabilities for 2x2 squares, thus...... allowing us to calculate the entropy of the field. All possible binary 2x2 constraints are considered and all constraints are categorized into groups according to their properties. For constraints which can be modeled by a PRF approach and with positive entropy, we characterize and provide statistics...... of the maximum PRF entropy. As examples, we consider the well known hard square constraint along with a few other constraints....
Livi, Lorenzo; Alippi, Cesare
2016-01-01
It is a widely accepted fact that the computational capability of recurrent neural networks is maximized on the so-called "edge of criticality". Once in this configuration, the network performs efficiently on a specific application both in terms of (i) low prediction error and (ii) high short-term memory capacity. Since the behavior of recurrent networks is strongly influenced by the particular input signal driving the dynamics, a universal, application-independent method for determining the edge of criticality is still missing. In this paper, we propose a theoretically motivated method based on Fisher information for determining the edge of criticality in recurrent neural networks. It is proven that Fisher information is maximized for (finite-size) systems operating in such critical regions. However, Fisher information is notoriously difficult to compute and either requires the probability density function or the conditional dependence of the system states with respect to the model parameters. The paper expl...
Synchronization in random balanced networks
García del Molino, Luis Carlos; Pakdaman, Khashayar; Touboul, Jonathan; Wainrib, Gilles
2013-10-01
Characterizing the influence of network properties on the global emerging behavior of interacting elements constitutes a central question in many areas, from physical to social sciences. In this article we study a primary model of disordered neuronal networks with excitatory-inhibitory structure and balance constraints. We show how the interplay between structure and disorder in the connectivity leads to a universal transition from trivial to synchronized stationary or periodic states. This transition cannot be explained only through the analysis of the spectral density of the connectivity matrix. We provide a low-dimensional approximation that shows the role of both the structure and disorder in the dynamics.
How to Maximize User Satisfaction Degree in Multi-service IP Networks
Nguyen, Huy; Choi, Deokjai
2010-01-01
Bandwidth allocation is a fundamental problem in communication networks. With current network moving towards the Future Internet model, the problem is further intensified as network traffic demanding far from exceeds network bandwidth capability. Maintaining a certain user satisfaction degree therefore becomes a challenge research topic. In this paper, we deal with the problem by proposing BASMIN, a novel bandwidth allocation scheme that aims to maximize network user's happiness. We also defined a new metric for evaluating network user satisfaction degree: network worth. A three-step evaluation process is then conducted to compare BASMIN efficiency with other three popular bandwidth allocation schemes. Throughout the tests, we experienced BASMIN's advantages over the others; we even found out that one of the most widely used bandwidth allocation schemes, in fact, is not effective at all.
A. V. Semenets
2015-12-01
3 Research results. Data integration of the user profiles of the scientific social networksThe maximization of visibility and bibliometrics citation increasing of the scientific papers initiated by the given above approach is discussed. The detailed strategy of the user profiles bibliometrics data integration through the scientific social networks is proposed. The role and ways to receiving of the Altmetric rating indices are mentioned.
Interference Mitigation in Large Random Wireless Networks
Aldridge, Matthew
2011-01-01
A central problem in the operation of large wireless networks is how to deal with interference -- the unwanted signals being sent by transmitters that a receiver is not interested in. This thesis looks at ways of combating such interference. In Chapters 1 and 2, we outline the necessary information and communication theory background, including the concept of capacity. We also include an overview of a new set of schemes for dealing with interference known as interference alignment, paying special attention to a channel-state-based strategy called ergodic interference alignment. In Chapter 3, we consider the operation of large regular and random networks by treating interference as background noise. We consider the local performance of a single node, and the global performance of a very large network. In Chapter 4, we use ergodic interference alignment to derive the asymptotic sum-capacity of large random dense networks. These networks are derived from a physical model of node placement where signal strength d...
Random Boolean network models and the yeast transcriptional network
Kauffman, Stuart; Peterson, Carsten; Samuelsson, Björn; Troein, Carl
2003-12-01
The recently measured yeast transcriptional network is analyzed in terms of simplified Boolean network models, with the aim of determining feasible rule structures, given the requirement of stable solutions of the generated Boolean networks. We find that for ensembles of generated models, those with canalyzing Boolean rules are remarkably stable, whereas those with random Boolean rules are only marginally stable. Furthermore, substantial parts of the generated networks are frozen, in the sense that they reach the same state regardless of initial state. Thus, our ensemble approach suggests that the yeast network shows highly ordered dynamics.
Maximizing influence in a social network: Improved results using a genetic algorithm
Zhang, Kaiqi; Du, Haifeng; Feldman, Marcus W.
2017-07-01
The influence maximization problem focuses on finding a small subset of nodes in a social network that maximizes the spread of influence. While the greedy algorithm and some improvements to it have been applied to solve this problem, the long solution time remains a problem. Stochastic optimization algorithms, such as simulated annealing, are other choices for solving this problem, but they often become trapped in local optima. We propose a genetic algorithm to solve the influence maximization problem. Through multi-population competition, using this algorithm we achieve an optimal result while maintaining diversity of the solution. We tested our method with actual networks, and our genetic algorithm performed slightly worse than the greedy algorithm but better than other algorithms.
Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution
Lynn, Christopher
2016-01-01
The problem of influence maximization in social networks has typically been studied in the context of contagion models and irreversible processes. In this paper, we consider an alternate model that treats individual opinions as spins in an Ising network at dynamic equilibrium. We formalize the Ising influence maximization (IIM) problem, which has a physical interpretation as the maximization of the magnetization given a budget of external magnetic field. Under the mean-field (MF) approximation, we develop a number of sufficient conditions for when the problem is convex and exactly solvable, and we provide a gradient ascent algorithm that efficiently achieves an $\\epsilon$-approximation to the optimal solution. We show that optimal strategies exhibit a phase transition from focusing influence on high-degree individuals at high interaction strengths to spreading influence among low-degree individuals at low interaction strengths. We also establish a number of novel results about the structure of steady-states i...
A random network based, node attraction facilitated network evolution method
WenJun Zhang
2016-03-01
Full Text Available In present study, I present a method of network evolution that based on random network, and facilitated by node attraction. In this method, I assume that the initial network is a random network, or a given initial network. When a node is ready to connect, it tends to link to the node already owning the most connections, which coincides with the general rule (Barabasi and Albert, 1999 of node connecting. In addition, a node may randomly disconnect a connection i.e., the addition of connections in the network is accompanied by the pruning of some connections. The dynamics of network evolution is determined of the attraction factor Lamda of nodes, the probability of node connection, the probability of node disconnection, and the expected initial connectance. The attraction factor of nodes, the probability of node connection, and the probability of node disconnection are time and node varying. Various dynamics can be achieved by adjusting these parameters. Effects of simplified parameters on network evolution are analyzed. The changes of attraction factor Lamda can reflect various effects of the node degree on connection mechanism. Even the changes of Lamda only will generate various networks from the random to the complex. Therefore, the present algorithm can be treated as a general model for network evolution. Modeling results show that to generate a power-law type of network, the likelihood of a node attracting connections is dependent upon the power function of the node's degree with a higher-order power. Matlab codes for simplified version of the method are provided.
Scaling properties of multilayer random networks
Méndez-Bermúdez, J. A.; de Arruda, Guilherme Ferraz; Rodrigues, Francisco A.; Moreno, Yamir
2017-07-01
Multilayer networks are widespread in natural and manmade systems. Key properties of these networks are their spectral and eigenfunction characteristics, as they determine the critical properties of many dynamics occurring on top of them. Here, we numerically demonstrate that the normalized localization length β of the eigenfunctions of multilayer random networks follows a simple scaling law given by β =x*/(1 +x*) , with x*=γ (beff2/L ) δ , δ ˜1 , and beff being the effective bandwidth of the adjacency matrix of the network, whose size is L . The scaling law for β , that we validate on real-world networks, might help to better understand criticality in multilayer networks and to predict the eigenfunction localization properties of them.
Random graph models for dynamic networks
Zhang, Xiao; Newman, M E J
2016-01-01
We propose generalizations of a number of standard network models, including the classic random graph, the configuration model, and the stochastic block model, to the case of time-varying networks. We assume that the presence and absence of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. In addition to computing equilibrium properties of these models, we demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data. This allows us, for instance, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate our methods with a selection of applications, both to computer-generated test networks and real-world examples.
Optimal Operation of Network-Connected Combined Heat and Powers for Customer Profit Maximization
Da Xie
2016-06-01
Full Text Available Network-connected combined heat and powers (CHPs, owned by a community, can export surplus heat and electricity to corresponding heat and electric networks after community loads are satisfied. This paper proposes a new optimization model for network-connected CHP operation. Both CHPs’ overall efficiency and heat to electricity ratio (HTER are assumed to vary with loading levels. Based on different energy flow scenarios where heat and electricity are exported to the network from the community or imported, four profit models are established accordingly. They reflect the different relationships between CHP energy supply and community load demand across time. A discrete optimization model is then developed to maximize the profit for the community. The models are derived from the intervals determined by the daily operation modes of CHP and real-time buying and selling prices of heat, electricity and natural gas. By demonstrating the proposed models on a 1 MW network-connected CHP, results show that the community profits are maximized in energy markets. Thus, the proposed optimization approach can help customers to devise optimal CHP operating strategies for maximizing benefits.
An efficient algorithm for maximizing range sum queries in a road network.
Phan, Tien-Khoi; Jung, HaRim; Kim, Ung-Mo
2014-01-01
Given a set of positive-weighted points and a query rectangle r (specified by a client) of given extents, the goal of a maximizing range sum (MaxRS) query is to find the optimal location of r such that the total weights of all the points covered by r are maximized. All existing methods for processing MaxRS queries assume the Euclidean distance metric. In many location-based applications, however, the motion of a client may be constrained by an underlying (spatial) road network; that is, the client cannot move freely in space. This paper addresses the problem of processing MaxRS queries in a road network. We propose the external-memory algorithm that is suited for a large road network database. In addition, in contrast to the existing methods, which retrieve only one optimal location, our proposed algorithm retrieves all the possible optimal locations. Through simulations, we evaluate the performance of the proposed algorithm.
The Problem of Finding the Maximal Multiple Flow in the Divisible Network and its Special Cases
A. V. Smirnov
2015-01-01
Full Text Available In the article the problem of ﬁnding the maximal multiple ﬂow in the network of any natural multiplicity k is studied. There are arcs of three types: ordinary arcs, multiple arcs and multi-arcs. Each multiple and multi-arc is a union of k linked arcs, which are adjusted with each other. The network constructing rules are described. The deﬁnitions of a divisible network and some associated subjects are stated. The important property of the divisible network is that every divisible network can be partitioned into k parts, which are adjusted on the linked arcs of each multiple and multi-arc. Each part is the ordinary transportation network. The main results of the article are the following subclasses of the problem of ﬁnding the maximal multiple ﬂow in the divisible network. 1. The divisible networks with the multi-arc constraints. Assume that only one vertex is the ending vertex for a multi-arc in k −1 network parts. In this case the problem can be solved in a polynomial time. 2. The divisible networks with the weak multi-arc constraints. Assume that only one vertex is the ending vertex for a multi-arc in s network parts (1 ≤ s < k − 1 and other parts have at least two such vertices. In that case the multiplicity of the multiple ﬂow problem can be decreased to k − s. 3. The divisible network of the parallel structure. Assume that the divisible network component, which consists of all multiple arcs, can be partitioned into subcomponents, each of them containing exactly one vertex-beginning of a multi-arc. Suppose that intersection of each pair of subcomponents is the only vertex-network source x0. If k = 2, the maximal ﬂow problem can be solved in a polynomial time. If k ≥ 3, the problem is NP-complete. The algorithms for each polynomial subclass are suggested. Also, the multiplicity decreasing algorithm for the divisible network with weak multi-arc constraints is formulated.
Random walk centrality in interconnected multilayer networks
Solé-Ribalta, Albert; Gómez, Sergio; Arenas, Alex
2015-01-01
Real-world complex systems exhibit multiple levels of relationships. In many cases they require to be modeled as interconnected multilayer networks, characterizing interactions of several types simultaneously. It is of crucial importance in many fields, from economics to biology and from urban planning to social sciences, to identify the most (or the less) influential nodes in a network using centrality measures. However, defining the centrality of actors in interconnected complex networks is not trivial. In this paper, we rely on the tensorial formalism recently proposed to characterize and investigate this kind of complex topologies, and extend two well known random walk centrality measures, the random walk betweenness and closeness centrality, to interconnected multilayer networks. For each of the measures we provide analytical expressions that completely agree with numerically results.
UMR: A utility-maximizing routing algorithm for delay-sensitive service in LEO satellite networks
Lu Yong
2015-04-01
Full Text Available This paper develops a routing algorithm for delay-sensitive packet transmission in a low earth orbit multi-hop satellite network consists of micro-satellites. The micro-satellite low earth orbit (MS-LEO network endures unstable link connection and frequent link congestion due to the uneven user distribution and the link capacity variations. The proposed routing algorithm, referred to as the utility maximizing routing (UMR algorithm, improve the network utility of the MS-LEO network for carrying flows with strict end-to-end delay bound requirement. In UMR, first, a link state parameter is defined to capture the link reliability on continuing to keep the end-to-end delay into constraint; then, on the basis of this parameter, a routing metric is formulated and a routing scheme is designed for balancing the reliability in delay bound guarantee among paths and building a path maximizing the network utility expectation. While the UMR algorithm has many advantages, it may result in a higher blocking rate of new calls. This phenomenon is discussed and a weight factor is introduced into UMR to provide a flexible performance option for network operator. A set of simulations are conducted to verify the good performance of UMR, in terms of balancing the traffic distribution on inter-satellite links, reducing the flow interruption rate, and improving the network utility.
Alam, Muhammad Mahbub; Hamid, Md. Abdul; Razzaque, Md. Abdur; Hong, Choong Seon
Broadband wireless access networks are promising technology for providing better end user services. For such networks, designing a scheduling algorithm that fairly allocates the available bandwidth to the end users and maximizes the overall network throughput is a challenging task. In this paper, we develop a centralized fair scheduling algorithm for IEEE 802.16 mesh networks that exploits the spatio-temporal bandwidth reuse to further enhance the network throughput. The proposed mechanism reduces the length of a transmission round by increasing the number of non-contending links that can be scheduled simultaneously. We also propose a greedy algorithm that runs in polynomial time. Performance of the proposed algorithms is evaluated by extensive simulations. Results show that our algorithms achieve higher throughput than that of the existing ones and reduce the computational complexity.
Oliver, Jonathan M.; Almada, Anthony L.; Van Eck, Leighsa E.; Shah, Meena; Mitchell, Joel B.; Jones, Margaret T.; Jagim, Andrew R.; Rowlands, David S.
2016-01-01
Athletes in sports demanding repeat maximal work outputs frequently train concurrently utilizing sequential bouts of intense endurance and resistance training sessions. On a daily basis, maximal work within subsequent bouts may be limited by muscle glycogen availability. Recently, the ingestion of a unique high molecular weight (HMW) carbohydrate was found to increase glycogen re-synthesis rate and enhance work output during subsequent endurance exercise, relative to low molecular weight (LMW) carbohydrate ingestion. The effect of the HMW carbohydrate, however, on the performance of intense resistance exercise following prolonged-intense endurance training is unknown. Sixteen resistance trained men (23±3 years; 176.7±9.8 cm; 88.2±8.6 kg) participated in a double-blind, placebo-controlled, randomized 3-way crossover design comprising a muscle-glycogen depleting cycling exercise followed by ingestion of placebo (PLA), or 1.2 g•kg•bw-1 of LMW or HMW carbohydrate solution (10%) with blood sampling for 2-h post-ingestion. Thereafter, participants performed 5 sets of 10 maximal explosive repetitions of back squat (75% of 1RM). Compared to PLA, ingestion of HMW (4.9%, 90%CI 3.8%, 5.9%) and LMW (1.9%, 90%CI 0.8%, 3.0%) carbohydrate solutions substantially increased power output during resistance exercise, with the 3.1% (90% CI 4.3, 2.0%) almost certain additional gain in power after HMW-LMW ingestion attributed to higher movement velocity after force kinematic analysis (HMW-LMW 2.5%, 90%CI 1.4, 3.7%). Both carbohydrate solutions increased post-exercise plasma glucose, glucoregulatory and gut hormones compared to PLA, but differences between carbohydrates were unclear; thus, the underlying mechanism remains to be elucidated. Ingestion of a HMW carbohydrate following prolonged intense endurance exercise provides superior benefits to movement velocity and power output during subsequent repeated maximal explosive resistance exercise. This study was registered with
Wright, Marvin N; Dankowski, Theresa; Ziegler, Andreas
2017-04-15
The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random survival forests is the log-rank test statistic, which favors splitting variables with many possible split points. Conditional inference forests avoid this split variable selection bias. However, linear rank statistics are utilized by default in conditional inference forests to select the optimal splitting variable, which cannot detect non-linear effects in the independent variables. An alternative is to use maximally selected rank statistics for the split point selection. As in conditional inference forests, splitting variables are compared on the p-value scale. However, instead of the conditional Monte-Carlo approach used in conditional inference forests, p-value approximations are employed. We describe several p-value approximations and the implementation of the proposed random forest approach. A simulation study demonstrates that unbiased split variable selection is possible. However, there is a trade-off between unbiased split variable selection and runtime. In benchmark studies of prediction performance on simulated and real datasets, the new method performs better than random survival forests if informative dichotomous variables are combined with uninformative variables with more categories and better than conditional inference forests if non-linear covariate effects are included. In a runtime comparison, the method proves to be computationally faster than both alternatives, if a simple p-value approximation is used. Copyright © 2017 John Wiley & Sons, Ltd.
Scaling Properties of Multilayer Random Networks
Méndez-Bermúdez, J A; Rodrigues, Francisco A; Moreno, Yamir
2016-01-01
Multilayer networks are widespread in natural and manmade systems. Key properties of these networks are their spectral and eigenfunction characteristics, as they determine the critical properties of many dynamics occurring on top of them. In this paper, we numerically demonstrate that the normalized localization length $\\beta$ of the eigenfunctions of multilayer random networks follows a simple scaling law given by $\\beta=x^*/(1+x^*)$, with $x^*=\\gamma(b_{\\text{eff}}^2/L)^\\delta$, $\\gamma,\\delta\\sim 1$ and $b_{\\text{eff}}$ being the effective bandwidth of the adjacency matrix of the network, whose size is $L=M\\times N$. The reported scaling law for $\\beta$ might help to better understand criticality in multilayer networks as well as to predict the eigenfunction localization properties of them.
Queuing delays in randomized load balanced networks
Borst, S.C.; et al, not CWI
2007-01-01
Valiant’s concept of Randomized Load Balancing (RLB), also promoted under the name ‘two-phase routing’, has previously been shown to provide a cost-effective way of implementing overlay networks that are robust to dynamically changing demand patterns. RLB is accomplished in two steps; in the first s
Coevolution of quantum and classical strategies on evolving random networks.
Qiang Li
Full Text Available We study the coevolution of quantum and classical strategies on weighted and directed random networks in the realm of the prisoner's dilemma game. During the evolution, agents can break and rewire their links with the aim of maximizing payoffs, and they can also adjust the weights to indicate preferences, either positive or negative, towards their neighbors. The network structure itself is thus also subject to evolution. Importantly, the directionality of links does not affect the accumulation of payoffs nor the strategy transfers, but serves only to designate the owner of each particular link and with it the right to adjust the link as needed. We show that quantum strategies outperform classical strategies, and that the critical temptation to defect at which cooperative behavior can be maintained rises, if the network structure is updated frequently. Punishing neighbors by reducing the weights of their links also plays an important role in maintaining cooperation under adverse conditions. We find that the self-organization of the initially random network structure, driven by the evolutionary competition between quantum and classical strategies, leads to the spontaneous emergence of small average path length and a large clustering coefficient.
Existence of isostatic, maximally random jammed monodisperse hard-disk packings.
Atkinson, Steven; Stillinger, Frank H; Torquato, Salvatore
2014-12-30
We generate jammed packings of monodisperse circular hard-disks in two dimensions using the Torquato-Jiao sequential linear programming algorithm. The packings display a wide diversity of packing fractions, average coordination numbers, and order as measured by standard scalar order metrics. This geometric-structure approach enables us to show the existence of relatively large maximally random jammed (MRJ) packings with exactly isostatic jammed backbones and a packing fraction (including rattlers) of [Formula: see text]. By contrast, the concept of random close packing (RCP) that identifies the most probable packings as the most disordered misleadingly identifies highly ordered disk packings as RCP in 2D. Fundamental structural descriptors such as the pair correlation function, structure factor, and Voronoi statistics show a strong contrast between the MRJ state and the typical hyperstatic, polycrystalline packings with [Formula: see text] that are more commonly obtained using standard packing protocols. Establishing that the MRJ state for monodisperse hard disks is isostatic and qualitatively distinct from commonly observed polycrystalline packings contradicts conventional wisdom that such a disordered, isostatic packing does not exist due to a lack of geometrical frustration and sheds light on the nature of disorder. This prompts the question of whether an algorithm may be designed that is strongly biased toward generating the monodisperse disk MRJ state.
Generalized Encoding CRDSA: Maximizing Throughput in Enhanced Random Access Schemes for Satellite
Manlio Bacco
2014-12-01
Full Text Available This work starts from the analysis of the literature about the Random Access protocols with contention resolution, such as Contention Resolution Diversity Slotted Aloha (CRDSA, and introduces a possible enhancement, named Generalized Encoding Contention Resolution Diversity Slotted Aloha (GE-CRDSA. The GE-CRDSA aims at improving the aggregated throughput when the system load is less than 50%, playing on the opportunity of transmitting an optimal combination of information and parity packets frame by frame. This paper shows the improvement in terms of throughput, by performing traffic estimation and adaptive choice of information and parity rates, when a satellite network undergoes a variable traffic load profile.
Scale-Invariant Random Spatial Networks
Aldous, David J
2012-01-01
Real-world road networks have an approximate scale-invariance property; can one devise mathematical models of random networks whose distributions are {\\em exactly} invariant under Euclidean scaling? This requires working in the continuum plane. We introduce an axiomatization of a class of processes we call {\\em scale-invariant random spatial networks}, whose primitives are routes between each pair of points in the plane. We prove that one concrete model, based on minimum-time routes in a binary hierarchy of roads with different speed limits, satisfies the axioms, and note informally that two other constructions (based on Poisson line processes and on dynamic proximity graphs) are expected also to satisfy the axioms. We initiate study of structure theory and summary statistics for general processes in this class.
How Clustering Affects Epidemics in Random Networks
Coupechoux, Emilie
2012-01-01
Motivated by the analysis of social networks, we study a model of random networks that has both a given degree distribution and a tunable clustering coefficient. We consider two types of growth processes on these graphs: diffusion and symmetric threshold model. The diffusion process is inspired from epidemic models. It is characterized by an infection probability, each neighbor transmitting the epidemic independently. In the symmetric threshold process, the interactions are still local but the propagation rule is governed by a threshold (that might vary among the different nodes). An interesting example of symmetric threshold process is the contagion process, which is inspired by a simple coordination game played on the network. Both types of processes have been used to model spread of new ideas, technologies, viruses or worms and results have been obtained for random graphs with no clustering. In this paper, we are able to analyze the impact of clustering on the growth processes. While clustering inhibits th...
Luo, Hanjiang; Guo, Zhongwen; Wu, Kaishun; Hong, Feng; Feng, Yuan
2009-01-01
Underwater acoustic sensor networks (UWA-SNs) are envisioned to perform monitoring tasks over the large portion of the world covered by oceans. Due to economics and the large area of the ocean, UWA-SNs are mainly sparsely deployed networks nowadays. The limited battery resources is a big challenge for the deployment of such long-term sensor networks. Unbalanced battery energy consumption will lead to early energy depletion of nodes, which partitions the whole networks and impairs the integrity of the monitoring datasets or even results in the collapse of the entire networks. On the contrary, balanced energy dissipation of nodes can prolong the lifetime of such networks. In this paper, we focus on the energy balance dissipation problem of two types of sparsely deployed UWA-SNs: underwater moored monitoring systems and sparsely deployed two-dimensional UWA-SNs. We first analyze the reasons of unbalanced energy consumption in such networks, then we propose two energy balanced strategies to maximize the lifetime of networks both in shallow and deep water. Finally, we evaluate our methods by simulations and the results show that the two strategies can achieve balanced energy consumption per node while at the same time prolong the networks lifetime.
Hanjiang Luo
2009-08-01
Full Text Available Underwater acoustic sensor networks (UWA-SNs are envisioned to perform monitoring tasks over the large portion of the world covered by oceans. Due to economics and the large area of the ocean, UWA-SNs are mainly sparsely deployed networks nowadays. The limited battery resources is a big challenge for the deployment of such long-term sensor networks. Unbalanced battery energy consumption will lead to early energy depletion of nodes, which partitions the whole networks and impairs the integrity of the monitoring datasets or even results in the collapse of the entire networks. On the contrary, balanced energy dissipation of nodes can prolong the lifetime of such networks. In this paper, we focus on the energy balance dissipation problem of two types of sparsely deployed UWA-SNs: underwater moored monitoring systems and sparsely deployed two-dimensional UWA-SNs. We first analyze the reasons of unbalanced energy consumption in such networks, then we propose two energy balanced strategies to maximize the lifetime of networks both in shallow and deep water. Finally, we evaluate our methods by simulations and the results show that the two strategies can achieve balanced energy consumption per node while at the same time prolong the networks lifetime.
Maximizing Interconnectedness and Availability in Directional Airborne RangeExtension Networks
2017-10-25
aerial electronics showing potential antenna placements concept for such a pod design with potential antenna place- ments on the ends , sides, and bottom...pod and to use the sides and /or ends of the pod for antennas intended to connect to other aerial nodes. However, there are important scenarios where...Maximizing Interconnectedness and Availability in Directional Airborne Range Extension Networks Thomas Shake, Rahul Amin MIT Lincoln Laboratory
Fuzzy neural network output maximization control for sensorless wind energy conversion system
Lin, Whei-Min; Hong, Chih-Ming [Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung (China); Cheng, Fu-Sheng [Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung (China)
2010-02-15
This paper presents the design of an online training fuzzy neural network (FNN) controller with a high-performance speed observer for the induction generator (IG). The proposed output maximization control is achieved without mechanical sensors such as the wind speed or position sensor, and the new control system will deliver maximum electric power with light weight, high efficiency, and high reliability. The estimation of the rotor speed is designed on the basis of the sliding mode control theory. (author)
Maximization of Energy Efficiency in Wireless ad hoc and Sensor Networks With SERENA
Saoucene Mahfoudh
2009-01-01
Full Text Available In wireless ad hoc and sensor networks, an analysis of the node energy consumption distribution shows that the largest part is due to the time spent in the idle state. This result is at the origin of SERENA, an algorithm to SchEdule RoutEr Nodes Activity. SERENA allows router nodes to sleep, while ensuring end-to-end communication in the wireless network. It is a localized and decentralized algorithm assigning time slots to nodes. Any node stays awake only during its slot and the slots assigned to its neighbors, it sleeps the remaining time. Simulation results show that SERENA enables us to maximize network lifetime while increasing the number of user messages delivered. SERENA is based on a two-hop coloring algorithm, whose complexity in terms of colors and rounds is evaluated. We then quantify the slot reuse. Finally, we show how SERENA improves the node energy consumption distribution and maximizes the energy efficiency of wireless ad hoc and sensor networks. We compare SERENA with classical TDMA and optimized variants such as USAP in wireless ad hoc and sensor networks.
Unraveling spurious properties of interaction networks with tailored random networks.
Stephan Bialonski
Full Text Available We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.
Unraveling spurious properties of interaction networks with tailored random networks.
Bialonski, Stephan; Wendler, Martin; Lehnertz, Klaus
2011-01-01
We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.
Kerner, Boris S.
2016-09-01
We have revealed general physical conditions for the maximization of the network throughput at which free flow conditions are ensured, i.e., traffic breakdown cannot occur in the whole traffic or transportation network. A physical measure of the network - network capacity is introduced that characterizes general features of the network with respect to the maximization of the network throughput. The network capacity allows us also to make a general proof of the deterioration of traffic system occurring when dynamic traffic assignment is performed in a network based on the classical Wardrop' user equilibrium (UE) and system optimum (SO) equilibrium.
SHEN Zhigang; HE Ning; LI Liang
2009-01-01
In metal cutting industry it is a common practice to search for optimal combination of cutting parameters in order to maximize the tool life for a fixed minimum value of material removal rate(MRR). After the advent of high-speed milling(HSM) pro cess, lots of experimental and theoretical researches have been done for this purpose which mainly emphasized on the optimization of the cutting parameters. It is highly beneficial to convert raw data into a comprehensive knowledge-based expert system using fuzzy logic as the reasoning mechanism. In this paper an attempt has been presented for the extraction of the rules from fuzzy neural network(FNN) so as to have the most effective knowledge-base for given set of data. Experiments were conducted to determine the best values of cutting speeds that can maximize tool life for different combinations of input parameters. A fuzzy neural network was constructed based on the fuzzification of input parameters and the cutting speed. After training process, raw rule sets were extracted and a rule pruning approach was proposed to obtain concise linguistic rules. The estimation process with fuzzy inference showed that the optimized combination of fuzzy rules provided the estimation error of only 6.34 m/min as compared to 314 m/min of that of randomized combination of rules.
Liu Yang
2015-12-01
Full Text Available Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs in the network act as routers to transmit data to base station (BS cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.
Yang, Liu; Lu, Yinzhi; Zhong, Yuanchang; Wu, Xuegang; Yang, Simon X
2015-12-26
Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs) because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC) algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs) in the network act as routers to transmit data to base station (BS) cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.
Sensor Networks with Random Links: Topology Design for Distributed Consensus
Kar, Soummya
2007-01-01
In a sensor network, in practice, the communication among sensors is subject to:(1) errors or failures at random times; (3) costs; and(2) constraints since sensors and networks operate under scarce resources, such as power, data rate, or communication. The signal-to-noise ratio (SNR) is usually a main factor in determining the probability of error (or of communication failure) in a link. These probabilities are then a proxy for the SNR under which the links operate. The paper studies the problem of designing the topology, i.e., assigning the probabilities of reliable communication among sensors (or of link failures) to maximize the rate of convergence of average consensus, when the link communication costs are taken into account, and there is an overall communication budget constraint. To consider this problem, we address a number of preliminary issues: (1) model the network as a random topology; (2) establish necessary and sufficient conditions for mean square sense (mss) and almost sure (a.s.) convergence o...
Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks
Zahid Wadud
2017-07-01
Full Text Available In Internet of Things (IoT enabled Wireless Sensor Networks (WSNs, there are two major factors which degrade the performance of the network. One is the void hole which occurs in a particular region due to unavailability of forwarder nodes. The other is the presence of energy hole which occurs due to imbalanced data traffic load on intermediate nodes. Therefore, an optimum transmission strategy is required to maximize the network lifespan via hole alleviation. In this regard, we propose a heterogeneous network solution that is capable to balance energy dissipation among network nodes. In addition, the divide and conquer approach is exploited to evenly distribute number of transmissions over various network areas. An efficient forwarder node selection is performed to alleviate coverage and energy holes. Linear optimization is performed to validate the effectiveness of our proposed work in term of energy minimization. Furthermore, simulations are conducted to show that our claims are well grounded. Results show the superiority of our work as compared to the baseline scheme in terms of energy consumption and network lifetime.
Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks.
Wadud, Zahid; Javaid, Nadeem; Khan, Muhammad Awais; Alrajeh, Nabil; Alabed, Mohamad Souheil; Guizani, Nadra
2017-07-21
In Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs), there are two major factors which degrade the performance of the network. One is the void hole which occurs in a particular region due to unavailability of forwarder nodes. The other is the presence of energy hole which occurs due to imbalanced data traffic load on intermediate nodes. Therefore, an optimum transmission strategy is required to maximize the network lifespan via hole alleviation. In this regard, we propose a heterogeneous network solution that is capable to balance energy dissipation among network nodes. In addition, the divide and conquer approach is exploited to evenly distribute number of transmissions over various network areas. An efficient forwarder node selection is performed to alleviate coverage and energy holes. Linear optimization is performed to validate the effectiveness of our proposed work in term of energy minimization. Furthermore, simulations are conducted to show that our claims are well grounded. Results show the superiority of our work as compared to the baseline scheme in terms of energy consumption and network lifetime.
On Maximizing the Lifetime of Wireless Sensor Networks by Optimally Assigning Energy Supplies
Francisco Javier González-Castano
2013-08-01
Full Text Available The extension of the network lifetime of Wireless Sensor Networks (WSN is an important issue that has not been appropriately solved yet. This paper addresses this concern and proposes some techniques to plan an arbitrary WSN. To this end, we suggest a hierarchical network architecture, similar to realistic scenarios, where nodes with renewable energy sources (denoted as primary nodes carry out most message delivery tasks, and nodes equipped with conventional chemical batteries (denoted as secondary nodes are those with less communication demands. The key design issue of this network architecture is the development of a new optimization framework to calculate the optimal assignment of renewable energy supplies (primary node assignment to maximize network lifetime, obtaining the minimum number of energy supplies and their node assignment. We also conduct a second optimization step to additionally minimize the number of packet hops between the source and the sink. In this work, we present an algorithm that approaches the results of the optimization framework, but with much faster execution speed, which is a good alternative for large-scale WSN networks. Finally, the network model, the optimization process and the designed algorithm are further evaluated and validated by means of computer simulation under realistic conditions. The results obtained are discussed comparatively.
Holographic duality from random tensor networks
Hayden, Patrick; Qi, Xiao-Liang; Thomas, Nathaniel; Walter, Michael; Yang, Zhao
2016-01-01
Tensor networks provide a natural framework for exploring holographic duality because they obey entanglement area laws. They have been used to construct explicit simple models realizing many of the interesting structural features of the AdS/CFT correspondence, including the non-uniqueness of bulk operator reconstruction in the boundary theory. In this article, we explore the holographic properties of networks of random tensors. We find that our models obey the Ryu-Takayanagi entropy formula for all boundary regions, whether connected or not, a fact closely related to known properties of the multipartite entanglement of assistance. Moreover, we find that all boundary regions faithfully encode the physics of their entire bulk entanglement wedges, not just their smaller causal wedges. Our method is to interpret the average over random tensors as the partition function of a classical ferromagnetic Ising model, so that the minimal surfaces of Ryu-Takayanagi appear as domain walls. Upon including the analog of a bu...
Tao, R.; Tang, H.
Chocolate is one of the most popular food types and flavors in the world. Unfortunately, at present, chocolate products contain too much fat, leading to obesity. For example, a typical molding chocolate has various fat up to 40% in total and chocolate for covering ice cream has fat 50 -60%. Especially, as children are the leading chocolate consumers, reducing the fat level in chocolate products to make them healthier is important and urgent. While this issue was called into attention and elaborated in articles and books decades ago and led to some patent applications, no actual solution was found unfortunately. Why is reducing fat in chocolate so difficult? What is the underlying physical mechanism? We have found that this issue is deeply related to the basic science of soft matters, especially to their viscosity and maximally random jammed (MRJ) density φx. All chocolate productions are handling liquid chocolate, a suspension with cocoa solid particles in melted fat, mainly cocoa butter. The fat level cannot be lower than 1-φxin order to have liquid chocolate to flow. Here we show that that with application of an electric field to liquid chocolate, we can aggregate the suspended particles into prolate spheroids. This microstructure change reduces liquid chocolate's viscosity along the flow direction and increases its MRJ density significantly. Hence the fat level in chocolate can be effectively reduced. We are looking forward to a new class of healthier and tasteful chocolate coming to the market soon. Dept. of Physics, Temple Univ, Philadelphia, PA 19122.
Quantum games on evolving random networks
Pawela, Łukasz
2016-09-01
We study the advantages of quantum strategies in evolutionary social dilemmas on evolving random networks. We focus our study on the two-player games: prisoner's dilemma, snowdrift and stag-hunt games. The obtained result show the benefits of quantum strategies for the prisoner's dilemma game. For the other two games, we obtain regions of parameters where the quantum strategies dominate, as well as regions where the classical strategies coexist.
Quantum games on evolving random networks
Pawela, Łukasz
2015-01-01
We study the advantages of quantum strategies in evolutionary social dilemmas on evolving random networks. We focus our study on the two-player games: prisoner's dilemma, snowdrift and stag-hunt games. The obtained result show the benefits of quantum strategies for the prisoner's dilemma game. For the other two games, we obtain regions of parameters where the quantum strategies dominate, as well as regions where the classical strategies coexist.
Opportunistic Interference Alignment for Random Access Networks
Jin, Hu; Jeon, Sang-Woon; Jung, Bang Chul
2015-01-01
An interference management problem among multiple overlapped random access networks (RANs) is investigated, each of which operates with slotted ALOHA protocol. Assuming that access points and users have multiple antennas, a novel opportunistic interference alignment~(OIA) is proposed to mitigate interference among overlapped RANs. The proposed technique intelligently combines the transmit beamforming technique at the physical layer and the opportunistic packet transmission at the medium acces...
Topics in networks: Community detection, random graphs, and network epidemiology
Karrer, Brian C.
In this dissertation, we present research on several topics in networks including community detection, random graphs, and network epidemiology. Traditional stochastic blockmodels may produce inaccurate fits to complex networks with heterogeneous degree distributions and we devise a degree-corrected block-model that alleviates this problematic behavior. The resulting objective function for community detection using the degree-corrected version outperforms the traditional model at finding communities on a variety of real-world and synthetic tests. Then we study a different generative model that associates communities to the edges of the network and naturally includes overlapping vertex communities. We create a fast and accurate algorithm to fit this model to empirical networks and show that it can be used to quickly find non-overlapping communities as well. We also develop random graph models for directed acyclic graphs, a class of networks including family trees and citation networks. We argue that the lack of cycles comes from an ordering constraint and then generalize the configuration model to incorporate this constraint. We calculate many properties of these models and demonstrate that sonic of the model predictions agree quite well with real-world networks, emphasizing the importance of vertex ordering to generating directed acyclic networks with realistic properties. Finally, we examine the spread of disease over networks, starting with a simple model of two diseases spreading with cross-immunity, where infection by one disease makes an individual immune to the other disease and vice versa. Utilizing a timescale separation argument, we map the system to consecutive bond percolation, one disease spreading after the other. The resulting phase diagram includes discontinuous and continuous phase transitions and a coexistence region where both diseases can spread to a substantial fraction of the population. Then we analyze a flexible susceptible
Symmetry in critical random Boolean network dynamics
Hossein, Shabnam; Reichl, Matthew D.; Bassler, Kevin E.
2014-04-01
Using Boolean networks as prototypical examples, the role of symmetry in the dynamics of heterogeneous complex systems is explored. We show that symmetry of the dynamics, especially in critical states, is a controlling feature that can be used both to greatly simplify analysis and to characterize different types of dynamics. Symmetry in Boolean networks is found by determining the frequency at which the various Boolean output functions occur. There are classes of functions that consist of Boolean functions that behave similarly. These classes are orbits of the controlling symmetry group. We find that the symmetry that controls the critical random Boolean networks is expressed through the frequency by which output functions are utilized by nodes that remain active on dynamical attractors. This symmetry preserves canalization, a form of network robustness. We compare it to a different symmetry known to control the dynamics of an evolutionary process that allows Boolean networks to organize into a critical state. Our results demonstrate the usefulness and power of using the symmetry of the behavior of the nodes to characterize complex network dynamics, and introduce an alternative approach to the analysis of heterogeneous complex systems.
Symmetry in Critical Random Boolean Networks Dynamics
Bassler, Kevin E.; Hossein, Shabnam
2014-03-01
Using Boolean networks as prototypical examples, the role of symmetry in the dynamics of heterogeneous complex systems is explored. We show that symmetry of the dynamics, especially in critical states, is a controlling feature that can be used to both greatly simplify analysis and to characterize different types of dynamics. Symmetry in Boolean networks is found by determining the frequency at which the various Boolean output functions occur. Classes of functions occur at the same frequency. These classes are orbits of the controlling symmetry group. We find the nature of the symmetry that controls the dynamics of critical random Boolean networks by determining the frequency of output functions utilized by nodes that remain active on dynamical attractors. This symmetry preserves canalization, a form of network robustness. We compare it to a different symmetry known to control the dynamics of an evolutionary process that allows Boolean networks to organize into a critical state. Our results demonstrate the usefulness and power of using symmetry to characterize complex network dynamics, and introduce a novel approach to the analysis of heterogeneous complex systems. This work was supported by the NSF through grants DMR-0908286 and DMR-1206839, and by the AFSOR and DARPA through grant FA9550-12-1-0405.
Symmetry in critical random Boolean network dynamics.
Hossein, Shabnam; Reichl, Matthew D; Bassler, Kevin E
2014-04-01
Using Boolean networks as prototypical examples, the role of symmetry in the dynamics of heterogeneous complex systems is explored. We show that symmetry of the dynamics, especially in critical states, is a controlling feature that can be used both to greatly simplify analysis and to characterize different types of dynamics. Symmetry in Boolean networks is found by determining the frequency at which the various Boolean output functions occur. There are classes of functions that consist of Boolean functions that behave similarly. These classes are orbits of the controlling symmetry group. We find that the symmetry that controls the critical random Boolean networks is expressed through the frequency by which output functions are utilized by nodes that remain active on dynamical attractors. This symmetry preserves canalization, a form of network robustness. We compare it to a different symmetry known to control the dynamics of an evolutionary process that allows Boolean networks to organize into a critical state. Our results demonstrate the usefulness and power of using the symmetry of the behavior of the nodes to characterize complex network dynamics, and introduce an alternative approach to the analysis of heterogeneous complex systems.
Unraveling Spurious Properties of Interaction Networks with Tailored Random Networks
Bialonski, Stephan; Lehnertz, Klaus; 10.1371/journal.pone.0022826
2012-01-01
We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erd\\H{o}s-R\\'{e}nyi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way i...
Diffusion and Cascading Behavior in Random Networks
Lelarge, Marc
2010-01-01
The spread of new ideas, behaviors or technologies has been extensively studied using epidemic models. Here we consider a model of diffusion where the individuals' behavior is the result of a strategic choice. We study a simple coordination game with binary choice and give a condition for a new action to become widespread in a random network. We also analyze the possible equilibria of this game and identify conditions for the coexistence of both strategies in large connected sets. Finally we look at how can firms use social networks to promote their goals with limited information. Our results differ strongly from the one derived with epidemic models. In particular, we show that connectivity plays an ambiguous role: while it allows the diffusion to spread, when the network is highly connected, the diffusion is also limited by high-degree nodes which are very stable. In the case of a sparse random network of interacting agents, we compute the contagion threshold for a general diffusion model and show the existe...
Epidemic spreading in random rectangular networks
Estrada, Ernesto; Meloni, Sandro; Sheerin, Matthew; Moreno, Yamir
2016-11-01
The use of network theory to model disease propagation on populations introduces important elements of reality to the classical epidemiological models. The use of random geometric graphs (RGGs) is one of such network models that allows for the consideration of spatial properties on disease propagation. In certain real-world scenarios—like in the analysis of a disease propagating through plants—the shape of the plots and fields where the host of the disease is located may play a fundamental role in the propagation dynamics. Here we consider a generalization of the RGG to account for the variation of the shape of the plots or fields where the hosts of a disease are allocated. We consider a disease propagation taking place on the nodes of a random rectangular graph and we consider a lower bound for the epidemic threshold of a susceptible-infected-susceptible model or a susceptible-infected-recovered model on these networks. Using extensive numerical simulations and based on our analytical results we conclude that (ceteris paribus) the elongation of the plot or field in which the nodes are distributed makes the network more resilient to the propagation of a disease due to the fact that the epidemic threshold increases with the elongation of the rectangle. These results agree with accumulated empirical evidence and simulation results about the propagation of diseases on plants in plots or fields of the same area and different shapes.
A Random Laser as a Dynamical Network
Höfner, M; Henneberger, F
2013-01-01
The mode dynamics of a random laser is investigated in experiment and theory. The laser consists of a ZnCdO/ZnO multiple quantum well with air-holes that provide the necessary feedback. Time-resolved measurements reveal multimode spectra with individually developing features but no variation from shot to shot. These findings are qualitatively reproduced with a model that exploits the specifics of a dilute system of weak scatterers and can be interpreted in terms of a lasing network. Introducing the phase-sensitive node coherence reveals new aspects of the self-organization of the laser field. Lasing is carried by connected links between a subset of scatterers, the fields on which are oscillating coherently in phase. In addition, perturbing feedback with possibly unfitting phases from frustrated other scatterers is suppressed by destructive superposition. We believe that our findings are representative at least for weakly scattering random lasers. A generalization to random laser with dense and strong scattere...
Maximal entropy coverings and the information dimension of a complex network
Rosenberg, Eric
2017-02-01
Computing the information dimension dI of a complex network G requires covering G by a minimal collection of "boxes" of size s to obtain a set of probabilities, computing the entropy H (s), and quantifying how H (s) scales with log s. We show that to determine whether dI ≤dB holds for G, where dB is the box counting dimension, it is not sufficient to determine a minimal covering for each s. We introduce the new notion of a maximal entropy minimal covering of G, and a corresponding new definition of dI. The use of maximal entropy minimal coverings in many cases enhances the ability to compute dI.
A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks
Bhaduri, Kanishka; Srivastava, Ashok N.
2009-01-01
This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining tasks in a distributed environment such as clustering, anomaly detection, target tracking to name a few. This technology is crucial for many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Centralizing all or some of the data for building global models is impractical in such peer-to-peer environments because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. The distributed algorithm we have developed in this paper is provably-correct i.e. it converges to the same result compared to a similar centralized algorithm and can automatically adapt to changes to the data and the network. We show that the communication overhead of the algorithm is very low due to its local nature. This monitoring algorithm is then used as a feedback loop to sample data from the network and rebuild the model when it is outdated. We present thorough experimental results to verify our theoretical claims.
An Efficient Algorithm for Maximizing Range Sum Queries in a Road Network
Tien-Khoi Phan
2014-01-01
Full Text Available Given a set of positive-weighted points and a query rectangle r (specified by a client of given extents, the goal of a maximizing range sum (MaxRS query is to find the optimal location of r such that the total weights of all the points covered by r are maximized. All existing methods for processing MaxRS queries assume the Euclidean distance metric. In many location-based applications, however, the motion of a client may be constrained by an underlying (spatial road network; that is, the client cannot move freely in space. This paper addresses the problem of processing MaxRS queries in a road network. We propose the external-memory algorithm that is suited for a large road network database. In addition, in contrast to the existing methods, which retrieve only one optimal location, our proposed algorithm retrieves all the possible optimal locations. Through simulations, we evaluate the performance of the proposed algorithm.
Random walks in directed modular networks
Comin, Cesar H.; Viana, Mateus P.; Antiqueira, Lucas; Costa, Luciano da F.
2014-12-01
Because diffusion typically involves symmetric interactions, scant attention has been focused on studying asymmetric cases. However, important networked systems underlain by diffusion (e.g. cortical networks and WWW) are inherently directed. In the case of undirected diffusion, it can be shown that the steady-state probability of the random walk dynamics is fully correlated with the degree, which no longer holds for directed networks. We investigate the relationship between such probability and the inward node degree, which we call efficiency, in modular networks. Our findings show that the efficiency of a given community depends mostly on the balance between its ingoing and outgoing connections. In addition, we derive analytical expressions to show that the internal degree of the nodes does not play a crucial role in their efficiency, when considering the Erdős-Rényi and Barabási-Albert models. The results are illustrated with respect to the macaque cortical network, providing subsidies for improving transportation and communication systems.
Klatt, Michael A.; Torquato, Salvatore
2016-08-01
In the first paper of this series, we introduced Voronoi correlation functions to characterize the structure of maximally random jammed (MRJ) sphere packings across length scales. In the present paper, we determine a variety of different correlation functions that arise in rigorous expressions for the effective physical properties of MRJ sphere packings and compare them to the corresponding statistical descriptors for overlapping spheres and equilibrium hard-sphere systems. Such structural descriptors arise in rigorous bounds and formulas for effective transport properties, diffusion and reactions constants, elastic moduli, and electromagnetic characteristics. First, we calculate the two-point, surface-void, and surface-surface correlation functions, for which we derive explicit analytical formulas for finite hard-sphere packings. We show analytically how the contact Dirac delta function contribution to the pair correlation function g2(r ) for MRJ packings translates into distinct functional behaviors of these two-point correlation functions that do not arise in the other two models examined here. Then we show how the spectral density distinguishes the MRJ packings from the other disordered systems in that the spectral density vanishes in the limit of infinite wavelengths; i.e., these packings are hyperuniform, which means that density fluctuations on large length scales are anomalously suppressed. Moreover, for all model systems, we study and compute exclusion probabilities and pore size distributions, as well as local density fluctuations. We conjecture that for general disordered hard-sphere packings, a central limit theorem holds for the number of points within an spherical observation window. Our analysis links problems of interest in material science, chemistry, physics, and mathematics. In the third paper of this series, we will evaluate bounds and estimates of a host of different physical properties of the MRJ sphere packings that are based on the
Adaptive Influence Maximization in Social Networks: Why Commit when You can Adapt?
Vaswani, Sharan; Lakshmanan, Laks V. S.
2016-01-01
Most previous work on influence maximization in social networks is limited to the non-adaptive setting in which the marketer is supposed to select all of the seed users, to give free samples or discounts to, up front. A disadvantage of this setting is that the marketer is forced to select all the seeds based solely on a diffusion model. If some of the selected seeds do not perform well, there is no opportunity to course-correct. A more practical setting is the adaptive setting in which the ma...
A Tabu Search DSA Algorithm for Reward Maximization in Cellular Networks
Kamal, Hany; Coupechoux, Marceau; Godlewski, Philippe
2010-01-01
International audience; In this paper, we present and analyze a Tabu Search (TS) algorithm for DSA (Dynamic Spectrum Access) in cellular networks. We study a mono-operator case where the operator is providing packet services to the end-users. The objective of the cellular operator is to maximize its reward while taking into account the trade-off between the spectrum cost and the revenues obtained from end-users. These revenue are modeled here as an increasing function of the achieved throughp...
Holographic coherent states from random tensor networks
Qi, Xiao-Liang; Yang, Zhao; You, Yi-Zhuang
2017-08-01
Random tensor networks provide useful models that incorporate various important features of holographic duality. A tensor network is usually defined for a fixed graph geometry specified by the connection of tensors. In this paper, we generalize the random tensor network approach to allow quantum superposition of different spatial geometries. We setup a framework in which all possible bulk spatial geometries, characterized by weighted adjacient matrices of all possible graphs, are mapped to the boundary Hilbert space and form an overcomplete basis of the boundary. We name such an overcomplete basis as holographic coherent states. A generic boundary state can be expanded in this basis, which describes the state as a superposition of different spatial geometries in the bulk. We discuss how to define distinct classical geometries and small fluctuations around them. We show that small fluctuations around classical geometries define "code subspaces" which are mapped to the boundary Hilbert space isometrically with quantum error correction properties. In addition, we also show that the overlap between different geometries is suppressed exponentially as a function of the geometrical difference between the two geometries. The geometrical difference is measured in an area law fashion, which is a manifestation of the holographic nature of the states considered.
Epidemic Spreading in Random Rectangular Networks
Estrada, Ernesto; Moreno, Yamir
2015-01-01
Recently, Estrada and Sheerin (Phys. Rev. E 91, 042805 (2015)) developed the random rectangular graph (RRG) model to account for the spatial distribution of nodes in a network allowing the variation of the shape of the unit square commonly used in random geometric graphs (RGGs). Here, we consider an epidemics dynamics taking place on the nodes and edges of an RRG and we derive analytically a lower bound for the epidemic threshold for a Susceptible-Infected-Susceptible (SIS) or Susceptible-Infected-Recovered (SIR) model on these networks. Using extensive numerical simulations of the SIS dynamics we show that the lower bound found is very tight. We conclude that the elongation of the area in which the nodes are distributed makes the network more resilient to the propagation of an epidemics due to the fact that the epidemic threshold increases with the elongation of the rectangle. On the other hand, using the "classical" RGG for modeling epidemics on non-squared cities generates a larger error due to the effects...
Optimal Marketing Policy in a Random Network
Amini, Hamed
2008-01-01
Viral marketing takes advantage of preexisting social networks among customers to achieve large changes in behavior. Models of influence spread have been studied in a number of domains, including the effect of 'word of mouth' in the promotion of new products or the diffusion of technologies. A social network can be represented by a graph where the nodes are individuals and the edges indicate a form of social relationship. The flow of influence through this network can be thought of as an increasing process of active nodes: as individuals become aware of new technologies, they have the potential to pass them on to their neighbors. The goal of marketing is to trigger a large cascade of adoptions. In this paper, we present and solve an analytical model where the individuals are connected according to a large sparse random graph. Borrowing ideas and techniques from Markov random fields, we derive analytical results for various threshold models. The parameters of the model (like the initial fraction of active indi...
Features of Random Metal Nanowire Networks with
Maloth, Thirupathi
2017-05-01
Among the alternatives to conventional Indium Tin Oxide (ITO) used in making transparent conducting electrodes, the random metal nanowire (NW) networks are considered to be superior offering performance at par with ITO. The performance is measured in terms of sheet resistance and optical transmittance. However, as the electrical properties of such random networks are achieved thanks to a percolation network, a minimum size of the electrodes is needed so it actually exceeds the representative volume element (RVE) of the material and the macroscopic electrical properties are achieved. There is not much information about the compatibility of this minimum RVE size with the resolution actually needed in electronic devices. Furthermore, the efficiency of NWs in terms of electrical conduction is overlooked. In this work, we address the above industrially relevant questions - 1) The minimum size of electrodes that can be made based on the dimensions of NWs and the material coverage. For this, we propose a morphology based classification in defining the RVE size and we also compare the same with that is based on macroscopic electrical properties stabilization. 2) The amount of NWs that do not participate in electrical conduction, hence of no practical use. The results presented in this thesis are a design guide to experimentalists to design transparent electrodes with more optimal usage of the material.
Random representation of spatially embedded complex transportation networks
Hackl, Jürgen
2016-01-01
Random networks are increasingly used to analyse complex transportation networks, such as airline routes, roads and rail networks. So far, this research has been focused on describing the properties of the networks with the help of random networks, often without considering their spatial properties. In this article, a methodology is proposed to create random networks conserving their spatial properties. The produced random networks are not intended to be an accurate model of the real-world network being investigated, but are to be used to gain insight into the functioning of the network taking into consideration its spatial properties, which has potential to be useful in many types of analysis, e.g. estimating the network related risk. The proposed methodology combines a spatial non-homogeneous point process for vertex creation, which accounts for the spatial distribution of vertices, considering clustering effects of the network and a hybrid connection model for the edge creation. To illustrate the ability o...
Energy-Latency Tradeoff for In-Network Function Computation in Random Networks
Balister, Paul; Anandkumar, Animashree; Willsky, Alan
2011-01-01
The problem of designing policies for in-network function computation with minimum energy consumption subject to a latency constraint is considered. The scaling behavior of the energy consumption under the latency constraint is analyzed for random networks, where the nodes are uniformly placed in growing regions and the number of nodes goes to infinity. The special case of sum function computation and its delivery to a designated root node is considered first. A policy which achieves order-optimal average energy consumption in random networks subject to the given latency constraint is proposed. The scaling behavior of the optimal energy consumption depends on the path-loss exponent of wireless transmissions and the dimension of the Euclidean region where the nodes are placed. The policy is then extended to computation of a general class of functions which decompose according to maximal cliques of a proximity graph such as the $k$-nearest neighbor graph or the geometric random graph. The modified policy achiev...
Distributive Network Utility Maximization (NUM) over Time-Varying Fading Channels
Chen, Junting; Lau, Vincent K N
2011-01-01
Distributed network utility maximization (NUM) has received an increasing intensity of interest over the past few years. Distributed solutions (e.g., the primal-dual gradient method) have been intensively investigated under fading channels. As such distributed solutions involve iterative updating and explicit message passing, it is unrealistic to assume that the wireless channel remains unchanged during the iterations. Unfortunately, the behavior of those distributed solutions under time-varying channels is in general unknown. In this paper, we shall investigate the convergence behavior and tracking errors of the iterative primal-dual scaled gradient algorithm (PDSGA) with dynamic scaling matrices (DSC) for solving distributive NUM problems under time-varying fading channels. We shall also study a specific application example, namely the multi-commodity flow control and multi-carrier power allocation problem in multi-hop ad hoc networks. Our analysis shows that the PDSGA converges to a limit region rather tha...
Biased random walks on Kleinberg's spatial networks
Pan, Gui-Jun; Niu, Rui-Wu
2016-12-01
We investigate the problem of the particle or message that travels as a biased random walk toward a target node in Kleinberg's spatial network which is built from a d-dimensional (d = 2) regular lattice improved by adding long-range shortcuts with probability P(rij) ∼rij-α, where rij is the lattice distance between sites i and j, and α is a variable exponent. Bias is represented as a probability p of the packet to travel at every hop toward the node which has the smallest Manhattan distance to the target node. We study the mean first passage time (MFPT) for different exponent α and the scaling of the MFPT with the size of the network L. We find that there exists a threshold probability pth ≈ 0.5, for p ≥pth the optimal transportation condition is obtained with an optimal transport exponent αop = d, while for 0 pth, and increases with L less than a power law and get close to logarithmical law for 0 complex network with a highly efficient structure for navigation although nodes hold null local information with a relatively large probability, which gives a powerful evidence for the reason why many real networks' navigability have small world property.
Tabita, F. Robert [The Ohio State University
2013-07-30
In this study, the Principal Investigator, F.R. Tabita has teemed up with J. C. Liao from UCLA. This project's main goal is to manipulate regulatory networks in phototrophic bacteria to affect and maximize the production of large amounts of hydrogen gas under conditions where wild-type organisms are constrained by inherent regulatory mechanisms from allowing this to occur. Unrestrained production of hydrogen has been achieved and this will allow for the potential utilization of waste materials as a feed stock to support hydrogen production. By further understanding the means by which regulatory networks interact, this study will seek to maximize the ability of currently available “unrestrained” organisms to produce hydrogen. The organisms to be utilized in this study, phototrophic microorganisms, in particular nonsulfur purple (NSP) bacteria, catalyze many significant processes including the assimilation of carbon dioxide into organic carbon, nitrogen fixation, sulfur oxidation, aromatic acid degradation, and hydrogen oxidation/evolution. Moreover, due to their great metabolic versatility, such organisms highly regulate these processes in the cell and since virtually all such capabilities are dispensable, excellent experimental systems to study aspects of molecular control and biochemistry/physiology are available.
Influence maximization in social networks under an independent cascade-based model
Wang, Qiyao; Jin, Yuehui; Lin, Zhen; Cheng, Shiduan; Yang, Tan
2016-02-01
The rapid growth of online social networks is important for viral marketing. Influence maximization refers to the process of finding influential users who make the most of information or product adoption. An independent cascade-based model for influence maximization, called IMIC-OC, was proposed to calculate positive influence. We assumed that influential users spread positive opinions. At the beginning, users held positive or negative opinions as their initial opinions. When more users became involved in the discussions, users balanced their own opinions and those of their neighbors. The number of users who did not change positive opinions was used to determine positive influence. Corresponding influential users who had maximum positive influence were then obtained. Experiments were conducted on three real networks, namely, Facebook, HEP-PH and Epinions, to calculate maximum positive influence based on the IMIC-OC model and two other baseline methods. The proposed model resulted in larger positive influence, thus indicating better performance compared with the baseline methods.
Hamza, Doha
2015-02-13
We propose a three-message superposition coding scheme in a cognitive radio relay network exploiting active cooperation between primary and secondary users. The primary user is motivated to cooperate by substantial benefits it can reap from this access scenario. Specifically, the time resource is split into three transmission phases: The first two phases are dedicated to primary communication, while the third phase is for the secondary’s transmission. We formulate two throughput maximization problems for the secondary network subject to primary user rate constraints and per-node power constraints with respect to the time durations of primary transmission and the transmit power of the primary and the secondary users. The first throughput maximization problem assumes a partial power constraint such that the secondary power dedicated to primary cooperation, i.e. for the first two communication phases, is fixed apriori. In the second throughput maximization problem, a total power constraint is assumed over the three phases of communication. The two problems are difficult to solve analytically when the relaying channel gains are strictly greater than each other and strictly greater than the direct link channel gain. However, mathematically tractable lowerbound and upperbound solutions can be attained for the two problems. For both problems, by only using the lowerbound solution, we demonstrate significant throughput gains for both the primary and the secondary users through this active cooperation scheme. We find that most of the throughput gains come from minimizing the second phase transmission time since the secondary nodes assist the primary communication during this phase. Finally, we demonstrate the superiority of our proposed scheme compared to a number of reference schemes that include best relay selection, dual-hop routing, and an interference channel model.
Mr. Yogesh Rai
2011-09-01
Full Text Available Many methods have been researched to prolong sensor network lifetime using mobile technologies. In the mobile sink research, there are the track based methods and the anchor points based methods as representative operation methods for mobile sinks. However, the existing methods decrease Quality of Service (QoS and lead the routing hotspot in the vicinity of the mobile sink. In large scale wireless sensor networks, clustering is an effective technique for the purpose of improving the utilization of limited energy and prolonging the network lifetime. However, the problem of unbalanced energy dissipation exists in the multi-hop clustering model, where the cluster heads closer to the sink have to relay heavier traffic and consume more energy than farther nodes. In this paper we analyze several aspects based on the optimal clustering architecture for maximizing lifetime for large scale wireless sensor network. We also provide some analytical concepts for energy-aware head rotation and routing protocols to further balance the energy consumption among all nodes.
FUSE: a profit maximization approach for functional summarization of biological networks
Seah Boon-Siew
2012-03-01
Full Text Available Abstract Background The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL principle to maximize information gain of the summary graph while satisfying the level of detail constraint. Results We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. Conclusion By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.
Exploring the randomness of Directed Acyclic Networks
Goñi, Joaquín; Solé, Ricard V; Rodríguez-Caso, Carlos
2010-01-01
The feed-forward relationship naturally observed in time-dependent processes and in a diverse number of real systems -such as some food-webs and electronic and neural wiring- can be described in terms of so-called directed acyclic graphs (DAGs). An important ingredient of the analysis of such networks is a proper comparison of their observed architecture against an ensemble of randomized graphs, thereby quantifying the {\\em randomness} of the real systems with respect to suitable null models. This approximation is particularly relevant when the finite size and/or large connectivity of real systems make inadequate a comparison with the predictions obtained from the so-called {\\em configuration model}. In this paper we analyze four methods of DAG randomization as defined by the desired combination of topological invariants (directed and undirected degree sequence and component distributions) aimed to be preserved. A highly ordered DAG, called \\textit{snake}-graph and a Erd\\:os-R\\'enyi DAG were used to validate ...
A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.
Alireza Alemi
2015-08-01
Full Text Available Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the
A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.
Alemi, Alireza; Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo
2015-08-01
Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored
Sum rate maximization in the uplink of multi-cell OFDMA networks
Tabassum, Hina
2012-10-03
Resource allocation in orthogonal frequency division multiple access (OFDMA) networks plays an imperative role to guarantee the system performance. However, most of the known resource allocation schemes are focused on maximizing the local throughput of each cell, while ignoring the significant effect of inter-cell interference. This paper investigates the problem of resource allocation (i.e., subcarriers and powers) in the uplink of a multi-cell OFDMA network. The problem has a non-convex combinatorial structure and is known to be NP hard. Firstly, we investigate the upper and lower bounds to the average network throughput due to the inherent complexity of implementing the optimal solution. Later, a centralized sub-optimal resource allocation scheme is developed. We further develop less complex centralized and distributed schemes that are well-suited for practical scenarios. The computational complexity of all schemes has been analyzed and the performance is compared through numerical simulations. Simulation results demonstrate that the distributed scheme achieves comparable performance to the centralized resource allocation scheme in various scenarios. © 2011 IEEE.
Fair data scheduling in OFDM wireless networks based on maximizing utility
无
2007-01-01
This paper proposes a joint layer scheme for fair downlink data scheduling in multiuser OFDM wireless networks. Based on the optimization model formulated as the maximization of total utility function with respect to the mean waiting time of user queue, we present an algorithm with low complexity for dynamic subcarrier allocation (DSA). The decision for subcarrier allocation was made according to delay utility function obtained by the algorithm that instantaneously estimated both channel condition and queue length using an exponentially weighted low-pass time window and pilot signals respectively. The complexity of algorithm was reduced by varying the length of the time window to make use of time diversity, which provided higher throughput ratio.Simulation results demonstrate that compared with the conventional approach, the proposed scheme achieves better performance and can significantly improve fairness among users, with very limited delay performance degradation by using a decreasing concave utility function when the traffic load increases.
Jiang, Jonathan Q
2011-01-01
We show here that the problem of maximizing a family of quantitative functions, encompassing both the modularity (Q-measure) and modularity density (D-measure), for community detection can be uniformly understood as a combinatoric optimization involving the trace of a matrix called modularity Laplacian. Instead of using traditional spectral relaxation, we apply additional nonnegative constraint into this graph clustering problem and design efficient algorithms to optimize the new objective. With the explicit nonnegative constraint, our solutions are very close to the ideal community indicator matrix and can directly assign nodes into communities. The near-orthogonal columns of the solution can be reformulated as the posterior probability of corresponding node belonging to each community. Therefore, the proposed method can be exploited to identify the fuzzy or overlapping communities and thus facilitates the understanding of the intrinsic structure of networks. Experimental results show that our new algorithm ...
Decentralized Lifetime Maximizing Tree with Clustering for Data Delivery in Wireless Sensor Networks
Deepali Virmani
2011-09-01
Full Text Available A wireless sensor network has a wide application domain which is expanding everyday and they have been deployed pertaining to their application area. An application independent approach is yet to come to terms with the ongoing exploitation of the WSNs. In this paper we propose a decentralized lifetime maximizing tree for application independent data aggregation scheme using the clustering for data delivery in WSNs. The proposed tree will minimize the energy consumption which has been a resisting factor in the smooth working of WSNs as well as minimize the distance between the communicating nodes under the control of a sub-sink which further communicate and transfer data to the sink node.
Energy and criticality in random Boolean networks
Andrecut, M. [Institute for Biocomplexity and Informatics, University of Calgary, 2500 University Drive NW, Calgary, Alberta, T2N 1N4 (Canada)], E-mail: mandrecu@ucalgary.ca; Kauffman, S.A. [Institute for Biocomplexity and Informatics, University of Calgary, 2500 University Drive NW, Calgary, Alberta, T2N 1N4 (Canada)
2008-06-30
The central issue of the research on the Random Boolean Networks (RBNs) model is the characterization of the critical transition between ordered and chaotic phases. Here, we discuss an approach based on the 'energy' associated with the unsatisfiability of the Boolean functions in the RBNs model, which provides an upper bound estimation for the energy used in computation. We show that in the ordered phase the RBNs are in a 'dissipative' regime, performing mostly 'downhill' moves on the 'energy' landscape. Also, we show that in the disordered phase the RBNs have to 'hillclimb' on the 'energy' landscape in order to perform computation. The analytical results, obtained using Derrida's approximation method, are in complete agreement with numerical simulations.
Multifractal properties of the random resistor network
Barthelemy; Buldyrev; Havlin; Stanley
2000-04-01
We study the multifractal spectrum of the current in the two-dimensional random resistor network at the percolation threshold. We consider two ways of applying the voltage difference: (i) two parallel bars, and (ii) two points. Our numerical results suggest that in the infinite system limit, the probability distribution behaves for small i as P(i) approximately 1/i, where i is the current. As a consequence, the moments of i of order q
Immunization for scale-free networks by random walker
Hu Ke; Tang Yi
2006-01-01
Based on the random walk and the intentional random walk, we propose two types of immunization strategies which require only local connectivity information. On several typical scale-free networks, we demonstrate that these strategies can lead to the eradication of the epidemic by immunizing a small fraction of the nodes in the networks.Particularly, the immunization strategy based on the intentional random walk is extremely efficient for the assortatively mixed networks.
Random symmetry breaking and freezing in chaotic networks.
Peleg, Y; Kinzel, W; Kanter, I
2012-09-01
Parameter space of a driven damped oscillator in a double well potential presents either a chaotic trajectory with sign oscillating amplitude or a nonchaotic trajectory with a fixed sign amplitude. A network of such delay coupled damped oscillators is shown to present chaotic dynamics while the sign amplitude of each damped oscillator is randomly frozen. This phenomenon of random broken global symmetry of the network simultaneous with random freezing of each degree of freedom is accompanied by the existence of exponentially many randomly frozen chaotic attractors with the size of the network. Results are exemplified by a network of modified Duffing oscillators with infinite range pseudoinverse delayed interactions.
Complex network analysis of state spaces for random Boolean networks
Shreim, Amer [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Berdahl, Andrew [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Sood, Vishal [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Grassberger, Peter [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Paczuski, Maya [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada)
2008-01-15
We apply complex network analysis to the state spaces of random Boolean networks (RBNs). An RBN contains N Boolean elements each with K inputs. A directed state space network (SSN) is constructed by linking each dynamical state, represented as a node, to its temporal successor. We study the heterogeneity of these SSNs at both local and global scales, as well as sample to-sample fluctuations within an ensemble of SSNs. We use in-degrees of nodes as a local topological measure, and the path diversity (Shreim A et al 2007 Phys. Rev. Lett. 98 198701) of an SSN as a global topological measure. RBNs with 2 {<=} K {<=} 5 exhibit non-trivial fluctuations at both local and global scales, while K = 2 exhibits the largest sample-to-sample (possibly non-self-averaging) fluctuations. We interpret the observed 'multi scale' fluctuations in the SSNs as indicative of the criticality and complexity of K = 2 RBNs. 'Garden of Eden' (GoE) states are nodes on an SSN that have in-degree zero. While in-degrees of non-GoE nodes for K > 1 SSNs can assume any integer value between 0 and 2{sup N}, for K = 1 all the non-GoE nodes in a given SSN have the same in-degree which is always a power of two.
A Round-based Pricing Scheme for Maximizing Service Provider's Revenue in P2PTV Networks
Bhutani, Gitanjali
2009-01-01
In this paper, we analyze a round-based pricing scheme that encourages favorable behavior from users of real-time P2P applications like P2PTV. In the design of pricing schemes, we consider price to be a function of usage and capacity of download/upload streams, and quality of content served. Users are consumers and servers at the same time in such networks, and often exhibit behavior that is unfavorable towards maximization of social benefits. Traditionally, network designers have overcome this difficulty by building-in traffic latencies. However, using simulations, we show that appropriate pricing schemes and usage terms can enable designers to limit required traffic latencies, and be able to earn nearly 30% extra revenue from providing P2PTV services. The service provider adjusts the prices of individual programs incrementally within rounds, while making relatively large-scale adjustments at the end of each round. Through simulations, we show that it is most beneficial for the service provider to carry out ...
Resource allocation via sum-rate maximization in the uplink of multi-cell OFDMA networks
Tabassum, Hina
2012-10-03
In this paper, we consider maximizing the sum rate in the uplink of a multi-cell orthogonal frequency-division multiple access network. The problem has a non-convex combinatorial structure and is known to be NP-hard. Because of the inherent complexity of implementing the optimal solution, firstly, we derive an upper bound (UB) and a lower bound (LB) to the optimal average network throughput. Moreover, we investigate the performance of a near-optimal single cell resource allocation scheme in the presence of inter-cell interference, which leads to another easily computable LB. We then develop a centralized sub-optimal scheme that is composed of a geometric programming-based power control phase in conjunction with an iterative subcarrier allocation phase. Although the scheme is computationally complex, it provides an effective benchmark for low complexity schemes even without the power control phase. Finally, we propose less complex centralized and distributed schemes that are well suited for practical scenarios. The computational complexity of all schemes is analyzed, and the performance is compared through simulations. Simulation results demonstrate that the proposed low complexity schemes can achieve comparable performance with that of the centralized sub-optimal scheme in various scenarios. Moreover, comparisons with the UB and LB provide insight on the performance gap between the proposed schemes and the optimal solution. Copyright © 2011 John Wiley & Sons, Ltd.
Throughput Maximization for Sensor-Aided Cognitive Radio Networks with Continuous Energy Arrivals.
Nguyen, Thanh-Tung; Koo, Insoo
2015-11-27
We consider a Sensor-Aided Cognitive Radio Network (SACRN) in which sensors capable of harvesting energy are distributed throughout the network to support secondary transmitters for sensing licensed channels in order to improve both energy and spectral efficiency. Harvesting ambient energy is one of the most promising solutions to mitigate energy deficiency, prolong device lifetime, and partly reduce the battery size of devices. So far, many works related to SACRN have considered single secondary users capable of harvesting energy in whole slot as well as short-term throughput. In the paper, we consider two types of energy harvesting sensor nodes (EHSN): Type-I sensor nodes will harvest ambient energy in whole slot duration, whereas type-II sensor nodes will only harvest energy after carrying out spectrum sensing. In the paper, we also investigate long-term throughput in the scheduling window, and formulate the throughput maximization problem by considering energy-neutral operation conditions of type-I and -II sensors and the target detection probability. Through simulations, it is shown that the sensing energy consumption of all sensor nodes can be efficiently managed with the proposed scheme to achieve optimal long-term throughput in the window.
Designing neural networks that process mean values of random variables
Barber, Michael J. [AIT Austrian Institute of Technology, Innovation Systems Department, 1220 Vienna (Austria); Clark, John W. [Department of Physics and McDonnell Center for the Space Sciences, Washington University, St. Louis, MO 63130 (United States); Centro de Ciências Matemáticas, Universidade de Madeira, 9000-390 Funchal (Portugal)
2014-06-13
We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence.
Search for Directed Networks by Different Random Walk Strategies
ZHU Zi-Qi; JIN Xiao-Ling; HUANG Zhi-Long
2012-01-01
A comparative study is carried out on the effciency of five different random walk strategies searching on directed networks constructed based on several typical complex networks.Due to the difference in search effciency of the strategies rooted in network clustering,the clustering coeFfcient in a random walker's eye on directed networks is defined and computed to be half of the corresponding undirected networks.The search processes are performed on the directed networks based on Erd(o)s-Rényi model,Watts-Strogatz model,Barabási-Albert model and clustered scale-free network model.It is found that self-avoiding random walk strategy is the best search strategy for such directed networks.Compared to unrestricted random walk strategy,path-iteration-avoiding random walks can also make the search process much more effcient. However,no-triangle-loop and no-quadrangle-loop random walks do not improve the search effciency as expected,which is different from those on undirected networks since the clustering coefficient of directed networks are smaller than that of undirected networks.
Perturbation propagation in random and evolved Boolean networks
Fretter, Christoph [Instistut fuer Informatik, Martin-Luther-Universitaet Halle-Wittenberg, Von-Seckendorffplatz 1, 06120 Halle (Germany); Szejka, Agnes; Drossel, Barbara [Institut fuer Festkoerperphysik, Technische Universitaet Darmstadt, Hochschulstrasse 6, 64289 Darmstadt (Germany)], E-mail: Christoph.Fretter@informatik.uni-halle.de
2009-03-15
In this paper, we investigate the propagation of perturbations in Boolean networks by evaluating the Derrida plot and its modifications. We show that even small random Boolean networks agree well with the predictions of the annealed approximation, but nonrandom networks show a very different behaviour. We focus on networks that were evolved for high dynamical robustness. The most important conclusion is that the simple distinction between frozen, critical and chaotic networks is no longer useful, since such evolved networks can display the properties of all three types of networks. Furthermore, we evaluate a simplified empirical network and show how its specific state space properties are reflected in the modified Derrida plots.
Holographic duality from random tensor networks
Hayden, Patrick; Nezami, Sepehr; Qi, Xiao-Liang; Thomas, Nathaniel; Walter, Michael; Yang, Zhao [Stanford Institute for Theoretical Physics, Department of Physics, Stanford University,382 Via Pueblo, Stanford, CA 94305 (United States)
2016-11-02
Tensor networks provide a natural framework for exploring holographic duality because they obey entanglement area laws. They have been used to construct explicit toy models realizing many of the interesting structural features of the AdS/CFT correspondence, including the non-uniqueness of bulk operator reconstruction in the boundary theory. In this article, we explore the holographic properties of networks of random tensors. We find that our models naturally incorporate many features that are analogous to those of the AdS/CFT correspondence. When the bond dimension of the tensors is large, we show that the entanglement entropy of all boundary regions, whether connected or not, obey the Ryu-Takayanagi entropy formula, a fact closely related to known properties of the multipartite entanglement of assistance. We also discuss the behavior of Rényi entropies in our models and contrast it with AdS/CFT. Moreover, we find that each boundary region faithfully encodes the physics of the entire bulk entanglement wedge, i.e., the bulk region enclosed by the boundary region and the minimal surface. Our method is to interpret the average over random tensors as the partition function of a classical ferromagnetic Ising model, so that the minimal surfaces of Ryu-Takayanagi appear as domain walls. Upon including the analog of a bulk field, we find that our model reproduces the expected corrections to the Ryu-Takayanagi formula: the bulk minimal surface is displaced and the entropy is augmented by the entanglement of the bulk field. Increasing the entanglement of the bulk field ultimately changes the minimal surface behavior topologically, in a way similar to the effect of creating a black hole. Extrapolating bulk correlation functions to the boundary permits the calculation of the scaling dimensions of boundary operators, which exhibit a large gap between a small number of low-dimension operators and the rest. While we are primarily motivated by the AdS/CFT duality, the main
Cross over of recurrence networks to random graphs and random geometric graphs
RINKU JACOB; K P HARIKRISHNAN; R MISRA; G AMBIKA
2017-02-01
Recurrence networks are complex networks constructed from the time series of chaotic dynamical systems where the connection between two nodes is limited by the recurrence threshold. This condition makes the topology of every recurrence network unique with the degree distribution determined by the probability densityvariations of the representative attractor from which it is constructed. Here we numerically investigate the properties of recurrence networks from standard low-dimensional chaotic attractors using some basic network measuresand show how the recurrence networks are different from random and scale-free networks. In particular, we show that all recurrence networks can cross over to random geometric graphs by adding sufficient amount of noise tothe time series and into the classical random graphs by increasing the range of interaction to the system size. We also highlight the effectiveness of a combined plot of characteristic path length and clustering coefficient in capturing the small changes in the network characteristics.
Cross over of recurrence networks to random graphs and random geometric graphs
Jacob, Rinku; Harikrishnan, K. P.; Misra, R.; Ambika, G.
2017-02-01
Recurrence networks are complex networks constructed from the time series of chaotic dynamical systems where the connection between two nodes is limited by the recurrence threshold. This condition makes the topology of every recurrence network unique with the degree distribution determined by the probability density variations of the representative attractor from which it is constructed. Here we numerically investigate the properties of recurrence networks from standard low-dimensional chaotic attractors using some basic network measures and show how the recurrence networks are different from random and scale-free networks. In particular, we show that all recurrence networks can cross over to random geometric graphs by adding sufficient amount of noise to the time series and into the classical random graphs by increasing the range of interaction to the system size. We also highlight the effectiveness of a combined plot of characteristic path length and clustering coefficient in capturing the small changes in the network characteristics.
Random network peristalsis in Physarum polycephalum organizes fluid flows across an individual.
Alim, Karen; Amselem, Gabriel; Peaudecerf, François; Brenner, Michael P; Pringle, Anne
2013-08-13
Individuals can function as integrated organisms only when information and resources are shared across a body. Signals and substrates are commonly moved using fluids, often channeled through a network of tubes. Peristalsis is one mechanism for fluid transport and is caused by a wave of cross-sectional contractions along a tube. We extend the concept of peristalsis from the canonical case of one tube to a random network. Transport is maximized within the network when the wavelength of the peristaltic wave is of the order of the size of the network. The slime mold Physarum polycephalum grows as a random network of tubes, and our experiments confirm peristalsis is used by the slime mold to drive internal cytoplasmic flows. Comparisons of theoretically generated contraction patterns with the patterns exhibited by individuals of P. polycephalum demonstrate that individuals maximize internal flows by adapting patterns of contraction to size, thus optimizing transport throughout an organism. This control of fluid flow may be the key to coordinating growth and behavior, including the dynamic changes in network architecture seen over time in an individual.
Social group utility maximization
Gong, Xiaowen; Yang, Lei; Zhang, Junshan
2014-01-01
This SpringerBrief explains how to leverage mobile users' social relationships to improve the interactions of mobile devices in mobile networks. It develops a social group utility maximization (SGUM) framework that captures diverse social ties of mobile users and diverse physical coupling of mobile devices. Key topics include random access control, power control, spectrum access, and location privacy.This brief also investigates SGUM-based power control game and random access control game, for which it establishes the socially-aware Nash equilibrium (SNE). It then examines the critical SGUM-b
Experimental percolation studies of random networks
Feinerman, A.; Weddell, J.
2017-06-01
This report establishes an experimental method of studying electrically percolating networks at a higher resolution than previously implemented. This method measures the current across a conductive sheet as a function of time as elliptical pores are cut into the sheet. This is done utilizing a Universal Laser System X2-600 100 W CO2 laser system with a 76 × 46 cm2 field and 394 dpc (dots/cm) resolution. This laser can cut a random system of elliptical pores into a conductive sheet with a potential voltage applied across it and measures the current versus time. This allows for experimental verification of a percolation threshold as a function of the ellipse's aspect ratio (minor/major diameter). We show that as an ellipse's aspect ratio approaches zero, the percolation threshold approaches one. The benefit of this method is that it can experimentally measure the effect of removing small pores, as well as pores with complex geometries, such as an asterisk from a conductive sheet.
Power Optimization on a Network: The effects of randomness
Moustakas, Aris L
2012-01-01
Consider a wireless network of transmitter-receiver pairs. The transmitters adjust their powers to maintain a particular SINR target in the presence of interference from neighboring transmitters. In this paper we analyze the optimal power vector that may achieve this target in the presence of randomness in the network. Specifically, we start from a regular grid of transmitter-receiver pairs and randomly turn-off a finite fraction of them. We apply concepts from random matrix theory to evaluate the asymptotic mean optimal power per link, as well as its variance. Our analytical results show remarkable agreement with numerically generated networks, not only in one-dimensional network arrays but also in two dimensional network geometries. Remarkably, we observe that the optimal power in random networks does not go to infinity in a continuous fashion as in regular grids. Rather, beyond a certain point, no finite power solution exists.
Random walk immunization strategy on scale-free networks
Weidong PEI; Zengqiang CHEN; Zhuzhi YUAN
2009-01-01
A novel immunization strategy called the random walk immunization strategy on scale-free networks is proposed. Different from other known immunization strategies, this strategy works as follows: a node is randomly chosen from the network. Starting from this node, randomly walk to one of its neighbor node; if the present node is not immunized, then immunize it and continue the random walk; otherwise go back to the previous node and randomly walk again. This process is repeated until a certain fraction of nodes is immunized. By theoretical analysis and numerical simulations, we found that this strategy is very effective in comparison with the other known immunization strategies.
Bayer, Christian
2016-02-20
© 2016 Taylor & Francis Group, LLC. ABSTRACT: In this work, we present an extension of the forward–reverse representation introduced by Bayer and Schoenmakers (Annals of Applied Probability, 24(5):1994–2032, 2014) to the context of stochastic reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, that is, SRNs conditional on their values in the extremes of given time intervals. We then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve a set of deterministic optimization problems where the SRNs are replaced by their reaction-rate ordinary differential equations approximation; then, during phase II, we apply the Monte Carlo version of the expectation-maximization algorithm to the phase I output. By selecting a set of overdispersed seeds as initial points in phase I, the output of parallel runs from our two-phase method is a cluster of approximate maximum likelihood estimates. Our results are supported by numerical examples.
Vilanova, Pedro
2016-01-07
In this work, we present an extension of the forward-reverse representation introduced in Simulation of forward-reverse stochastic representations for conditional diffusions , a 2014 paper by Bayer and Schoenmakers to the context of stochastic reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, i.e., SRNs conditional on their values in the extremes of given time-intervals. We then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve a set of deterministic optimization problems where the SRNs are replaced by their reaction-rate ordinary differential equations approximation; then, during phase II, we apply the Monte Carlo version of the Expectation-Maximization algorithm to the phase I output. By selecting a set of over-dispersed seeds as initial points in phase I, the output of parallel runs from our two-phase method is a cluster of approximate maximum likelihood estimates. Our results are supported by numerical examples.
Randomizing bipartite networks: the case of the World Trade Web
Saracco, Fabio; Gabrielli, Andrea; Squartini, Tiziano
2015-01-01
Within the last fifteen years, network theory has been successfully applied both to natural sciences and to socioeconomic disciplines. In particular, bipartite networks have been recognized to provide a particularly insightful representation of many systems, ranging from mutualistic networks in ecology to trade networks in economy, whence the need of a pattern detection-oriented analysis in order to identify statistically-significant structural properties. Such an analysis rests upon the definition of suitable null models, i.e. upon the choice of the portion of network structure to be preserved while randomizing everything else. However, quite surprisingly, little work has been done so far to define null models for real bipartite networks. The aim of the present work is to fill this gap, extending a recently-proposed method to randomize monopartite networks to bipartite networks. While the proposed formalism is perfectly general, we apply our method to the binary, undirected, bipartite representation of the W...
Random matrix analysis of localization properties of gene coexpression network.
Jalan, Sarika; Solymosi, Norbert; Vattay, Gábor; Li, Baowen
2010-04-01
We analyze gene coexpression network under the random matrix theory framework. The nearest-neighbor spacing distribution of the adjacency matrix of this network follows Gaussian orthogonal statistics of random matrix theory (RMT). Spectral rigidity test follows random matrix prediction for a certain range and deviates afterwards. Eigenvector analysis of the network using inverse participation ratio suggests that the statistics of bulk of the eigenvalues of network is consistent with those of the real symmetric random matrix, whereas few eigenvalues are localized. Based on these IPR calculations, we can divide eigenvalues in three sets: (a) The nondegenerate part that follows RMT. (b) The nondegenerate part, at both ends and at intermediate eigenvalues, which deviates from RMT and expected to contain information about important nodes in the network. (c) The degenerate part with zero eigenvalue, which fluctuates around RMT-predicted value. We identify nodes corresponding to the dominant modes of the corresponding eigenvectors and analyze their structural properties.
Near-Optimal Random Walk Sampling in Distributed Networks
Sarma, Atish Das; Pandurangan, Gopal
2012-01-01
Performing random walks in networks is a fundamental primitive that has found numerous applications in communication networks such as token management, load balancing, network topology discovery and construction, search, and peer-to-peer membership management. While several such algorithms are ubiquitous, and use numerous random walk samples, the walks themselves have always been performed naively. In this paper, we focus on the problem of performing random walk sampling efficiently in a distributed network. Given bandwidth constraints, the goal is to minimize the number of rounds and messages required to obtain several random walk samples in a continuous online fashion. We present the first round and message optimal distributed algorithms that present a significant improvement on all previous approaches. The theoretical analysis and comprehensive experimental evaluation of our algorithms show that they perform very well in different types of networks of differing topologies. In particular, our results show h...
Phase transitions for information diffusion in random clustered networks
Lim, Sungsu; Shin, Joongbo; Kwak, Namju; Jung, Kyomin
2016-09-01
We study the conditions for the phase transitions of information diffusion in complex networks. Using the random clustered network model, a generalisation of the Chung-Lu random network model incorporating clustering, we examine the effect of clustering under the Susceptible-Infected-Recovered (SIR) epidemic diffusion model with heterogeneous contact rates. For this purpose, we exploit the branching process to analyse information diffusion in random unclustered networks with arbitrary contact rates, and provide novel iterative algorithms for estimating the conditions and sizes of global cascades, respectively. Showing that a random clustered network can be mapped into a factor graph, which is a locally tree-like structure, we successfully extend our analysis to random clustered networks with heterogeneous contact rates. We then identify the conditions for phase transitions of information diffusion using our method. Interestingly, for various contact rates, we prove that random clustered networks with higher clustering coefficients have strictly lower phase transition points for any given degree sequence. Finally, we confirm our analytical results with numerical simulations of both synthetically-generated and real-world networks.
Measuring maximal eigenvalue distribution of Wishart random matrices with coupled lasers
Fridman, Moti; Nixon, Micha; Friesem, Asher A; Davidson, Nir
2010-01-01
We determined the probability distribution of the combined output power from twenty five coupled fiber lasers and show that it agrees well with the Tracy-Widom and Majumdar-Vergassola distributions of the largest eigenvalue of Wishart random matrices with no fitting parameters. This was achieved with $500,000$ measurements of the combined output power from the fiber lasers, that continuously changes with variations of the fiber lasers lengths. We show experimentally that for small deviations of the combined output power over its mean value the Tracy-Widom distribution is correct, while for large deviations the Majumdar-Vergassola distribution is correct.
Measuring maximal eigenvalue distribution of Wishart random matrices with coupled lasers.
Fridman, Moti; Pugatch, Rami; Nixon, Micha; Friesem, Asher A; Davidson, Nir
2012-02-01
We determined the probability distribution of the combined output power from 25 coupled fiber lasers and show that it agrees well with the Tracy-Widom and Majumdar-Vergassola distributions of the largest eigenvalue of Wishart random matrices with no fitting parameters. This was achieved with 500,000 measurements of the combined output power from the fiber lasers, that continuously changes with variations of the fiber lasers lengths. We show experimentally that for small deviations of the combined output power over its mean value the Tracy-Widom distribution is correct, while for large deviations the Majumdar-Vergassola distribution is correct.
LingaRaj.K
2013-11-01
Full Text Available This paper attempts to undertake the study of maximizing the lifetime of Heterogeneous wireless sensor networks (WSNs. In wireless sensor networks, sensor nodes are typically power-constrained with limited lifetime, and thus it is necessary to know how long the network sustains its networking operations. Heterogeneous WSNs consists of different sensor devices with different capabilities. We can enhance the quality of monitoring in wireless sensor networks by increasing the coverage area. One of major issue in WSNs is finding maximum number of connected coverage. This paper proposed a Swarm Intelligence, Ant Colony Optimization (ACO based approach. Ant colony optimization algorithm provides a natural and intrinsic way of exploration of search space of coverage area. Ants communicate with their nest- mates using chemical scents known as pheromones, Based on Pheromone trail between sensor devices the shortest path is found. The methodology is based on finding the maximum number of connected covers that satisfy both sensing coverage and network connectivity. By finding the coverage area and sensing range, the network lifetime maximized and reduces the energy consumption
Application of random matrix theory to biological networks
Luo Feng [Department of Computer Science, Clemson University, 100 McAdams Hall, Clemson, SC 29634 (United States); Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhong Jianxin [Department of Physics, Xiangtan University, Hunan 411105 (China) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhongjn@ornl.gov; Yang Yunfeng [Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Scheuermann, Richard H. [Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhou Jizhong [Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019 (United States) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhouj@ornl.gov
2006-09-25
We show that spectral fluctuation of interaction matrices of a yeast protein-protein interaction network and a yeast metabolic network follows the description of the Gaussian orthogonal ensemble (GOE) of random matrix theory (RMT). Furthermore, we demonstrate that while the global biological networks evaluated belong to GOE, removal of interactions between constituents transitions the networks to systems of isolated modules described by the Poisson distribution. Our results indicate that although biological networks are very different from other complex systems at the molecular level, they display the same statistical properties at network scale. The transition point provides a new objective approach for the identification of functional modules.
Network Entropy Based on Topology Configuration and Its Computation to Random Networks
LI Ji; WANG Bing-Hong; WANG Wen-Xu; ZHOU Tao
2008-01-01
A definition of network entropy is presented, and as an example, the relationship between the value of network entropy of ER network model and the connect probability p as well as the total nodes N is discussed. The theoretical result and the simulation result based on the network entropy of the ER network are in agreement well with each other. The result indicated that different from the other network entropy reported before, the network entropy defined here has an obvious difference from different type of random networks or networks having different total nodes. Thus, this network entropy may portray the characters of complex networks better. It is also pointed out that, with the aid of network entropy defined, the concept of equilibrium networks and the concept of non-equilibrium networks may be introduced, and a quantitative measurement to describe the deviation to equilibrium state of a complex network is carried out.
Random matrix analysis for gene interaction networks in cancer cells
Kikkawa, Ayumi
2016-01-01
Motivation: The investigation of topological modifications of the gene interaction networks in cancer cells is essential for understanding the desease. We study gene interaction networks in various human cancer cells with the random matrix theory. This study is based on the Cancer Network Galaxy (TCNG) database which is the repository of huge gene interactions inferred by Bayesian network algorithms from 256 microarray experimental data downloaded from NCBI GEO. The original GEO data are provided by the high-throughput microarray expression experiments on various human cancer cells. We apply the random matrix theory to the computationally inferred gene interaction networks in TCNG in order to detect the universality in the topology of the gene interaction networks in cancer cells. Results: We found the universal behavior in almost one half of the 256 gene interaction networks in TCNG. The distribution of nearest neighbor level spacing of the gene interaction matrix becomes the Wigner distribution when the net...
Jain-Shing Liu
2010-01-01
Full Text Available MIMO links can significantly improve network throughput by supporting multiple concurrent data streams between a pair of nodes and suppressing wireless interference. In this paper, we study joint rate control, routing, and scheduling in MIMO-based multihop wireless networks, which are traditionally known as transport layer, network layer, and MAC layer issues, respectively. Our aim is to find a rate allocation along with a flow allocation and a transmission schedule for a set of end-to-end communication sessions so as to maximize the network throughput and also to achieve the proportional or weighted fairness among these sessions. To this end, we develop Transmission Mode Generating Algorithms (TMGAs, and Linear Programming- (LP- and Convex Programming- (CP- based optimization schemes for the MIMO networks. The performances of the proposed schemes are verified by simulation experiments, and the results show that the different schemes have different performance benefits when achieving a tradeoff between throughput and fairness.
Performance of wireless sensor networks under random node failures
Bradonjic, Milan [Los Alamos National Laboratory; Hagberg, Aric [Los Alamos National Laboratory; Feng, Pan [Los Alamos National Laboratory
2011-01-28
Networks are essential to the function of a modern society and the consequence of damages to a network can be large. Assessing network performance of a damaged network is an important step in network recovery and network design. Connectivity, distance between nodes, and alternative routes are some of the key indicators to network performance. In this paper, random geometric graph (RGG) is used with two types of node failure, uniform failure and localized failure. Since the network performance are multi-facet and assessment can be time constrained, we introduce four measures, which can be computed in polynomial time, to estimate performance of damaged RGG. Simulation experiments are conducted to investigate the deterioration of networks through a period of time. With the empirical results, the performance measures are analyzed and compared to provide understanding of different failure scenarios in a RGG.
Self-Organized Criticality in a Random Network Model
Nirei, Makoto
1998-01-01
A new model of self-organized criticality is defined by incorporating a random network model in order to explain endogenous complex fluctuations of economic aggregates. The model can feature many globally interactive systems such as economies or societies.
Softening in random networks of non-identical beams
Ban, Ehsan; Barocas, Victor H.; Shephard, Mark S.; Picu, R. Catalin
2016-02-01
Random fiber networks are assemblies of elastic elements connected in random configurations. They are used as models for a broad range of fibrous materials including biopolymer gels and synthetic nonwovens. Although the mechanics of networks made from the same type of fibers has been studied extensively, the behavior of composite systems of fibers with different properties has received less attention. In this work we numerically and theoretically study random networks of beams and springs of different mechanical properties. We observe that the overall network stiffness decreases on average as the variability of fiber stiffness increases, at constant mean fiber stiffness. Numerical results and analytical arguments show that for small variabilities in fiber stiffness the amount of network softening scales linearly with the variance of the fiber stiffness distribution. This result holds for any beam structure and is expected to apply to a broad range of materials including cellular solids.
Scaling of random walk betweenness in networks
Narayan, O
2016-01-01
The betweenness centrality of graphs using random walk paths instead of geodesics is studied. A scaling collapse with no adjustable parameters is obtained as the graph size $N$ is varied; the scaling curve depends on the graph model. A normalized random betweenness, that counts each walk passing through a node only once, is also defined. It is argued to be more useful and seen to have simpler scaling behavior. In particular, the probability for a random walk on a preferential attachment graph to pass through the root node is found to tend to unity as $N\\rightarrow\\infty.$
A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks
National Aeronautics and Space Administration — This paper describes a local and distributed expectation maximization algorithm for learning parameters of Gaussian mixture models (GMM) in large peer-to-peer (P2P)...
Selectivity and sparseness in randomly connected balanced networks.
Cengiz Pehlevan
Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.
Robustness of Dengue Complex Network under Targeted versus Random Attack
Hafiz Abid Mahmood Malik
2017-01-01
Full Text Available Dengue virus infection is one of those epidemic diseases that require much consideration in order to save the humankind from its unsafe impacts. According to the World Health Organization (WHO, 3.6 billion individuals are at risk because of the dengue virus sickness. Researchers are striving to comprehend the dengue threat. This study is a little commitment to those endeavors. To observe the robustness of the dengue network, we uprooted the links between nodes randomly and targeted by utilizing different centrality measures. The outcomes demonstrated that 5% targeted attack is equivalent to the result of 65% random assault, which showed the topology of this complex network validated a scale-free network instead of random network. Four centrality measures (Degree, Closeness, Betweenness, and Eigenvector have been ascertained to look for focal hubs. It has been observed through the results in this study that robustness of a node and links depends on topology of the network. The dengue epidemic network presented robust behaviour under random attack, and this network turned out to be more vulnerable when the hubs of higher degree have higher probability to fail. Moreover, representation of this network has been projected, and hub removal impact has been shown on the real map of Gombak (Malaysia.
Stability of biological networks as represented in Random Boolean Nets.
Slepoy, Alexander; Thompson, Marshall
2004-09-01
We explore stability of Random Boolean Networks as a model of biological interaction networks. We introduce surface-to-volume ratio as a measure of stability of the network. Surface is defined as the set of states within a basin of attraction that maps outside the basin by a bit-flip operation. Volume is defined as the total number of states in the basin. We report development of an object-oriented Boolean network analysis code (Attract) to investigate the structure of stable vs. unstable networks. We find two distinct types of stable networks. The first type is the nearly trivial stable network with a few basins of attraction. The second type contains many basins. We conclude that second type stable networks are extremely rare.
General clique percolation in random networks
Fan, Jingfang; Chen, Xiaosong
2014-07-01
A general (k,l) clique community of a network, which consists of adjacent k-cliques sharing at least l vertices with k-1\\ge l\\ge1 , is introduced. With the emergence of a giant (k,l) clique community in the network, there is a (k,l) clique percolation. Using the largest size jump Δ of the largest clique community during network evolution and the corresponding evolution step Tc, we study the general (k,l) clique percolation of the Erdős-Rényi network. We investigate the averages of Δ and Tc and their fluctuations for different network size N. The clique percolation can be identified by the power-law finite-size effects of the averages and root mean squares of fluctuation. The finite-size scaling distribution functions of fluctuations are calculated. The universality class of the (k,l) clique percolation is characterized by the critical exponents of power-law finite-size effects. Using Monte Carlo simulations, we find that the Erdős-Rényi network experiences a series of (k,l) clique percolation with (k,l)=(2,1),(3,1),(3,2),(4,1),(4,2),(4,3),(5,1) . We find that the critical exponents and therefore the universality class of the (k,l) clique percolation depend on clique connection index l, but are independent of clique size k.
A scaling law for random walks on networks
Perkins, Theodore J.; Foxall, Eric; Glass, Leon; Edwards, Roderick
2014-10-01
The dynamics of many natural and artificial systems are well described as random walks on a network: the stochastic behaviour of molecules, traffic patterns on the internet, fluctuations in stock prices and so on. The vast literature on random walks provides many tools for computing properties such as steady-state probabilities or expected hitting times. Previously, however, there has been no general theory describing the distribution of possible paths followed by a random walk. Here, we show that for any random walk on a finite network, there are precisely three mutually exclusive possibilities for the form of the path distribution: finite, stretched exponential and power law. The form of the distribution depends only on the structure of the network, while the stepping probabilities control the parameters of the distribution. We use our theory to explain path distributions in domains such as sports, music, nonlinear dynamics and stochastic chemical kinetics.
Research on Some Bus Transport Networks with Random Overlapping Clique Structure
YANG Xu-Hua; WANG Bo; WANG Wan-Liang; SUN You-Xian
2008-01-01
On the basis of investigating the statistical data of bus transport networks of three big cities in China, we propose that each bus route is a clique (maximal complete subgraph) and a bus transport network (BTN) consists of a lot of cliques, which intensively connect and overlap with each other. We study the network properties, which include the degree distribution, multiple edges' overlapping time distribution, distribution of the overlap size between any two overlapping cliques, distribution of the number of cliques that a node belongs to. Naturally, the cliques also constitute a network, with the overlapping nodes being their multiple links. We' also research its network properties such as degree distribution, clustering, average path length, and so on. We propose that a BTN has the properties of random clique increment and random overlapping clique, at the same time, a BTN is a small-world network with highly clique-clustered and highly clique-overlapped. Finally, we introduce a BTN evolution model, whose simulation results agree well with the statistical laws that emerge in real BTNs.
Timme, Marc; Geisel, Theo; Wolf, Fred
2006-03-01
We analyze the dynamics of networks of spiking neural oscillators. First, we present an exact linear stability theory of the synchronous state for networks of arbitrary connectivity. For general neuron rise functions, stability is determined by multiple operators, for which standard analysis is not suitable. We describe a general nonstandard solution to the multioperator problem. Subsequently, we derive a class of neuronal rise functions for which all stability operators become degenerate and standard eigenvalue analysis becomes a suitable tool. Interestingly, this class is found to consist of networks of leaky integrate-and-fire neurons. For random networks of inhibitory integrate-and-fire neurons, we then develop an analytical approach, based on the theory of random matrices, to precisely determine the eigenvalue distributions of the stability operators. This yields the asymptotic relaxation time for perturbations to the synchronous state which provides the characteristic time scale on which neurons can coordinate their activity in such networks. For networks with finite in-degree, i.e., finite number of presynaptic inputs per neuron, we find a speed limit to coordinating spiking activity. Even with arbitrarily strong interaction strengths neurons cannot synchronize faster than at a certain maximal speed determined by the typical in-degree.
Boyen, P.; Neven, F.; Valentim, F.L.; Dijk, van A.D.J.
2013-01-01
Correlated motif covering (CMC) is the problem of finding a set of motif pairs, i.e., pairs of patterns, in the sequences of proteins from a protein-protein interaction network (PPI-network) that describe the interactions in the network as concisely as possible. In other words, a perfect solution fo
Shuiqing Yu
2013-01-01
Full Text Available This paper investigates the dynamic output feedback control for nonlinear networked control systems with both random packet dropout and random delay. Random packet dropout and random delay are modeled as two independent random variables. An observer-based dynamic output feedback controller is designed based upon the Lyapunov theory. The quantitative relationship of the dropout rate, transition probability matrix, and nonlinear level is derived by solving a set of linear matrix inequalities. Finally, an example is presented to illustrate the effectiveness of the proposed method.
Random field Ising model and community structure in complex networks
Son, S.-W.; Jeong, H.; Noh, J. D.
2006-04-01
We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)
Random walks on activity-driven networks with attractiveness
Alessandretti, Laura; Sun, Kaiyuan; Baronchelli, Andrea; Perra, Nicola
2017-05-01
Virtually all real-world networks are dynamical entities. In social networks, the propensity of nodes to engage in social interactions (activity) and their chances to be selected by active nodes (attractiveness) are heterogeneously distributed. Here, we present a time-varying network model where each node and the dynamical formation of ties are characterized by these two features. We study how these properties affect random-walk processes unfolding on the network when the time scales describing the process and the network evolution are comparable. We derive analytical solutions for the stationary state and the mean first-passage time of the process, and we study cases informed by empirical observations of social networks. Our work shows that previously disregarded properties of real social systems, such as heterogeneous distributions of activity and attractiveness as well as the correlations between them, substantially affect the dynamical process unfolding on the network.
Modeling of Random Delays in Networked Control Systems
Yuan Ge
2013-01-01
Full Text Available In networked control systems (NCSs, the presence of communication networks in control loops causes many imperfections such as random delays, packet losses, multipacket transmission, and packet disordering. In fact, random delays are usually the most important problems and challenges in NCSs because, to some extent, other problems are often caused by random delays. In order to compensate for random delays which may lead to performance degradation and instability of NCSs, it is necessary to establish the mathematical model of random delays before compensation. In this paper, four major delay models are surveyed including constant delay model, mutually independent stochastic delay model, Markov chain model, and hidden Markov model. In each delay model, some promising compensation methods of delays are also addressed.
Throughput vs. Delay in Lossy Wireless Mesh Networks with Random Linear Network Coding
Hundebøll, Martin; Pahlevani, Peyman; Roetter, Daniel Enrique Lucani; Fitzek, Frank
2014-01-01
This work proposes a new protocol applying on–the–fly random linear network coding in wireless mesh net-works. The protocol provides increased reliability, low delay,and high throughput to the upper layers, while being obliviousto their specific requirements. This seemingly conflicting goalsare achieved by design, using an on–the–fly network codingstrategy. Our protocol also exploits relay nodes to increasethe overall performance of individual links. Since our protocolnaturally masks random p...
Iva Klimešová
2017-03-01
Full Text Available Background: The positive effects of caffeine supplementation on strength-power and endurance performance in healthy athletes have been demonstrated in many studies. A possible mechanism for its ergogenic effect relates to its influence on the central nervous system. Post-traumatic complications in cervical spinal cord injury affect almost all body systems including the nervous system. For this reason, we expect that caffeine will have a different effect of performance in the group of athletes with spinal cord injuries. Objective: To examine the effects of caffeine supplementation on maximal aerobic power in elite wheelchair rugby players. Methods: Seven elite male wheelchair rugby players with complete cervical-level SCI (C4-Th1 were recruited (mean age: 28 ± 5.42 years; mean body mass index: 26 ± 2.84 kg/m2. The effect of caffeine was assessed by an incremental arm ergometer test until volitional exhaustion. The maximal oxygen uptake (VO2max/kg, maximum power (W max/kg, peak heart rate (HR peak, and intensity of perceived exertion (RPE were measured. Participants performed the test twice with a two-week washout period. One hour before each exercise test subjects ingested a capsule of placebo or caffeine (3 mg per kg of body weight. The tests were applied in a double-blind, randomized, repeated-measures, and cross-over design. Wheelchair rugby players were chosen because of the expected high homogeneity of participants - in terms of the type and degree of disability, gender, and age of the players. Results: The monitored parameters were not significantly influenced by caffeine intervention as compared to placebo: VO2max/kg (p = .40, W max/kg (p = .34, HR peak (p = .50 and RPE (p = .50. Conclusions: The current findings suggest that a caffeine dose of 3 mg/kg body mass does not improve oxygen uptake and maximal power in elite wheelchair rugby players.
Dual random circuit breaker network model with equivalent thermal circuit network
Kim, Kwanyong; Yoon, Seong Jun; Choi, Woo Young
2014-02-01
A SPICE-based dual random circuit breaker (RCB) network model with an equivalent thermal circuit network has been proposed in order to emulate resistance switching (RS) of unipolar resistive random access memory (RRAM). The dual RCB network model consists of the electrical RCB network model for the forming and set operations and the equivalent thermal circuit network model for the reset operation. In addition, the proposed model can explain the effects of heat dissipation on the memory and threshold RS with the variation in electrode thickness.
Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks
Heide, Janus; Zhang, Qi; Fitzek, Frank
2013-01-01
This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [1] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant...
Paul D. McNellis
1998-03-01
Full Text Available This paper examines money demand and the seigniorage-maximizing inflation rates in Chile, Colombia, Mexico, and Peru with error-correction models (ECM and artificial neural network (ANN methods. The purpose is to approximate more accurately the "true" underlying non-linear functional forms for the long-run equilibrium demand for money, to estimate the learning process in short-run monthly adjustment of money stocks, and to obtain better estimates of the seigniorage-maximizing rates of inflation in a region characterized by macroeconomic instability. Unlike most previous studies, this paper explicitly incorporates parallel-market exchange-rate uncertainty in the short-run demand for money. The ANN model shows that there are various degrees of non-linearity in the long-run demand for money, with Chile ranking highest. However, all of the countries examined showed that a relatively simple ANN model outperformed the ECM for the error-correction or learning process in the short-run demand for money. In one case, Peru, the ANN more than doubled the explanatory power of the linear ECM. The ANN approach also shows that uncertainty plays the dominant role in short-run money demand, and that the seigniorage-maximizing inflation rates are much lower that the predictions of previous studies. This paper examines money demand and the seigniorage-maximizing inflation rates in Chile, Colombia, Mexico, and Peru with error-correction models (ECM and artificial neural network (ANN methods. The purpose is to approximate more accurately the "true" underlying non-linear functional forms for the long-run equilibrium demand for money, to estimate the learning process in short-run monthly adjustment of money stocks, and to obtain better estimates of the seigniorage-maximizing rates of inflation in a region characterized by macroeconomic instability. Unlike most previous studies, this paper explicitly incorporates parallel-market exchange-rate uncertainty in the short
Community Detection in Sparse Random Networks
2013-08-13
hypothesis testing, Erdös- Rényi random graph, scan statistic, planted clique problem, largest connected component. 1 Introduction Community detection...very special case where p1 = 1, meaning that the anomalous subgraph S is a clique . This is the case that Sun and Nobel (2008) consider motivated by a...data mining application. We confirmed the intuition that the clique test, which is based on the size of the largest clique , is asymptotically minimax
COMPARING RANDOM AND REGULAR NETWORK RESILIANCE AGAINST RANDOM ATTACK ON THEIR NODES
Ilya B. Gertsbakh
2013-01-01
Full Text Available We consider a family of connected networks whose nodes are subject to randomfailures ("attacks". Node failure means elimination of all links incident to the attacked node.Each node, independently of others, fails with probability q . Network failure (DOWN state isdefined as the situation when the largest connected component has "critical" size L . Wecompare the probabilistic resilience of a simulated network (obtained by a preferentialassignment-type algorithm versus a regular network having the same number of nodes and links.This comparison is carried out for three types of regular networks: the dodecahedron (20 nodes,30 links, square torus-type grid (25 nodes, 50 links and five-dimensional cubic network (32nodes, 80 links. For all three types of networks the critical value of L was approximately equalone third of the nodes. It turns out that the network with regular structure and node degree 5 d has higher resilience than a network with random structure, i.e. a regular network has smallerDOWN probability than a random network for the same q value and for the same number offailed nodes x It turns out, however, that the advantage of a regular network over a randomnetwork vanishes with the decrease of the average node degree. So, for 3, d random networkand its regular counterpart (so-called dodecahedron have approximately the same resilience. Ourinvestigation is based on comparing the so-called cumulative D-spectra and the network DOWNprobabilities as a function of node failure probability . q
Selective randomized load balancing and mesh networks with changing demands
Shepherd, F. B.; Winzer, P. J.
2006-05-01
We consider the problem of building cost-effective networks that are robust to dynamic changes in demand patterns. We compare several architectures using demand-oblivious routing strategies. Traditional approaches include single-hop architectures based on a (static or dynamic) circuit-switched core infrastructure and multihop (packet-switched) architectures based on point-to-point circuits in the core. To address demand uncertainty, we seek minimum cost networks that can carry the class of hose demand matrices. Apart from shortest-path routing, Valiant's randomized load balancing (RLB), and virtual private network (VPN) tree routing, we propose a third, highly attractive approach: selective randomized load balancing (SRLB). This is a blend of dual-hop hub routing and randomized load balancing that combines the advantages of both architectures in terms of network cost, delay, and delay jitter. In particular, we give empirical analyses for the cost (in terms of transport and switching equipment) for the discussed architectures, based on three representative carrier networks. Of these three networks, SRLB maintains the resilience properties of RLB while achieving significant cost reduction over all other architectures, including RLB and multihop Internet protocol/multiprotocol label switching (IP/MPLS) networks using VPN-tree routing.
New Approaches to Maximizing Thermo-oxidation Resistance of Polycyanurate Networks
2014-08-13
thermo -oxidation resistance of polycyanurate networks 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER Andrew J...enhanced thermo -oxidative stability compared to epoxy resins. Recent structure-property investigations in polycyanurate networks have revealed new...insights into the relationship between the chemical moieties found in the networks and the corresponding levels of thermo -oxidative resistance. In
Enhanced networked server management with random remote backups
Kim, Song-Kyoo
2003-08-01
In this paper, the model is focused on available server management in network environments. The (remote) backup servers are hooked up by VPN (Virtual Private Network) and replace broken main severs immediately. A virtual private network (VPN) is a way to use a public network infrastructure and hooks up long-distance servers within a single network infrastructure. The servers can be represent as "machines" and then the system deals with main unreliable and random auxiliary spare (remote backup) machines. When the system performs a mandatory routine maintenance, auxiliary machines are being used for backups during idle periods. Unlike other existing models, the availability of auxiliary machines is changed for each activation in this enhanced model. Analytically tractable results are obtained by using several mathematical techniques and the results are demonstrated in the framework of optimized networked server allocation problems.
Cascading Node Failure with Continuous States in Random Geometric Networks
Kamran, Khashayar
2016-01-01
The increasing complexity and interdependency of today's networks highlight the importance of studying network robustness to failure and attacks. Many large-scale networks are prone to cascading effects where a limited number of initial failures (due to attacks, natural hazards or resource depletion) propagate through a dependent mechanism, ultimately leading to a global failure scenario where a substantial fraction of the network loses its functionality. These cascading failure scenarios often take place in networks which are embedded in space and constrained by geometry. Building on previous results on cascading failure in random geometric networks, we introduce and analyze a continuous cascading failure model where a node has an initial continuously-valued state, and fails if the aggregate state of its neighbors fall below a threshold. Within this model, we derive analytical conditions for the occurrence and non-occurrence of cascading node failure, respectively.
Pseudo Random Number Generator Based on Back Propagation Neural Network
WANG Bang-ju; WANG Yu-hua; NIU Li-ping; ZHANG Huan-guo
2007-01-01
Random numbers play an increasingly important role in secure wire and wireless communication.Thus the design quality of random number generator(RNG) is significant in information security.A novel pseudo RNG is proposed for improving the security of network communication.The back propagation neural network(BPNN) is nonlinear,which can be used to improve the traditional RNG.The novel pseudo RNG is based on BPNN techniques.The result of test suites standardized by the U.S shows that the RNG can satisfy the security of communication.
Random resistor network model of minimal conductivity in graphene.
Cheianov, Vadim V; Fal'ko, Vladimir I; Altshuler, Boris L; Aleiner, Igor L
2007-10-26
Transport in undoped graphene is related to percolating current patterns in the networks of n- and p-type regions reflecting the strong bipolar charge density fluctuations. Finite transparency of the p-n junctions is vital in establishing the macroscopic conductivity. We propose a random resistor network model to analyze scaling dependencies of the conductance on the doping and disorder, the quantum magnetoresistance and the corresponding dephasing rate.
Characteristic times of biased random walks on complex networks.
Bonaventura, Moreno; Nicosia, Vincenzo; Latora, Vito
2014-01-01
We consider degree-biased random walkers whose probability to move from a node to one of its neighbors of degree k is proportional to k(α), where α is a tuning parameter. We study both numerically and analytically three types of characteristic times, namely (i) the time the walker needs to come back to the starting node, (ii) the time it takes to visit a given node for the first time, and (iii) the time it takes to visit all the nodes of the network. We consider a large data set of real-world networks and we show that the value of α which minimizes the three characteristic times differs from the value α(min)=-1 analytically found for uncorrelated networks in the mean-field approximation. In addition to this, we found that assortative networks have preferentially a value of α(min) in the range [-1,-0.5], while disassortative networks have α(min) in the range [-0.5,0]. We derive an analytical relation between the degree correlation exponent ν and the optimal bias value α(min), which works well for real-world assortative networks. When only local information is available, degree-biased random walks can guarantee smaller characteristic times than the classical unbiased random walks by means of an appropriate tuning of the motion bias.
Listening to the noise: random fluctuations reveal gene network parameters
Munsky, Brian [Los Alamos National Laboratory; Khammash, Mustafa [UCSB
2009-01-01
The cellular environment is abuzz with noise. The origin of this noise is attributed to the inherent random motion of reacting molecules that take part in gene expression and post expression interactions. In this noisy environment, clonal populations of cells exhibit cell-to-cell variability that frequently manifests as significant phenotypic differences within the cellular population. The stochastic fluctuations in cellular constituents induced by noise can be measured and their statistics quantified. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. This establishes a potentially powerful approach for the identification of gene networks and offers a new window into the workings of these networks.
Mary P McGowan
Full Text Available OBJECTIVES: Mipomersen, an antisense oligonucleotide targeting apolipoprotein B synthesis, significantly reduces LDL-C and other atherogenic lipoproteins in familial hypercholesterolemia when added to ongoing maximally tolerated lipid-lowering therapy. Safety and efficacy of mipomersen in patients with severe hypercholesterolemia was evaluated. METHODS AND RESULTS: Randomized, double-blind, placebo-controlled, multicenter trial. Patients (n = 58 were ≥18 years with LDL-C ≥7.8 mmol/L or LDL-C ≥5.1 mmol/L plus CHD disease, on maximally tolerated lipid-lowering therapy that excluded apheresis. Weekly subcutaneous injections of mipomersen 200 mg (n = 39 or placebo (n = 19 were added to lipid-lowering therapy for 26 weeks. MAIN OUTCOME: percent reduction in LDL-C from baseline to 2 weeks after the last dose of treatment. Mipomersen (n = 27 reduced LDL-C by 36%, from a baseline of 7.2 mmol/L, for a mean absolute reduction of 2.6 mmol/L. Conversely, mean LDL-C increased 13% in placebo (n = 18 from a baseline of 6.5 mmol/L (mipomersen vs placebo p<0.001. Mipomersen produced statistically significant (p<0.001 reductions in apolipoprotein B and lipoprotein(a, with no change in high-density lipoprotein cholesterol. Mild-to-moderate injection site reactions were the most frequently reported adverse events with mipomersen. Mild-to-moderate flu-like symptoms were reported more often with mipomersen. Alanine transaminase increase, aspartate transaminase increase, and hepatic steatosis occurred in 21%, 13% and 13% of mipomersen treated patients, respectively. Adverse events by category for the placebo and mipomersen groups respectively were: total adverse events, 16(84.2%, 39(100%; serious adverse events, 0(0%, 6(15.4%; discontinuations due to adverse events, 1(5.3%, 8(20.5% and cardiac adverse events, 1(5.3%, 5(12.8%. CONCLUSION: Mipomersen significantly reduced LDL-C, apolipoprotein B, total cholesterol and non
Hugo Fort
2015-11-01
Full Text Available Mutualistic networks in nature are widespread and play a key role in generating the diversity of life on Earth. They constitute an interdisciplinary field where physicists, biologists and computer scientists work together. Plant-pollinator mutualisms in particular form complex networks of interdependence between often hundreds of species. Understanding the architecture of these networks is of paramount importance for assessing the robustness of the corresponding communities to global change and management strategies. Advances in this problem are currently limited mainly due to the lack of methodological tools to deal with the intrinsic complexity of mutualisms, as well as the scarcity and incompleteness of available empirical data. One way to uncover the structure underlying complex networks is to employ information theoretical statistical inference methods, such as the expectation maximization (EM algorithm. In particular, such an approach can be used to cluster the nodes of a network based on the similarity of their node neighborhoods. Here, we show how to connect network theory with the classical ecological niche theory for mutualistic plant-pollinator webs by using the EM algorithm. We apply EM to classify the nodes of an extensive collection of mutualistic plant-pollinator networks according to their connection similarity. We find that EM recovers largely the same clustering of the species as an alternative recently proposed method based on resource overlap, where one considers each party as a consuming resource for the other party (plants providing food to animals, while animals assist the reproduction of plants. Furthermore, using the EM algorithm, we can obtain a sequence of successfully-refined classifications that enables us to identify the fine-structure of the ecological network and understand better the niche distribution both for plants and animals. This is an example of how information theoretical methods help to systematize and
Optimal evolution on random networks: from social to airports networks
Curty, P
2005-01-01
Social movements, neurons in the brain or even industrial suppliers are best described by agents evolving on networks with basic interaction rules. In these real systems, the connectivity between agents corresponds to the most efficient state of the system. The new idea is that connectivity adjusts itself because of two opposite tendencies: information percolation, decision making or coordination are better when the network connectivity is small. When agents have many connections, the opinion of a person or the state of a neuron tend to freeze: agents find always a minority among their advisors to support their opinion. A general and new model reproduces these features showing a clear transition between the two tendencies at some critical connectivity. Depending on the noise, the evolution of the system is optimal at a precise critical connectivity since, away from this critical point, the system always ends up in a static phase. When the error tolerance is very small, the optimal connectivity becomes very la...
A Novel Top-k Strategy for Influence Maximization in Complex Networks with Community Structure.
Jia-Lin He
Full Text Available In complex networks, it is of great theoretical and practical significance to identify a set of critical spreaders which help to control the spreading process. Some classic methods are proposed to identify multiple spreaders. However, they sometimes have limitations for the networks with community structure because many chosen spreaders may be clustered in a community. In this paper, we suggest a novel method to identify multiple spreaders from communities in a balanced way. The network is first divided into a great many super nodes and then k spreaders are selected from these super nodes. Experimental results on real and synthetic networks with community structure show that our method outperforms the classic methods for degree centrality, k-core and ClusterRank in most cases.
R. A. Eminov,
2013-03-01
Full Text Available The existing actual material on experimental assessment of positioning error in VRS GPS networks is analyzed where the mobile receiver is provided with virtual reference station. The method of highly informative zone is suggested for removal of initial uncertainty in reference station selection with the aim to develop minimal GPS network consisting of three reference stations. Methodical recommendations and directions are given for the suggested method application.
Maps of random walks on complex networks reveal community structure.
Rosvall, Martin; Bergstrom, Carl T
2008-01-29
To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.
Dense graphlet statistics of protein interaction and random networks.
Colak, R; Hormozdiari, F; Moser, F; Schönhuth, A; Holman, J; Ester, M; Sahinalp, S C
2009-01-01
Understanding evolutionary dynamics from a systemic point of view crucially depends on knowledge about how evolution affects size and structure of the organisms' functional building blocks (modules). It has been recently reported that statistics over sparse PPI graphlets can robustly monitor such evolutionary changes. However, there is abundant evidence that in PPI networks modules can be identified with highly interconnected (dense) and/or bipartite subgraphs. We count such dense graphlets in PPI networks by employing recently developed search strategies that render related inference problems tractable. We demonstrate that corresponding counting statistics differ significantly between prokaryotes and eukaryotes as well as between "real" PPI networks and scale free network emulators. We also prove that another class of emulators, the low-dimensional geometric random graphs (GRGs) cannot contain a specific type of motifs, complete bipartite graphs, which are abundant in PPI networks.
Extraordinary variability and sharp transitions in a maximally frustrated dynamic network
Liu, Wenjia; Schmittmann, Beate; Zia, R. K. P.
2013-03-01
Most previous studies of complex networks have focused on single, static networks. However, in the real world, networks are dynamic and interconnected. Inspired by the presence of extroverts and introverts in the general population, we investigate a highly simplified model of a social network, involving two types of nodes: one preferring the highest degree possible, and one preferring no connections whatsoever. There are only two control parameters in the model: the number of ``introvert'' and ``extrovert'' nodes, NI and NE. Our key findings are as follows: As a function of NI and NE, the system exhibits a highly unusual transition, displaying extraordinary fluctuations (as in 2nd order transitions) and discontinuous jumps (characteristic of 1st order transitions). Most remarkably, the system can be described by an Ising-like Hamiltonian with long-range multi-spin interactions and some of its properties can be obtained analytically. This is in stark contrast with other dynamic network models which rely almost exclusively on simulations. NSF-DMR-1005417/1244666 and and ICTAS Virginia Tech
Random Time Identity Based Firewall In Mobile Ad hoc Networks
Suman, Patel, R. B.; Singh, Parvinder
2010-11-01
A mobile ad hoc network (MANET) is a self-organizing network of mobile routers and associated hosts connected by wireless links. MANETs are highly flexible and adaptable but at the same time are highly prone to security risks due to the open medium, dynamically changing network topology, cooperative algorithms, and lack of centralized control. Firewall is an effective means of protecting a local network from network-based security threats and forms a key component in MANET security architecture. This paper presents a review of firewall implementation techniques in MANETs and their relative merits and demerits. A new approach is proposed to select MANET nodes at random for firewall implementation. This approach randomly select a new node as firewall after fixed time and based on critical value of certain parameters like power backup. This approach effectively balances power and resource utilization of entire MANET because responsibility of implementing firewall is equally shared among all the nodes. At the same time it ensures improved security for MANETs from outside attacks as intruder will not be able to find out the entry point in MANET due to the random selection of nodes for firewall implementation.
Navigation by anomalous random walks on complex networks
Weng, Tongfeng; Zhang, Jie; Khajehnejad, Moein; Small, Michael; Zheng, Rui; Hui, Pan
2016-11-01
Anomalous random walks having long-range jumps are a critical branch of dynamical processes on networks, which can model a number of search and transport processes. However, traditional measurements based on mean first passage time are not useful as they fail to characterize the cost associated with each jump. Here we introduce a new concept of mean first traverse distance (MFTD) to characterize anomalous random walks that represents the expected traverse distance taken by walkers searching from source node to target node, and we provide a procedure for calculating the MFTD between two nodes. We use Lévy walks on networks as an example, and demonstrate that the proposed approach can unravel the interplay between diffusion dynamics of Lévy walks and the underlying network structure. Moreover, applying our framework to the famous PageRank search, we show how to inform the optimality of the PageRank search. The framework for analyzing anomalous random walks on complex networks offers a useful new paradigm to understand the dynamics of anomalous diffusion processes, and provides a unified scheme to characterize search and transport processes on networks.
Arnon, Shlomi
2010-09-01
The availability of sophisticated and low-cost hardware on a single chip, for example, CMOS cameras, CPU, DSP, processors and communication transceivers, optics, microfluidics, and micromechanics, has fostered the development of system-on-chip (SoC) technology, such as lab-on-chip or wireless multimedia sensor networks (WMSNs). WMSNs are networks of wirelessly interconnected devices on a chip that are able to ubiquitously retrieve multimedia content such as video from the environment and transfer it to a central location for additional processing. In this paper, we study WMSNs that include an optical wireless communication transceiver that uses light to transmit the information. One of the primary challenges in SoC design is to attain adequate resources like energy harvesting using solar cells in addition to imaging and communication capabilities, all within stringent spatial limitations while maximizing system performances. There is an inevitable trade-off between enhancing the imaging resolution and the expense of reducing communication capacity and energy harvesting capabilities, on one hand, and increasing the communication or the solar cell size to the detriment of the imaging resolution, on the other hand. We study these trade-offs, derive a mathematical model to maximize the resolution of the imaging system, and present a numerical example that demonstrates maximum imaging resolution. Our results indicate that an eighth-order polynomial with only two constants provides the required area allocation between the different functionalities.
Thi-Tham Nguyen
2014-03-01
Full Text Available This paper proposes a practical low-complexity MAC (medium access control scheme for quality of service (QoS-aware and cluster-based underwater acoustic sensor networks (UASN, in which the provision of differentiated QoS is required. In such a network, underwater sensors (U-sensor in a cluster are divided into several classes, each of which has a different QoS requirement. The major problem considered in this paper is the maximization of the number of nodes that a cluster can accommodate while still providing the required QoS for each class in terms of the PDR (packet delivery ratio. In order to address the problem, we first estimate the packet delivery probability (PDP and use it to formulate an optimization problem to determine the optimal value of the maximum packet retransmissions for each QoS class. The custom greedy and interior-point algorithms are used to find the optimal solutions, which are verified by extensive simulations. The simulation results show that, by solving the proposed optimization problem, the supportable number of underwater sensor nodes can be maximized while satisfying the QoS requirements for each class.
Nguyen, Thi-Tham; Le, Duc Van; Yoon, Seokhoon
2014-03-07
This paper proposes a practical low-complexity MAC (medium access control) scheme for quality of service (QoS)-aware and cluster-based underwater acoustic sensor networks (UASN), in which the provision of differentiated QoS is required. In such a network, underwater sensors (U-sensor) in a cluster are divided into several classes, each of which has a different QoS requirement. The major problem considered in this paper is the maximization of the number of nodes that a cluster can accommodate while still providing the required QoS for each class in terms of the PDR (packet delivery ratio). In order to address the problem, we first estimate the packet delivery probability (PDP) and use it to formulate an optimization problem to determine the optimal value of the maximum packet retransmissions for each QoS class. The custom greedy and interior-point algorithms are used to find the optimal solutions, which are verified by extensive simulations. The simulation results show that, by solving the proposed optimization problem, the supportable number of underwater sensor nodes can be maximized while satisfying the QoS requirements for each class.
Stability of Random Networks under Evolution of Attack and Repair
CHI Li-Ping; YANG Chun-Bin; CAI Xu
2006-01-01
With a simple model, we study the stability of random networks under the evolution of attack and repair. We introduce a new quantity, i.e. invulnerability I(s), to describe the stability of the system. It is found that the network can evolve to a stationary state. The stationary value Ic has a power-law dependence on the initial average degree , with the slope about -1.5. In the stationary state, the degree distribution is a normal distribution, rather than a typical Poisson distribution for general random graphs. The clustering coefficient in the stationary state is much larger than that in the initial state. The stability of the network depends only on the initial average degree , which increases rapidly with the decrease of .
A Distributed MAC Protocol for Cooperation in Random Access Networks
Böcherer, Georg
2008-01-01
WLAN is one of the most successful applications of wireless communications in daily life because of low cost and ease of deployment. The enabling technique for this success is the use of random access schemes for the wireless channel. Random access requires minimal coordination between the nodes, which considerably reduces the cost of the infrastructure. Recently, cooperative communication in wireless networks has been of increasing interest because it promises higher rates and reliability. An additional MAC overhead is necessary to coordinate the nodes to allow cooperation and this overhead can possibly cancel out the cooperative benefits. In this work, a completely distributed protocol is proposed that allows nodes in the network to cooperate via Two-Hop and Decode-and-Forward for transmitting their data to a common gateway node. It is shown that high throughput gains are obtained in terms of the individual throughput that can be guaranteed to any node in the network. These results are validated by Monte Ca...
Visual Saliency and Attention as Random Walks on Complex Networks
Costa, L F
2006-01-01
The unmatched versatility of vision in mammals is totally dependent on purposive eye movements and selective attention guided by saliencies in the presented images. The current article shows how concepts and tools from the areas of random walks, Markov chains, complex networks and artificial image analysis can be naturally combined in order to provide a unified and biologically plausible model for saliency detection and visual attention, which become indistinguishable in the process. Images are converted into complex networks by considering pixels as nodes while connections are established in terms of fields of influence defined by visual features such as tangent fields induced by luminance contrasts, distance, and size. Random walks are performed on such networks in order to emulate attentional shifts and even eye movements in the case of large shapes, and the frequency of visits to each node is conveniently obtained from the eigenequation defined by the stochastic matrix associated to the respectively drive...
Computer simulation of randomly cross-linked polymer networks
Williams, T P
2002-01-01
In this work, Monte Carlo and Stochastic Dynamics computer simulations of mesoscale model randomly cross-linked networks were undertaken. Task parallel implementations of the lattice Monte Carlo Bond Fluctuation model and Kremer-Grest Stochastic Dynamics bead-spring continuum model were designed and used for this purpose. Lattice and continuum precursor melt systems were prepared and then cross-linked to varying degrees. The resultant networks were used to study structural changes during deformation and relaxation dynamics. The effects of a random network topology featuring a polydisperse distribution of strand lengths and an abundance of pendant chain ends, were qualitatively compared to recent published work. A preliminary investigation into the effects of temperature on the structural and dynamical properties was also undertaken. Structural changes during isotropic swelling and uniaxial deformation, revealed a pronounced non-affine deformation dependant on the degree of cross-linking. Fractal heterogeneiti...
Maximizing Throughput of SW ARQ with Network Coding through Forward Error Correction
Farouq M. Aliyu
2015-06-01
Full Text Available Over the years, several techniques for improving throughput of wireless communication have been developed in order to cater for the ever increasing demand of high speed network service. However, these techniques can only give little improvement in performance because packets have to be delivered as is. As such researchers have begun thinking outside the box by proposing ideas that require relay nodes to temper packets’ contents in order to improve the throughput of a network. One of the state of the art techniques in this field is called Network Coding (NC. NC is a state of the art technique that allows relay nodes linearly combine two or more packets in a way they can be recovered upon reaching their destination. However, increasing packet size increases possibility of error affecting it. In this paper, the authors decide to investigate whether adding data recovery technique can improve the performance of a network that uses network coding, if it can, by how much can it? Is it worth the trouble? In order to answer these questions, the authors carried out a quantitative analysis of throughput in a Stop-and-Wait Automatic Repeat reQuest (SW-ARQ data transmission system with Network Coding (NC and Forward Error Correction (FEC. Vandermonde matrix is chosen as the coding technique for this research because it has both NC and data recovery characteristics. Python programming language is used to develop three Discrete Event Simulations: SW-ARQ without any NC, SW-ARQ with NC and SW-ARQ with NC and FEC. The obtained results show that SW-ARQ with NC and FEC is superior to traditional SW-ARQ in terms of throughput, especially in channels with high error rates.
Exact Modeling of the Performance of Random Linear Network Coding in Finite-buffer Networks
Torabkhani, Nima; Beirami, Ahmad; Fekri, Faramarz
2011-01-01
In this paper, we present an exact model for the analysis of the performance of Random Linear Network Coding (RLNC) in wired erasure networks with finite buffers. In such networks, packets are delayed due to either random link erasures or blocking by full buffers. We assert that because of RLNC, the content of buffers have dependencies which cannot be captured directly using the classical queueing theoretical models. We model the performance of the network using Markov chains by a careful derivation of the buffer occupancy states and their transition rules. We verify by simulations that the proposed framework results in an accurate measure of the network throughput offered by RLNC. Further, we introduce a class of acyclic networks for which the number of state variables is significantly reduced.
Weighted Scaling in Non-growth Random Networks
陈光; 杨旭华; 徐新黎
2012-01-01
We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in nongrowth random networks. In this model, we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it. The network evolves according to a vertex strength preferential selection mechanism. During the evolution process, the network always holds its totM number of vertices and its total number of single-edges constantly. We show analytically and numerically that a network will form steady scale-free distributions with our model. The results show that a weighted non-growth random network can evolve into scMe-free state. It is interesting that the network also obtains the character of an exponential edge weight distribution. Namely, coexistence of scale-free distribution and exponential distribution emerges.
2014-02-26
Reliability and Maintainability Symposium, Anaheim, California , USA, 1994, pp. 442–448. [46] I.R. Chen, F.B. Bastani, T.W. Tsao, On the reliability of AI...Cunha, O.C. Duarte , G. Pujolle, Trust management in mobile ad hoc networks using a scalable maturity- based model, IEEE Trans. Netw. Serv. Manage. 7
Wave speed in excitable random networks with spatially constrained connections.
Nikita Vladimirov
Full Text Available Very fast oscillations (VFO in neocortex are widely observed before epileptic seizures, and there is growing evidence that they are caused by networks of pyramidal neurons connected by gap junctions between their axons. We are motivated by the spatio-temporal waves of activity recorded using electrocorticography (ECoG, and study the speed of activity propagation through a network of neurons axonally coupled by gap junctions. We simulate wave propagation by excitable cellular automata (CA on random (Erdös-Rényi networks of special type, with spatially constrained connections. From the cellular automaton model, we derive a mean field theory to predict wave propagation. The governing equation resolved by the Fisher-Kolmogorov PDE fails to describe wave speed. A new (hyperbolic PDE is suggested, which provides adequate wave speed v( that saturates with network degree , in agreement with intuitive expectations and CA simulations. We further show that the maximum length of connection is a much better predictor of the wave speed than the mean length. When tested in networks with various degree distributions, wave speeds are found to strongly depend on the ratio of network moments / rather than on mean degree , which is explained by general network theory. The wave speeds are strikingly similar in a diverse set of networks, including regular, Poisson, exponential and power law distributions, supporting our theory for various network topologies. Our results suggest practical predictions for networks of electrically coupled neurons, and our mean field method can be readily applied for a wide class of similar problems, such as spread of epidemics through spatial networks.
Timofey, Sizonenko; Karsanina, Marina; Byuk, Irina; Gerke, Kirill
2016-04-01
To characterize pore structure relevant to single and multi-phase flow modelling it is of special interest to extract topology of the pore space. This is usually achieved using so-called pore-network models. Such models are useful not only to characterize pore space and pore size distributions, but also provide means to simulate flow and transport with very limited computational resources compared to other pore-scale modelling techniques. The main drawback of the pore-network approach is that they have first to simplify the pore space geometry. This crucial step is both time consuming and prone to numerous errors. Two most popular methods based on median axis or inscribed maximal balls have their own strong sides and disadvantages. To address aforementioned problems related to pore-network extraction here we propose a novel method utilizing the advantages of both popular approaches. Combining two algorithms resulted in much faster and robust extraction methodology. Moreover, we have found that accurate topology representation requires extension of the conventional pore-body and pore-throat classification. We test our new methodology using pore structures with "analytical solutions" such as different sphere packs. In addition, we rigorously compare it against inscribed maximal balls methodology's results using numerous 3D images of sandstone and carbonate rocks, soils and some other porous materials. Another verification includes permeability calculations which are also compared both against lab data and voxel based pore-scale modelling simulations. This work was partially supported by RFBR grant 15-34-20989 (X-ray tomography and image fusion) and RSF grant 14-17-00658 (image segmentation and pore-scale modelling).
Chaos synchronization of two stochastically coupled random Boolean networks
Hung, Y.-C. [Department of Physics, National Sun Yat-sen University, Kaohsiung, Taiwan (China) and Nonlinear Science Group, Department of Physics, National Kaohsiung Normal University, Kaohsiung, Taiwan (China)]. E-mail: d9123801@student.nsysu.edu.tw; Ho, M.-C. [Nonlinear Science Group, Department of Physics, National Kaohsiung Normal University, Kaohsiung, Taiwan (China)]. E-mail: t1603@nknucc.nknu.edu.tw; Lih, J.-S. [Department of Physics and Geoscience, National Pingtung University of Education, Pingtung, Taiwan (China); Nonlinear Science Group, Department of Physics, National Kaohsiung Normal University, Kaohsiung, Taiwan (China); Jiang, I-M. [Department of Physics, National Sun Yat-sen University, Kaohsiung, Taiwan (China); Nonlinear Science Group, Department of Physics, National Kaohsiung Normal University, Kaohsiung, Taiwan (China)
2006-07-24
In this Letter, we study the chaos synchronization of two stochastically coupled random Boolean networks (RBNs). Instead of using the 'site-by-site and all-to-all' coupling, the coupling mechanism we consider here is that: the nth cell in a network is linked by an arbitrarily chosen cell in the other network with probability {rho}, and it possesses no links with probability 1-{rho}. The mechanism is useful to investigate the coevolution of biological species via horizontal genetic exchange. We show that the density evolution of networks can be described by two deterministic coupled polynomial maps. The complete synchronization occurs when the coupling parameter exceeds a critical value. Moreover, the reverse bifurcations in inhomogeneous condition are observed and under our discussion.
Hierarchy in directed random networks: analytical and numerical results
Mones, Enys
2012-01-01
In recent years, the theory and application of complex networks has been quickly developing in a markable way due to the increasing amount of data from real systems and to the fruitful application of powerful methods used in statistical physics. Many important characteristics of social or biological systems can be described by the study of their underlying structure of interactions. Hierarchy is one of these features that can be formulated in the language of networks. In this paper we present the analytic results on the hierarchical properties of random network models with zero correlations and also investigate the effects of different type of correlations. The behavior of hierarchy is different in the absence and the presence of the giant components. We show that the hierarchical structure can be drastically different if there are one-point correlations in the network. We also show numerical results suggesting that hierarchy does not change monotonously with the correlations and there is an optimal level of ...
Guevara Erra, R.; Mateos, D. M.; Wennberg, R.; Perez Velazquez, J. L.
2016-11-01
It is said that complexity lies between order and disorder. In the case of brain activity and physiology in general, complexity issues are being considered with increased emphasis. We sought to identify features of brain organization that are optimal for sensory processing, and that may guide the emergence of cognition and consciousness, by analyzing neurophysiological recordings in conscious and unconscious states. We find a surprisingly simple result: Normal wakeful states are characterized by the greatest number of possible configurations of interactions between brain networks, representing highest entropy values. Therefore, the information content is larger in the network associated to conscious states, suggesting that consciousness could be the result of an optimization of information processing. These findings help to guide in a more formal sense inquiry into how consciousness arises from the organization of matter.
Ryo Hase
2016-01-01
Full Text Available This paper presents a sequential solution method to discover efficient trades in an electricity market model. The market model represents deregulated electricity market consisting of four types of participants: independent power producers, retailers, public utilities, and consumers. Our model is based on graph theory, and the market participants are denoted by a network composes of three types of agents including sellers, buyers, and traders. The market participants have different capacity and demand of electricity from each other, and each electricity trade should satisfy the capacity and demand. Our sequential solution method can discover efficient electricity trades satisfying the constraints regarding capacity and demand by utilizing network flow. Simulation results demonstrate the efficiency of electricity trades determined by our method by examining social welfare, which is the total of payoffs of all market participants. Furthermore, the simulation results also indicate the allocation of payoff to each market participant.
Guevara Erra, R; Mateos, D M; Wennberg, R; Perez Velazquez, J L
2016-11-01
It is said that complexity lies between order and disorder. In the case of brain activity and physiology in general, complexity issues are being considered with increased emphasis. We sought to identify features of brain organization that are optimal for sensory processing, and that may guide the emergence of cognition and consciousness, by analyzing neurophysiological recordings in conscious and unconscious states. We find a surprisingly simple result: Normal wakeful states are characterized by the greatest number of possible configurations of interactions between brain networks, representing highest entropy values. Therefore, the information content is larger in the network associated to conscious states, suggesting that consciousness could be the result of an optimization of information processing. These findings help to guide in a more formal sense inquiry into how consciousness arises from the organization of matter.
Navigation by anomalous random walks on complex networks
Weng, Tongfeng; Khajehnejad, Moein; Small, Michael; Zheng, Rui; Hui, Pan
2016-01-01
Anomalous random walks having long-range jumps are a critical branch of dynamical processes on networks, which can model a number of search and transport processes. However, traditional measurements based on mean first passage time are not useful as they fail to characterize the cost associated with each jump. Here we introduce a new concept of mean first traverse distance (MFTD) to characterize anomalous random walks that represents the expected traverse distance taken by walkers searching from source node to target node, and we provide a procedure for calculating the MFTD between two nodes. We use Levy walks on networks as an example, and demonstrate that the proposed approach can unravel the interplay between diffusion dynamics of Levy walks and the underlying network structure. Interestingly, applying our framework to the famous PageRank search, we can explain why its damping factor empirically chosen to be around 0.85. The framework for analyzing anomalous random walks on complex networks offers a new us...
Maximizing the Lifetime of Wireless Sensor Networks Using Multiple Sets of Rendezvous
Bo Li
2015-01-01
Full Text Available In wireless sensor networks (WSNs, there is a “crowded center effect” where the energy of nodes located near a data sink drains much faster than other nodes resulting in a short network lifetime. To mitigate the “crowded center effect,” rendezvous points (RPs are used to gather data from other nodes. In order to prolong the lifetime of WSN further, we propose using multiple sets of RPs in turn to average the energy consumption of the RPs. The problem is how to select the multiple sets of RPs and how long to use each set of RPs. An optimal algorithm and a heuristic algorithm are proposed to address this problem. The optimal algorithm is highly complex and only suitable for small scale WSN. The performance of the proposed algorithms is evaluated through simulations. The simulation results indicate that the heuristic algorithm approaches the optimal one and that using multiple RP sets can significantly prolong network lifetime.
Delineating social network data anonymization via random edge perturbation
Xue, Mingqiang
2012-01-01
Social network data analysis raises concerns about the privacy of related entities or individuals. To address this issue, organizations can publish data after simply replacing the identities of individuals with pseudonyms, leaving the overall structure of the social network unchanged. However, it has been shown that attacks based on structural identification (e.g., a walk-based attack) enable an adversary to re-identify selected individuals in an anonymized network. In this paper we explore the capacity of techniques based on random edge perturbation to thwart such attacks. We theoretically establish that any kind of structural identification attack can effectively be prevented using random edge perturbation and show that, surprisingly, important properties of the whole network, as well as of subgraphs thereof, can be accurately calculated and hence data analysis tasks performed on the perturbed data, given that the legitimate data recipient knows the perturbation probability as well. Yet we also examine ways to enhance the walk-based attack, proposing a variant we call probabilistic attack. Nevertheless, we demonstrate that such probabilistic attacks can also be prevented under sufficient perturbation. Eventually, we conduct a thorough theoretical study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments with synthetic and real datasets confirm our expectations. © 2012 ACM.
Characteristic times of biased random walks on complex networks
Bonaventura, Moreno; Latora, Vito
2013-01-01
We consider degree-biased random walkers whose probability to move from a node to one of its neighbours of degree k is proportional to k^{\\alpha}, where \\alpha is a tuning parameter. We study both numerically and analytically three types of characteristic times, namely: i) the time the walker needs to come back to the starting node, ii) the time it takes to pass from a given node, and iii) the time it takes to visit all the nodes of the network. We consider a large database of real-world networks and we show that the value of \\alpha which minimizes the three characteristic times is different from the value \\alpha_{min}=-1 analytically found for uncorrelated networks in the mean-field approximation. In addition to this, assortative networks have preferentially a value of \\alpha_{min} in the range [-1,-0.5], while disassortative networks have \\alpha_{min} in the range [-0.5, 0]. When only local information is available, degree-biased random walks can guarantee smaller characteristic times by means of an appropr...
Low-dimensional dynamics of structured random networks
Aljadeff, Johnatan; Renfrew, David; Vegué, Marina; Sharpee, Tatyana O.
2016-02-01
Using a generalized random recurrent neural network model, and by extending our recently developed mean-field approach [J. Aljadeff, M. Stern, and T. Sharpee, Phys. Rev. Lett. 114, 088101 (2015), 10.1103/PhysRevLett.114.088101], we study the relationship between the network connectivity structure and its low-dimensional dynamics. Each connection in the network is a random number with mean 0 and variance that depends on pre- and postsynaptic neurons through a sufficiently smooth function g of their identities. We find that these networks undergo a phase transition from a silent to a chaotic state at a critical point we derive as a function of g . Above the critical point, although unit activation levels are chaotic, their autocorrelation functions are restricted to a low-dimensional subspace. This provides a direct link between the network's structure and some of its functional characteristics. We discuss example applications of the general results to neuroscience where we derive the support of the spectrum of connectivity matrices with heterogeneous and possibly correlated degree distributions, and to ecology where we study the stability of the cascade model for food web structure.
James C. Liao
2012-05-22
This project is a collaboration with F. R. Tabita of Ohio State. Our major goal is to understand the factors and regulatory mechanisms that influence hydrogen production. The organisms to be utilized in this study, phototrophic microorganisms, in particular nonsulfur purple (NSP) bacteria, catalyze many significant processes including the assimilation of carbon dioxide into organic carbon, nitrogen fixation, sulfur oxidation, aromatic acid degradation, and hydrogen oxidation/evolution. Our part of the project was to develop a modeling technique to investigate the metabolic network in connection to hydrogen production and regulation. Organisms must balance the pathways that generate and consume reducing power in order to maintain redox homeostasis to achieve growth. Maintaining this homeostasis in the nonsulfur purple photosynthetic bacteria is a complex feat with many avenues that can lead to balance, as these organisms possess versatile metabolic capabilities including anoxygenic photosynthesis, aerobic or anaerobic respiration, and fermentation. Growth is achieved by using H{sub 2} as an electron donor and CO{sub 2} as a carbon source during photoautotrophic and chemoautotrophic growth, where CO{sub 2} is fixed via the Calvin-Benson-Bassham (CBB) cycle. Photoheterotrophic growth can also occur when alternative organic carbon compounds are utilized as both the carbon source and electron donor. Regardless of the growth mode, excess reducing equivalents generated as a result of oxidative processes, must be transferred to terminal electron acceptors, thus insuring that redox homeostasis is maintained in the cell. Possible terminal acceptors include O{sub 2}, CO{sub 2}, organic carbon, or various oxyanions. Cells possess regulatory mechanisms to balance the activity of the pathways which supply energy, such as photosynthesis, and those that consume energy, such as CO{sub 2} assimilation or N{sub 2} fixation. The major route for CO{sub 2} assimilation is the CBB
A Random Dot Product Model for Weighted Networks
DeFord, Daryl R
2016-01-01
This paper presents a generalization of the random dot product model for networks whose edge weights are drawn from a parametrized probability distribution. We focus on the case of integer weight edges and show that many previously studied models can be recovered as special cases of this generalization. Our model also determines a dimension--reducing embedding process that gives geometric interpretations of community structure and centrality. The dimension of the embedding has consequences for the derived community structure and we exhibit a stress function for determining appropriate dimensions. We use this approach to analyze a coauthorship network and voting data from the U.S. Senate.
Optimal resource allocation in random networks with transportation bandwidths
Yeung, C. H.; Wong, K. Y. Michael
2009-03-01
We apply statistical physics to study the task of resource allocation in random sparse networks with limited bandwidths for the transportation of resources along the links. Recursive relations from the Bethe approximation are converted into useful algorithms. Bottlenecks emerge when the bandwidths are small, causing an increase in the fraction of idle links. For a given total bandwidth per node, the efficiency of allocation increases with the network connectivity. In the high connectivity limit, we find a phase transition at a critical bandwidth, above which clusters of balanced nodes appear, characterized by a profile of homogenized resource allocation similar to the Maxwell construction.
Random lasing in organo-lead halide perovskite microcrystal networks
Dhanker, R.; Brigeman, A. N.; Larsen, A. V.; Stewart, R. J.; Asbury, J. B.; Giebink, N. C.
2014-10-01
We report optically pumped random lasing in planar methylammonium lead iodide perovskite microcrystal networks that form spontaneously from spin coating. Low thresholds (100 μm and spatially overlap with one another, resulting in chaotic pulse-to-pulse intensity fluctuations due to gain competition. These results demonstrate this class of hybrid organic-inorganic perovskite as a platform to study random lasing with well-defined, low-level disorder, and support the potential of these materials for use in semiconductor laser applications.
Clinic Network Collaboration and Patient Tracing to Maximize Retention in HIV Care.
James H McMahon
Full Text Available Understanding retention and loss to follow up in HIV care, in particular the number of people with unknown outcomes, is critical to maximise the benefits of antiretroviral therapy. Individual-level data are not available for these outcomes in Australia, which has an HIV epidemic predominantly focused amongst men who have sex with men.A network of the 6 main HIV clinical care sites was established in the state of Victoria, Australia. Individuals who had accessed care at these sites between February 2011 and June 2013 as assessed by HIV viral load testing but not accessed care between June 2013 and February 2014 were considered individuals with potentially unknown outcomes. For this group an intervention combining cross-referencing of clinical data between sites and phone tracing individuals with unknown outcomes was performed. 4966 people were in care in the network and before the intervention estimates of retention ranged from 85.9%-95.8% and the proportion with unknown outcomes ranged from 1.3-5.5%. After the intervention retention increased to 91.4-98.8% and unknown outcomes decreased to 0.1-2.4% (p<.01 for all sites for both outcomes. Most common reasons for disengagement from care were being too busy to attend or feeling well. For those with unknown outcomes prior to the intervention documented active psychiatric illness at last visit was associated with not re-entering care (p = 0.04.The network demonstrated low numbers of people with unknown outcomes and high levels of retention in care. Increased levels of retention in care and reductions in unknown outcomes identified after the intervention largely reflected confirmation of clinic transfers while a smaller number were successfully re-engaged in care. Factors associated with disengagement from care were identified. Systems to monitor patient retention, care transfer and minimize disengagement will maximise individual and population-level outcomes for populations with HIV.
He, Peter; Zhao, Lian; Lu, Jianhua
2013-12-01
In this article, an efficient distributed and parallel algorithm is proposed to maximize the sum-rate and optimize the input distribution policy for the multi-user single input multiple output multiple access channel (MU-SIMO MAC) system with concurrent access within a cognitive radio (CR) network. The single input means that every user has a single antenna and multiple output means that base station(s) has multiple antennas. The main features are: (i) the power distribution for the users is updated by using variable scale factors which effectively and efficiently maximize the objective function at each iteration; (ii) distributed and parallel computation is employed to expedite convergence of the proposed distributed algorithm; and (iii) a novel water-filling with mixed constraints is investigated, and used as a fundamental block of the proposed algorithm. Due to sufficiently exploiting the structure of the proposed model, the proposed algorithm owns fast convergence. Numerical results verify that the proposed algorithm is effective and fast convergent. Using the proposed approach, for the simulated range, the required number of iterations for convergence is two and this number is not sensitive to the increase of the number of users. This feature is quite desirable for large scale systems with dense active users. In addition, it is also worth noting that the proposed algorithm is a monotonic feasible operator to the iteration. Thus, the stop criterion for computation could be easily set up.
Limited Imitation Contagion on Random Networks: Chaos, Universality, and Unpredictability
Dodds, Peter Sheridan; Danforth, Christopher M
2012-01-01
We study a family of binary state, socially-inspired contagion models which incorporate imitation limited by an aversion to complete conformity. We uncover rich behavior in our models whether operating with probabilistic or deterministic individual response functions, both on dynamic or fixed random networks. In particular, we find significant variation in the limiting behavior of a population's infected fraction, ranging from steady-state to chaotic. We show that period doubling arises as we increase the average node degree, and that the universality class of this well known route to chaos depends on the interaction structure of random networks rather than the microscopic behavior of individual nodes. We find that increasing the fixedness of the system tends to stabilize the infected fraction, yet disjoint, multiple equilibria are possible depending solely on the choice of the initially infected node.
Emergent classical geometries on boundaries of randomly connected tensor networks
Chen, Hua; Sasakura, Naoki; Sato, Yuki
2016-03-01
It is shown that classical spaces with geometries emerge on boundaries of randomly connected tensor networks with appropriately chosen tensors in the thermodynamic limit. With variation of the tensors the dimensions of the spaces can be freely chosen, and the geometries—which are curved in general—can be varied. We give the explicit solvable examples of emergent flat tori in arbitrary dimensions, and the correspondence from the tensors to the geometries for general curved cases. The perturbative dynamics in the emergent space is shown to be described by an effective action which is invariant under the spatial diffeomorphism due to the underlying orthogonal group symmetry of the randomly connected tensor network. It is also shown that there are various phase transitions among spaces, including extended and point-like ones, under continuous change of the tensors.
Emergent classical geometries on boundaries of randomly connected tensor networks
Chen, Hua; Sato, Yuki
2016-01-01
It is shown that classical spaces with geometries emerge on boundaries of randomly connected tensor networks with appropriately chosen tensors in the thermodynamic limit. With variation of the tensors, the dimensions of the spaces can be freely chosen, and the geometries, which are curved in general, can be varied. We give the explicit solvable examples of emergent flat tori in arbitrary dimensions, and the correspondence from the tensors to the geometries for general curved cases. The perturbative dynamics in the emergent space is shown to be described by an effective action which is invariant under the spatial diffeomorphism due to the underlying orthogonal group symmetry of the randomly connected tensor network. It is also shown that there are various phase transitions among spaces, including extended and point-like ones, under continuous change of the tensors.
Margareta RACOVITA
2011-11-01
Full Text Available This work proposes to solve a problem related to maximizing hotels’ revenues through two methods established in operational research domain. In the first part of the paper, the approach involves formulating the objective function and problem’s constraints, as well as the expansion of the model, taking into consideration clients’ preferences and the opportunities of group reservations. In the second part of the paper, the problem is solved with the help of network flows model, which allows optimum allocation of the rooms in real time. At the end of the paper, there are highlighted the advantages of applying those two mathematic methods within the strategies of performances development within hotel industry.
A simple model of global cascades on random networks
Watts, Duncan J.
2002-04-01
The origin of large but rare cascades that are triggered by small initial shocks is a phenomenon that manifests itself as diversely as cultural fads, collective action, the diffusion of norms and innovations, and cascading failures in infrastructure and organizational networks. This paper presents a possible explanation of this phenomenon in terms of a sparse, random network of interacting agents whose decisions are determined by the actions of their neighbors according to a simple threshold rule. Two regimes are identified in which the network is susceptible to very large cascadesherein called global cascadesthat occur very rarely. When cascade propagation is limited by the connectivity of the network, a power law distribution of cascade sizes is observed, analogous to the cluster size distribution in standard percolation theory and avalanches in self-organized criticality. But when the network is highly connected, cascade propagation is limited instead by the local stability of the nodes themselves, and the size distribution of cascades is bimodal, implying a more extreme kind of instability that is correspondingly harder to anticipate. In the first regime, where the distribution of network neighbors is highly skewed, it is found that the most connected nodes are far more likely than average nodes to trigger cascades, but not in the second regime. Finally, it is shown that heterogeneity plays an ambiguous role in determining a system's stability: increasingly heterogeneous thresholds make the system more vulnerable to global cascades; but an increasingly heterogeneous degree distribution makes it less vulnerable.
Adaptive Local Information Transfer in Random Boolean Networks.
Haruna, Taichi
2017-01-01
Living systems such as gene regulatory networks and neuronal networks have been supposed to work close to dynamical criticality, where their information-processing ability is optimal at the whole-system level. We investigate how this global information-processing optimality is related to the local information transfer at each individual-unit level. In particular, we introduce an internal adjustment process of the local information transfer and examine whether the former can emerge from the latter. We propose an adaptive random Boolean network model in which each unit rewires its incoming arcs from other units to balance stability of its information processing based on the measurement of the local information transfer pattern. First, we show numerically that random Boolean networks can self-organize toward near dynamical criticality in our model. Second, the proposed model is analyzed by a mean-field theory. We recognize that the rewiring rule has a bootstrapping feature. The stationary indegree distribution is calculated semi-analytically and is shown to be close to dynamical criticality in a broad range of model parameter values.
Order-based representation in random networks of cortical neurons.
Goded Shahaf
2008-11-01
Full Text Available The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen.
Randomized vs. orthogonal spectrum allocation in decentralized networks: Outage Analysis
Moshksar, Kamyar; Khandani, Amir K
2009-01-01
We address a decentralized wireless communication network with a fixed number $u$ of frequency sub-bands to be shared among $N$ transmitter-receiver pairs. It is assumed that the number of users $N$ is a random variable with a given distribution and the channel gains are quasi-static Rayleigh fading. The transmitters are assumed to be unaware of the number of active users in the network as well as the channel gains and not capable of detecting the presence of other users in a given frequency sub-band. Moreover, the users are unaware of each other's codebooks and hence, no multiuser detection is possible. We consider a randomized Frequency Hopping (FH) scheme in which each transmitter randomly hops over a subset of the $u$ sub-bands from transmission to transmission. Developing a new upper bound on the differential entropy of a mixed Gaussian random vector and using entropy power inequality, we offer a series of lower bounds on the achievable rate of each user. Thereafter, we obtain lower bounds on the maximum...
Nonlinear system modeling with random matrices: echo state networks revisited.
Zhang, Bai; Miller, David J; Wang, Yue
2012-01-01
Echo state networks (ESNs) are a novel form of recurrent neural networks (RNNs) that provide an efficient and powerful computational model approximating nonlinear dynamical systems. A unique feature of an ESN is that a large number of neurons (the "reservoir") are used, whose synaptic connections are generated randomly, with only the connections from the reservoir to the output modified by learning. Why a large randomly generated fixed RNN gives such excellent performance in approximating nonlinear systems is still not well understood. In this brief, we apply random matrix theory to examine the properties of random reservoirs in ESNs under different topologies (sparse or fully connected) and connection weights (Bernoulli or Gaussian). We quantify the asymptotic gap between the scaling factor bounds for the necessary and sufficient conditions previously proposed for the echo state property. We then show that the state transition mapping is contractive with high probability when only the necessary condition is satisfied, which corroborates and thus analytically explains the observation that in practice one obtains echo states when the spectral radius of the reservoir weight matrix is smaller than 1.
Identifying streamgage networks for maximizing the effectiveness of regional water balance modeling
Fry, L. M.; Hunter, T. S.; Phanikumar, M. S.; Fortin, V.; Gronewold, A. D.
2013-05-01
One approach to regional water balance modeling is to constrain rainfall-runoff models with a synthetic regionalized hydrologic response. For example, the Large Basin Runoff Model (LBRM), a cornerstone of hydrologic forecasting in the Laurentian Great Lakes basin, was calibrated to a synthetic discharge record resulting from a drainage area ratio method (ARM) for extrapolating beyond gaged areas. A challenge of such approaches is the declining availability of observations for development of synthetic records. To advance efficient use of the declining gage network in the context of regional water balance modeling, we present results from an assessment of ARM. All possible combinations of "most-downstream" gages were used to simulate runoff at the gaged outlet of Michigan's Clinton River watershed in order to determine the influence of gages' drainage area and other physical characteristics on model skill. For nearly all gage combinations, ARM simulations resulted in good model skill. However, the gages' catchment area relative to that of the outlet's catchment is not an unquestionable predictor of model performance. Results indicate that combinations representing less than 30% of the total catchment area (less than 10% in some cases) can provide very good discharge simulations, but that similarity of the gaged catchments' developed and cultivated area, stream density, and permeability relative to the outlet's catchment is also important for successful simulations. Recognition of thresholds on the relationship between the number of gages and their relative value in simulating flow over large area provides an opportunity for improving historical records for regional hydrologic modeling.
求解 CPM 网络计划的最大网络时差%Solving Maximal Network Float of CPM Network Planning
苏志雄; 乞建勋; 阚芝南
2014-01-01
Network float of network planning denotes sum of floats which could be consumed practically by each activity in project , and it isn't the simple sum of floats in theory .Network float determines sum of the maximal reachable durations of all activities in practice under the condition of fixed total duration , and is closely correla-tive to cost management and time management of project engineering .The network float is variable , and is correlative to time schedule of each activity .It illustrates that value especial maximal value of the float could be decided the by adjusting time schedule of activities , and then realizes the optimization of cost and time .In this paper, firstly, the meaning of network float is analyzed from a new visual angle; secondly, algorithm to solve maximal network float is designed based on the above meanings , and the algorithm is that we transform the prob-lem to especial “time-cost tradeoff problem” by founding and analyzing the model of maximal network float , which could be solved by using classic algorithm such as Fulkerson algorithm;and finally , the algorithm is dem-onstrated by an example .%CPM网络计划的网络时差表示项目中各工序实际可使用的机动时间的总和（绝非理论上机动时间的简单加总），即CPM网络计划的总机动时间，它决定着在总工期不变的前提下，所有工序实际可以达到的最大工期的总和，与项目的成本管理和时间管理密切相关。网络时差是变量，取决于各工序的时间进度安排，说明可以通过调整工序的时间进度来决定该时差的取值，特别是其最大值，进而实现成本和时间优化。本文首先从新的角度分析了网络时差的含义；然后，在此基础上设计了求解最大网络时差的算法，其思路为，通过建立和分析最大网络时差模型，将其转化为特殊的“时间-费用权衡问题”，进而可运用Fulkerson 算法等经典算法求解；最
Network sampling coverage II: The effect of non-random missing data on network measurement.
Smith, Jeffrey A; Moody, James; Morgan, Jonathan
2017-01-01
Missing data is an important, but often ignored, aspect of a network study. Measurement validity is affected by missing data, but the level of bias can be difficult to gauge. Here, we describe the effect of missing data on network measurement across widely different circumstances. In Part I of this study (Smith and Moody, 2013), we explored the effect of measurement bias due to randomly missing nodes. Here, we drop the assumption that data are missing at random: what happens to estimates of key network statistics when central nodes are more/less likely to be missing? We answer this question using a wide range of empirical networks and network measures. We find that bias is worse when more central nodes are missing. With respect to network measures, Bonacich centrality is highly sensitive to the loss of central nodes, while closeness centrality is not; distance and bicomponent size are more affected than triad summary measures and behavioral homophily is more robust than degree-homophily. With respect to types of networks, larger, directed networks tend to be more robust, but the relation is weak. We end the paper with a practical application, showing how researchers can use our results (translated into a publically available java application) to gauge the bias in their own data.
Izmailian, N Sh; Huang, Ming-Chang
2010-07-01
We analyze the exact formulas for the resistance between two arbitrary notes in a rectangular network of resistors under free, periodic and cylindrical boundary conditions obtained by Wu [J. Phys. A 37, 6653 (2004)]. Based on such expression, we then apply the algorithm of Ivashkevich, Izmailian, and Hu [J. Phys. A 35, 5543 (2002)] to derive the exact asymptotic expansions of the resistance between two maximally separated nodes on an M×N rectangular network of resistors with resistors r and s in the two spatial directions. Our results is 1/s (R(M×N))(r,s) = c(ρ)ln S + c(0)(ρ,ξ) + ∑(p=1)(∞) (c(2p)(ρ,ξ))/S(p) with S = MN, ρ = r/s and ξ = M/N. The all coefficients in this expansion are expressed through analytical functions. We have introduced the effective aspect ratio ξeff = square root(ρ)ξ for free and periodic boundary conditions and ξeff = square root(ρ)ξ/2 for cylindrical boundary condition and show that all finite-size correction terms are invariant under transformation ξeff→1/ξeff.
Zhao, Jun; Gligor, Virgil
2015-01-01
Random intersection graphs have received much interest and been used in diverse applications. They are naturally induced in modeling secure sensor networks under random key predistribution schemes, as well as in modeling the topologies of social networks including common-interest networks, collaboration networks, and actor networks. Simply put, a random intersection graph is constructed by assigning each node a set of items in some random manner and then putting an edge between any two nodes that share a certain number of items. Broadly speaking, our work is about analyzing random intersection graphs, and models generated by composing it with other random graph models including random geometric graphs and Erd\\H{o}s-R\\'enyi graphs. These compositional models are introduced to capture the characteristics of various complex natural or man-made networks more accurately than the existing models in the literature. For random intersection graphs and their compositions with other random graphs, we study properties su...
Mechanical Behavior of Homogeneous and Composite Random Fiber Networks
Shahsavari, Ali
Random fiber networks are present in many biological and non-biological materials such as paper, cytoskeleton, and tissue scaffolds. Mechanical behavior of networks is controlled by the mechanical properties of the constituent fibers and the architecture of the network. To characterize these two main factors, different parameters such as fiber density, fiber length, average segment length, nature of the cross-links at the fiber intersections, ratio of bending to axial behavior of fibers have been considered. Random fiber networks are usually modeled by representing each fiber as a Timoshenko or an Euler-Bernoulli beam and each cross-link as either a welded or rotating joint. In this dissertation, the effect of these modeling options on the dependence of the overall linear network modulus on microstructural parameters is studied. It is concluded that Timoshenko beams can be used for the whole range of density and fiber stiffness parameters, while the Euler-Bernoulli model can be used only at relatively low densities. In the low density-low bending stiffness range, elastic strain energy is stored in the bending mode of the deformation, while in the other extreme range of parameters, the energy is stored predominantly in the axial and shear deformation modes. It is shown that both rotating and welded joint models give the same rules for scaling of the network modulus with different micromechanical parameters. The elastic modulus of sparsely cross-linked random fiber networks, i.e. networks in which the degree of cross-linking varies, is studied. The relationship between the micromechanical parameters - fiber density, fiber axial and bending stiffness, and degree of cross-linking - and the overall elastic modulus is presented in terms of a master curve. It is shown that the master plot with various degrees of cross-linking can be collapsed to a curve which is also valid for fully cross-linked networks. Random fiber networks in which fibers are bonded to each other are
Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm
Qing Han
2013-10-01
Full Text Available Emergency rescues require that first responders provide support to evacuate injured and other civilians who are obstructed by the hazards. In this case, the emergency personnel can take actions strategically in order to rescue people maximally, efficiently and quickly. The paper studies the effectiveness of a random neural network (RNN-based task assignment algorithm involving optimally matching emergency personnel and injured civilians, so that the emergency personnel can aid trapped people to move towards evacuation exits in real-time. The evaluations are run on a decision support evacuation system using the Distributed Building Evacuation Simulator (DBES multi-agent platform in various emergency scenarios. The simulation results indicate that the RNN-based task assignment algorithm provides a near-optimal solution to resource allocation problems, which avoids resource wastage and improves the efficiency of the emergency rescue process.
Predicting genetic interactions with random walks on biological networks
Singh Ambuj K
2009-01-01
Full Text Available Abstract Background Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree
Flexible sampling large-scale social networks by self-adjustable random walk
Xu, Xiao-Ke; Zhu, Jonathan J. H.
2016-12-01
Online social networks (OSNs) have become an increasingly attractive gold mine for academic and commercial researchers. However, research on OSNs faces a number of difficult challenges. One bottleneck lies in the massive quantity and often unavailability of OSN population data. Sampling perhaps becomes the only feasible solution to the problems. How to draw samples that can represent the underlying OSNs has remained a formidable task because of a number of conceptual and methodological reasons. Especially, most of the empirically-driven studies on network sampling are confined to simulated data or sub-graph data, which are fundamentally different from real and complete-graph OSNs. In the current study, we propose a flexible sampling method, called Self-Adjustable Random Walk (SARW), and test it against with the population data of a real large-scale OSN. We evaluate the strengths of the sampling method in comparison with four prevailing methods, including uniform, breadth-first search (BFS), random walk (RW), and revised RW (i.e., MHRW) sampling. We try to mix both induced-edge and external-edge information of sampled nodes together in the same sampling process. Our results show that the SARW sampling method has been able to generate unbiased samples of OSNs with maximal precision and minimal cost. The study is helpful for the practice of OSN research by providing a highly needed sampling tools, for the methodological development of large-scale network sampling by comparative evaluations of existing sampling methods, and for the theoretical understanding of human networks by highlighting discrepancies and contradictions between existing knowledge/assumptions of large-scale real OSN data.
Epidemic spreading on random surfer networks with infected avoidance strategy
Feng, Yun; Ding, Li; Huang, Yun-Han; Guan, Zhi-Hong
2016-12-01
In this paper, we study epidemic spreading on random surfer networks with infected avoidance (IA) strategy. In particular, we consider that susceptible individuals’ moving direction angles are affected by the current location information received from infected individuals through a directed information network. The model is mainly analyzed by discrete-time numerical simulations. The results indicate that the IA strategy can restrain epidemic spreading effectively. However, when long-distance jumps of individuals exist, the IA strategy’s effectiveness on restraining epidemic spreading is heavily reduced. Finally, it is found that the influence of the noises from information transferring process on epidemic spreading is indistinctive. Project supported in part by the National Natural Science Foundation of China (Grant Nos. 61403284, 61272114, 61673303, and 61672112) and the Marine Renewable Energy Special Fund Project of the State Oceanic Administration of China (Grant No. GHME2013JS01).
Cascading dynamics on random networks: Crossover in phase transition
Liu, Run-Ran; Wang, Wen-Xu; Lai, Ying-Cheng; Wang, Bing-Hong
2012-02-01
In a complex network, random initial attacks or failures can trigger subsequent failures in a cascading manner, which is effectively a phase transition. Recent works have demonstrated that in networks with interdependent links so that the failure of one node causes the immediate failures of all nodes connected to it by such links, both first- and second-order phase transitions can arise. Moreover, there is a crossover between the two types of transitions at a critical system-parameter value. We demonstrate that these phenomena can occur in the more general setting where no interdependent links are present. A heuristic theory is derived to estimate the crossover and phase-transition points, and a remarkable agreement with numerics is obtained.
Exploring MEDLINE space with random indexing and pathfinder networks.
Cohen, Trevor
2008-11-06
The integration of disparate research domains is a prerequisite for the success of the translational science initiative. MEDLINE abstracts contain content from a broad range of disciplines, presenting an opportunity for the development of methods able to integrate the knowledge they contain. Latent Semantic Analysis (LSA) and related methods learn human-like associations between terms from unannotated text. However, their computational and memory demands limits their ability to address a corpus of this size. Furthermore, visualization methods previously used in conjunction with LSA have limited ability to define the local structure of the associative networks LSA learns. This paper explores these issues by (1) processing the entire MEDLINE corpus using Random Indexing, a variant of LSA, and (2) exploring learned associations using Pathfinder Networks. Meaningful associations are inferred from MEDLINE, including a drug-disease association undetected by PUBMED search.
SIRS Dynamics on Random Networks: Simulations and Analytical Models
Rozhnova, Ganna; Nunes, Ana
The standard pair approximation equations (PA) for the Susceptible-Infective-Recovered-Susceptible (SIRS) model of infection spread on a network of homogeneous degree k predict a thin phase of sustained oscillations for parameter values that correspond to diseases that confer long lasting immunity. Here we present a study of the dependence of this oscillatory phase on the parameter k and of its relevance to understand the behaviour of simulations on networks. For k = 4, we compare the phase diagram of the PA model with the results of simulations on regular random graphs (RRG) of the same degree. We show that for parameter values in the oscillatory phase, and even for large system sizes, the simulations either die out or exhibit damped oscillations, depending on the initial conditions. This failure of the standard PA model to capture the qualitative behaviour of the simulations on large RRGs is currently being investigated.
Trend-driven information cascades on random networks.
Kobayashi, Teruyoshi
2015-12-01
Threshold models of global cascades have been extensively used to model real-world collective behavior, such as the contagious spread of fads and the adoption of new technologies. A common property of those cascade models is that a vanishingly small seed fraction can spread to a finite fraction of an infinitely large network through local infections. In social and economic networks, however, individuals' behavior is often influenced not only by what their direct neighbors are doing, but also by what the majority of people are doing as a trend. A trend affects individuals' behavior while individuals' behavior creates a trend. To analyze such a complex interplay between local- and global-scale phenomena, I generalize the standard threshold model by introducing a type of node called global nodes (or trend followers), whose activation probability depends on a global-scale trend, specifically the percentage of activated nodes in the population. The model shows that global nodes play a role as accelerating cascades once a trend emerges while reducing the probability of a trend emerging. Global nodes thus either facilitate or inhibit cascades, suggesting that a moderate share of trend followers may maximize the average size of cascades.
Distributed clone detection in static wireless sensor networks: random walk with network division.
Wazir Zada Khan
Full Text Available Wireless Sensor Networks (WSNs are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.
Network Randomization and Dynamic Defense for Critical Infrastructure Systems
Chavez, Adrian R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Martin, Mitchell Tyler [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hamlet, Jason [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Stout, William M.S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Lee, Erik [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-04-01
Critical Infrastructure control systems continue to foster predictable communication paths, static configurations, and unpatched systems that allow easy access to our nation's most critical assets. This makes them attractive targets for cyber intrusion. We seek to address these attack vectors by automatically randomizing network settings, randomizing applications on the end devices themselves, and dynamically defending these systems against active attacks. Applying these protective measures will convert control systems into moving targets that proactively defend themselves against attack. Sandia National Laboratories has led this effort by gathering operational and technical requirements from Tennessee Valley Authority (TVA) and performing research and development to create a proof-of-concept solution. Our proof-of-concept has been tested in a laboratory environment with over 300 nodes. The vision of this project is to enhance control system security by converting existing control systems into moving targets and building these security measures into future systems while meeting the unique constraints that control systems face.
Scaling law of resistance fluctuations in stationary random resistor networks
Pennetta; Trefan; Reggiani
2000-12-11
In a random resistor network we consider the simultaneous evolution of two competing random processes consisting in breaking and recovering the elementary resistors with probabilities W(D) and W(R). The condition W(R)>W(D)/(1+W(D)) leads to a stationary state, while in the opposite case, the broken resistor fraction reaches the percolation threshold p(c). We study the resistance noise of this system under stationary conditions by Monte Carlo simulations. The variance of resistance fluctuations is found to follow a scaling law |p-p(c)|(-kappa(0)) with kappa(0) = 5.5. The proposed model relates quantitatively the defectiveness of a disordered media with its electrical and excess-noise characteristics.
A random resistor network model of voltage trimming
Grimaldi, C [Laboratoire de Production Microtechnique, Ecole Polytechnique Federale de Lausanne, CH-1015 Lausanne (Switzerland); Maeder, T [Laboratoire de Production Microtechnique, Ecole Polytechnique Federale de Lausanne, CH-1015 Lausanne (Switzerland); Ryser, P [Laboratoire de Production Microtechnique, Ecole Polytechnique Federale de Lausanne, CH-1015 Lausanne (Switzerland); Straessler, S [Laboratoire de Production Microtechnique, Ecole Polytechnique Federale de Lausanne, CH-1015 Lausanne (Switzerland)
2004-08-07
In industrial applications, the controlled adjustment (trimming) of resistive elements via the application of high voltage pulses is a promising technique, with several advantages with respect to more classical approaches such as the laser cutting method. The microscopic processes governing the response to high voltage pulses depend on the nature of the resistor and on the interaction with the local environment. Here we provide a theoretical statistical description of voltage discharge effects on disordered composites by considering random resistor network models with different properties and processes due to the voltage discharge. We compare standard percolation results with biased percolation effects and provide a tentative explanation of the different scenarios observed during trimming processes.
van Essen, J; Hübner, M; von Mittelstaedt, G
2007-03-01
Hospital billing converted to "German diagnosis-related groups" (G-DRG) for in-patient treatment in Germany is reviewed, except in psychiatry where per-diems are still in use. Currently thousands of bills are sent to the Medical Service for scrutiny. In addition, the law relating to Hospital Financing (Krankenhausfinanzierungsgesetz, para. 17 c) provides for systematic checks on a random sample of bills from a given hospital. The Medical Service of the Social Security Health Insurance reports on the experience in the State of Hessen. Present regulations exclude from the random sample those bills that have already been presented for a check on a case by case basis. Excluding these cases from the random sample introduces a bias in an avoidable way. The present rule is contrary to valid conclusions from the random sampling and should be abolished.
Todorov, Todor
2011-01-01
Given a chain complex with the only modi?cation that each cell of the complex has a probability distribution assigned. We will call this complex - a random complex and what should be understood in practice, is that we have a classical chain complex whose cells appear and disappear according to some probability distributions. In this paper, we will try to fi?nd the stochastic homology of random complex, whose simplices have independent discrete distributions.
Random walk approach for dispersive transport in pipe networks
Sämann, Robert; Graf, Thomas; Neuweiler, Insa
2016-04-01
Keywords: particle transport, random walk, pipe, network, HYSTEM-EXTAN, OpenGeoSys After heavy pluvial events in urban areas the available drainage system may be undersized at peak flows (Fuchs, 2013). Consequently, rainwater in the pipe network is likely to spill out through manholes. The presence of hazardous contaminants in the pipe drainage system represents a potential risk to humans especially when the contaminated drainage water reaches the land surface. Real-time forecasting of contaminants in the drainage system needs a quick calculation. Numerical models to predict the fate of contaminants are usually based on finite volume methods. Those are not applicable here because of their volume averaging elements. Thus, a more efficient method is preferable, which is independent from spatial discretization. In the present study, a particle-based method is chosen to calculate transport paths and spatial distribution of contaminants within a pipe network. A random walk method for particles in turbulent flow in partially filled pipes has been developed. Different approaches for in-pipe-mixing and node-mixing with respect to the geometry in a drainage network are shown. A comparison of dispersive behavior and calculation time is given to find the fastest model. The HYSTEM-EXTRAN (itwh, 2002) model is used to provide hydrodynamic conditions in the pipe network according to surface runoff scenarios in order to real-time predict contaminant transport in an urban pipe network system. The newly developed particle-based model will later be coupled to the subsurface flow model OpenGeoSys (Kolditz et al., 2012). References: Fuchs, L. (2013). Gefährdungsanalyse zur Überflutungsvorsorge kommunaler Entwässerungssysteme. Sanierung und Anpassung von Entwässerungssystemen-Alternde Infrastruktur und Klimawandel, Österreichischer Wasser-und Abfallwirtschaftsverband, Wien, ISBN, 978-3. itwh (2002). Modellbeschreibung, Institut für technisch-wissenschaftliche Hydrologie Gmb
Eigenvalue tunneling and decay of quenched random network
Avetisov, V.; Hovhannisyan, M.; Gorsky, A.; Nechaev, S.; Tamm, M.; Valba, O.
2016-12-01
We consider the canonical ensemble of N -vertex Erdős-Rényi (ER) random topological graphs with quenched vertex degree, and with fugacity μ for each closed triple of bonds. We claim complete defragmentation of large-N graphs into the collection of [p-1] almost full subgraphs (cliques) above critical fugacity, μc, where p is the ER bond formation probability. Evolution of the spectral density, ρ (λ ) , of the adjacency matrix with increasing μ leads to the formation of a multizonal support for μ >μc . Eigenvalue tunneling from the central zone to the side one means formation of a new clique in the defragmentation process. The adjacency matrix of the network ground state has a block-diagonal form, where the number of vertices in blocks fluctuates around the mean value N p . The spectral density of the whole network in this regime has triangular shape. We interpret the phenomena from the viewpoint of the conventional random matrix model and speculate about possible physical applications.
Eigenvalue tunnelling and decay of quenched random networks
Avetisov, V; Gorsky, A; Nechaev, S; Tamm, M; Valba, O
2016-01-01
We consider the canonical ensemble of Erd\\H{o}s-R\\'enyi (ER) random graphs with quenched vertex degree, and with fugacity $\\mu$ for each closed triple of bonds. We claim complete defragmentation of large-$N$ topological graphs into the collection of $[p^{-1}]$ almost full subgraphs (cliques) above critical fugacity, $\\mu_c$, where $p$ is the ER bond formation probability. Evolution of the spectral density, $\\rho(\\lambda)$, of the adjacency matrix with increasing $\\mu$ leads to the formation of two-zonal support above some critical point $\\mu_c$. Eigenvalue tunnelling from one (central) zone to the other means formation of a new clique in the defragmentation process. The symmetry of clusterized network breaks down from $U(N)$ to $U([p^{-1}])\\times U(N-[p^{-1}])$ and the spectral density of the whole network in this regime has triangular shape. We interpret the phenomena from the viewpoint of the conventional random matrix model and speculate about possible physical applications.
Analysis of complex contagions in random multiplex networks
Yagan, Osman
2012-01-01
We study the diffusion of influence in random multiplex networks where links can be of $r$ different types, and for a given content (e.g., rumor, product, political view), each link type is associated with a content dependent parameter $c_i$ in $[0,\\infty]$ that measures the relative bias type-$i$ links have in spreading this content. In this setting, we propose a linear threshold model of contagion where nodes switch state if their "perceived" proportion of active neighbors exceeds a threshold \\tau. Namely, a node connected to $m_i$ active neighbors and $k_i-m_i$ inactive neighbors via type-$i$ links will turn active if $\\sum{c_i m_i}/\\sum{c_i k_i}$ exceeds its threshold \\tau. Under this model, we obtain the condition, probability and expected size of global spreading events. Our results extend the existing work on complex contagions in several directions by i) providing solutions for coupled random networks whose vertices are neither identical nor disjoint, (ii) highlighting the effect of content on the dyn...
The Random Walk Model Based on Bipartite Network
Zhang Man-Dun
2016-01-01
Full Text Available With the continuing development of the electronic commerce and growth of network information, there is a growing possibility for citizens to be confused by the information. Though the traditional technology of information retrieval have the ability to relieve the overload of information in some extent, it can not offer a targeted personality service based on user’s interests and activities. In this context, the recommendation algorithm arose. In this paper, on the basis of conventional recommendation, we studied the scheme of random walk based on bipartite network and the application of it. We put forward a similarity measurement based on implicit feedback. In this method, a uneven character vector is imported(the weight of item in the system. We put forward a improved random walk pattern which make use of partial or incomplete neighbor information to create recommendation information. In the end, there is an experiment in the real data set, the recommendation accuracy and practicality are improved. We promise the reality of the result of the experiment
Random sampling versus exact enumeration of attractors in random Boolean networks
Berdahl, Andrew; Shreim, Amer; Sood, Vishal; Paczuski, Maya; Davidsen, Joern [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Alberta (Canada)], E-mail: aberdahl@phas.ucalgary.ca
2009-04-15
We clarify the effect different sampling methods and weighting schemes have on the statistics of attractors in ensembles of random Boolean networks (RBNs). We directly measure the cycle lengths of attractors and the sizes of basins of attraction in RBNs using exact enumeration of the state space. In general, the distribution of attractor lengths differs markedly from that obtained by randomly choosing an initial state and following the dynamics to reach an attractor. Our results indicate that the former distribution decays as a power law with exponent 1 for all connectivities K>1 in the infinite system size limit. In contrast, the latter distribution decays as a power law only for K=2. This is because the mean basin size grows linearly with the attractor cycle length for K>2, and is statistically independent of the cycle length for K=2. We also find that the histograms of basin sizes are strongly peaked at integer multiples of powers of two for K<3.
Enhancing the Performance of Random Access Networks with Random Packet CDMA and Joint Detection
Behrouz Farhang-Boroujeny
2008-09-01
Full Text Available Random packet CDMA (RP-CDMA is a recently proposed random transmission scheme which has been designed from the beginning as a cross-layer method to overcome the restrictive nature of the Aloha protocol. Herein, we more precisely model its performance and investigate throughput and network stability. In contrast to previous works, we adopt the spread Aloha model for header transmission, and the performance of different joint detection methods for the payload data is investigated. Furthermore, we introduce performance measures for multiple access systems based on the diagonal elements of a modified multipacket reception matrix, and show that our measures describe the upper limit of the vector of stable arrival rates for a finite number of users. Finally, we simulate queue sizes and throughput characteristics of RP-CDMA with various receiver structures and compare them to spread Aloha.
Power-law relations in random networks with communities
Stegehuis, Clara; van Leeuwaarden, Johan S H
2016-01-01
Most random graph models are locally tree-like - do not contain short cycles - which makes them unfit for modeling networks with a community structure. We introduce the hierarchical configuration model (HCM), a generalization of the configuration model that includes community structures, while properties such as the size of the giant component, and the size of the giant percolating cluster under bond percolation can still be derived analytically. Furthermore, viewing real-world networks as realizations of the HCM reveals two previously unobserved power-law relations: between the number of edges inside a community and the community sizes, and between the number of edges going out of a community and the community sizes. Many real-world networks have both a community structure and a power-law degree distribution. We relate the power-law exponent $\\tau$ of the degree distribution with the power-law exponent of the community size distribution $\\gamma$. In the special case of dense communities, this relation takes ...
Influence of weight heterogeneity on random walks in scale-free networks
Li, Ling; Guan, Jihong; Qi, Zhaohui
2016-07-01
Many systems are best described by weighted networks, in which the weights of the edges are heterogeneous. In this paper, we focus on random walks in weighted network, investigating the impacts of weight heterogeneity on the behavior of random walks. We study random walks in a family of weighted scale-free tree-like networks with power-law weight distribution. We concentrate on three cases of random walk problems: with a trap located at a hub node, a leaf adjacent to a hub node, and a farthest leaf node from a hub. For all these cases, we calculate analytically the global mean first passage time (GMFPT) measuring the efficiency of random walk, as well as the leading scaling of GMFPT. We find a significant decrease in the dominating scaling of GMFPT compared with the corresponding binary networks in all three random walk problems, which implies that weight heterogeneity has a significant influence on random walks in scale-free networks.
Information Filtering via Biased Random Walk on Coupled Social Network
Da-Cheng Nie
2014-01-01
Full Text Available The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users’ purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendation results based on both social interests and users’ preference. Numerical analyses on two real data sets, Epinions and Friendfeed, demonstrate the improvement of recommendation performance by taking social interests into account, and experimental results show that our algorithm can alleviate the user cold-start problem more effectively compared with the mass diffusion and user-based collaborative filtering methods.
Incorporating Contact Network Structure in Cluster Randomized Trials
Staples, Patrick C; Onnela, Jukka-Pekka
2015-01-01
Whenever possible, the efficacy of a new treatment, such as a drug or behavioral intervention, is investigated by randomly assigning some individuals to a treatment condition and others to a control condition, and comparing the outcomes between the two groups. Often, when the treatment aims to slow an infectious disease, groups or clusters of individuals are assigned en masse to each treatment arm. The structure of interactions within and between clusters can reduce the power of the trial, i.e. the probability of correctly detecting a real treatment effect. We investigate the relationships among power, within-cluster structure, between-cluster mixing, and infectivity by simulating an infectious process on a collection of clusters. We demonstrate that current power calculations may be conservative for low levels of between-cluster mixing, but failing to account for moderate or high amounts can result in severely underpowered studies. Power also depends on within-cluster network structure for certain kinds of i...
Minimum spanning trees and random resistor networks in d dimensions.
Read, N
2005-09-01
We consider minimum-cost spanning trees, both in lattice and Euclidean models, in d dimensions. For the cost of the optimum tree in a box of size L , we show that there is a correction of order L(theta) , where theta or =1 . The arguments all rely on the close relation of Kruskal's greedy algorithm for the minimum spanning tree, percolation, and (for some arguments) random resistor networks. The scaling of the entropy and free energy at small nonzero T , and hence of the number of near-optimal solutions, is also discussed. We suggest that the Steiner tree problem is in the same universality class as the minimum spanning tree in all dimensions, as is the traveling salesman problem in two dimensions. Hence all will have the same value of theta=-3/4 in two dimensions.
Damage spreading in spatial and small-world random boolean networks
Lu, Qiming [Los Alamos National Laboratory; Teuscher, Christof [Los Alamos National Laboratory
2008-01-01
Random Boolean Networks (RBNs) are often used as generic models for certain dynamics of complex systems, ranging from social networks, neural networks, to gene or protein interaction networks. Traditionally, RBNs are interconnected randomly and without considering any spatial arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ({bar K} << 1) and that the critical connectivity of stability K{sub s} changes compared to random networks. At higher {bar K}, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key trade-offs between damage spreading (robustness), the network wiring cost, and the network's communication characteristics.
NCSA: A New Protocol for Random Multiple Access Based on Physical Layer Network Coding
Bui, Huyen Chi; Boucheret, Marie-Laure
2010-01-01
This paper introduces a random multiple access method for satellite communications, named Network Coding-based Slotted Aloha (NCSA). The goal is to improve diversity of data bursts on a slotted-ALOHA-like channel thanks to error correcting codes and Physical-layer Network Coding (PNC). This scheme can be considered as a generalization of the Contention Resolution Diversity Slotted Aloha (CRDSA) where the different replicas of this system are replaced by the different parts of a single word of an error correcting code. The performance of this scheme is first studied through a density evolution approach. Then, simulations confirm the CRDSA results by showing that, for a time frame of $400$ slots, the achievable total throughput is greater than $0.7\\times C$, where $C$ is the maximal throughput achieved by a centralized scheme. This paper is a first analysis of the proposed scheme which open several perspectives. The most promising approach is to integrate collided bursts into the decoding process in order to im...
Pal, S P; Kumar, S; Pal, Sudebkumar Prasant; Singh, Sudhir Kumar; Kumar, Somesh
2003-01-01
In this paper we show that sufficient multi-partite quantum entanglement helps in fair and unbiased election of a leader in a distributed network of processors with only linear classical communication complexity. We show that a total of $O(log n)$ distinct multi-partite maximally entanglement sets (ebits) are capable of supporting such a protocol in the presence of nodes that may lie and thus be biased. Here, $n$ is the number of nodes in the network. We also demonstrate the difficulty of performing unbiased and fair election of a leader with linear classical communication complexity in the absence of quantum entanglement even if all nodes have perfect random bit generators. We show that the presence of a sufficient number $O(n/log n)$ of biased agents leads to a non-zero limiting probability of biased election of the leader, whereas, the presence of a smaller number $O(log n)$ of biased agents matters little. We define two new related complexity classes motivated by the our leader election problem and discus...
Naparstek, Oshri; Leshem, Amir
2013-01-01
In this paper we analyze the expected time complexity of the auction algorithm for the matching problem on random bipartite graphs. We prove that the expected time complexity of the auction algorithm for bipartite matching is $O\\left(\\frac{N\\log^2(N)}{\\log\\left(Np\\right)}\\right)$ on sequential machines. This is equivalent to other augmenting path algorithms such as the HK algorithm. Furthermore, we show that the algorithm can be implemented on parallel machines with $O(\\log(N))$ processors an...
Brendle, Joerg
2016-01-01
We show that, consistently, there can be maximal subtrees of P (omega) and P (omega) / fin of arbitrary regular uncountable size below the size of the continuum. We also show that there are no maximal subtrees of P (omega) / fin with countable levels. Our results answer several questions of Campero, Cancino, Hrusak, and Miranda.
Helen Lunt
Full Text Available BACKGROUND: In research clinic settings, overweight adults undertaking HIIT (high intensity interval training improve their fitness as effectively as those undertaking conventional walking programs but can do so within a shorter time spent exercising. We undertook a randomized controlled feasibility (pilot study aimed at extending HIIT into a real world setting by recruiting overweight/obese, inactive adults into a group based activity program, held in a community park. METHODS: Participants were allocated into one of three groups. The two interventions, aerobic interval training and maximal volitional interval training, were compared with an active control group undertaking walking based exercise. Supervised group sessions (36 per intervention were held outdoors. Cardiorespiratory fitness was measured using VO2max (maximal oxygen uptake, results expressed in ml/min/kg, before and after the 12 week interventions. RESULTS: On ITT (intention to treat analyses, baseline (N = 49 and exit (N = 39 [Formula: see text]O2 was 25.3±4.5 and 25.3±3.9, respectively. Participant allocation and baseline/exit VO2max by group was as follows: Aerobic interval training N = 16, 24.2±4.8/25.6±4.8; maximal volitional interval training N = 16, 25.0±2.8/25.2±3.4; walking N = 17, 26.5±5.3/25.2±3.6. The post intervention change in VO2max was +1.01 in the aerobic interval training, -0.06 in the maximal volitional interval training and -1.03 in the walking subgroups. The aerobic interval training subgroup increased VO2max compared to walking (p = 0.03. The actual (observed, rather than prescribed time spent exercising (minutes per week, ITT analysis was 74 for aerobic interval training, 45 for maximal volitional interval training and 116 for walking (p = 0.001. On descriptive analysis, the walking subgroup had the fewest adverse events. CONCLUSIONS: In contrast to earlier studies, the improvement in cardiorespiratory fitness in a
Van Roie, Evelien; Bautmans, Ivan; Boonen, Steven; Coudyzer, Walter; Kennis, Eva; Delecluse, Christophe
2013-04-01
It remains controversial whether maximal effort attained by high external resistance is required to optimize muscle adaptation to strengthening exercise. Here, we compared different training protocols reaching maximal effort with either high-resistance (HImax, 80% of 1-repetition maximum [1RM]) or low-resistance (LOmax, ≤40% 1RM). Thirty-six young volunteers were randomly assigned to 9 weeks of leg extension training at either HImax (1 set of 10-12 repetitions at 80% 1RM), LO (1 set of 10-12 repetitions at 40% 1RM, no maximal effort), or LOmax (1 set of 10-12 repetitions at 40% 1RM, preceded [no rest] by 60 repetitions at 20-25% 1RM). Knee extension 1RM was measured preintervention and postintervention and before the 7th, 13th, and 19th training sessions. Preintervention and postintervention, knee extensor static (PTstat) and dynamic (PTdyn) peak torque, maximal work (MW), and speed of movement at 20% (S20), 40% (S40), and 60% (S60) of PTstat were recorded with a Biodex dynamometer. All the groups showed a significant increase in 1RM, with a greater improvement in HImax from the 13th session on (p < 0.05). The HImax was the only group that significantly increased PTstat (+7.4 ± 8.1%, p = 0.01). The LOmax showed a significantly greater increase in S20 (+6.0 ± 3.2%), PTdyn (+9.8 ± 5.6%), and MW (+15.1 ± 10.6%) than both HImax and LO (p = 0.044 for S20, p = 0.030 for PTdyn, p = 0.025 for MW) and was the only group that increased in S40 (+7.7 ± 9.7%, p = 0.032). In conclusion, significant differences between HImax and LOmax on force-velocity characteristics of the knee extensors were found, although maximal effort was achieved in both training regimens. Thus, LOmax may not be considered as a replacement for HImax but rather as an alternative with different training-specific adaptations.
Connie Ko
2016-08-01
Full Text Available Recent research into improving the effectiveness of forest inventory management using airborne LiDAR data has focused on developing advanced theories in data analytics. Furthermore, supervised learning as a predictive model for classifying tree genera (and species, where possible has been gaining popularity in order to minimize this labor-intensive task. However, bottlenecks remain that hinder the immediate adoption of supervised learning methods. With supervised classification, training samples are required for learning the parameters that govern the performance of a classifier, yet the selection of training data is often subjective and the quality of such samples is critically important. For LiDAR scanning in forest environments, the quantification of data quality is somewhat abstract, normally referring to some metric related to the completeness of individual tree crowns; however, this is not an issue that has received much attention in the literature. Intuitively the choice of training samples having varying quality will affect classification accuracy. In this paper a Diversity Index (DI is proposed that characterizes the diversity of data quality (Qi among selected training samples required for constructing a classification model of tree genera. The training sample is diversified in terms of data quality as opposed to the number of samples per class. The diversified training sample allows the classifier to better learn the positive and negative instances and; therefore; has a higher classification accuracy in discriminating the “unknown” class samples from the “known” samples. Our algorithm is implemented within the Random Forests base classifiers with six derived geometric features from LiDAR data. The training sample contains three tree genera (pine; poplar; and maple and the validation samples contains four labels (pine; poplar; maple; and “unknown”. Classification accuracy improved from 72.8%; when training samples were
A simple consensus algorithm for distributed averaging in random geographical networks
Mahdi Jalili
2012-09-01
Random geographical networks are realistic models for wireless sensor networks which are used in many applications. Achieving average consensus is very important in sensor networks and the faster the consensus is, the durable the sensors’ life, and thus, the better the performance of the network. In this paper we compared the performance of a number of linear consensus algorithms with application to distributed averaging in random geographical networks. Interestingly, the simplest algorithm – where only the degree of receiving nodes is needed for the averaging – had the best performance in terms of the consensus time. Furthermore, we proved that the network has guaranteed convergence with this simple algorithm.
Damage Spreading in Spatial and Small-world Random Boolean Networks
Lu, Qiming [Fermilab; Teuscher, Christof [Portland State U.
2014-02-18
The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean Networks (RBNs) are commonly used a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ($\\bar{K} \\ll 1$) and that the critical connectivity of stability $K_s$ changes compared to random networks. At higher $\\bar{K}$, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.
Chow, Sy-Miin; Lu, Zhaohua; Sherwood, Andrew; Zhu, Hongtu
2016-03-01
The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation-maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed.
R. Dhanasekaran
2014-01-01
Full Text Available Large number of low power, tiny radio jammers are constituting a Distributed Jammer Network (DJN is used nowadays to cause a Denial of Service (DoS attack on a Distributed Wireless Network (DWN. Using NANO technologies, it is possible to build huge number of tiny jammers in millions, if not more. The Denial of Service (DoS attacks in Distributed Wireless Network (DWN using Distributed Jammer Network (DJN considering each of them as separate Poisson Random Process. In an integrated approach, in this study, we advocate the more natural Birth-Death Random Process route to study the impact of Distributed Jammer Network (DJN on the connectivity of Distributed Wireless Network (DWN. We express that the Distributed Jammer Network (DJN can root a phase transition in the performance of the target network. We use Birth-Death Random Process (BDRP route for this phase transition to evaluate the collision of Distributed Jammer Network (DJN on the connectivity and global percolation of the target network. This study confirms the global percolation of Distributed Wireless Network (DWN is definite when the Distributed Jammer Network (DJN is not more significant.
Random sequential renormalization of networks: application to critical trees.
Bizhani, Golnoosh; Sood, Vishal; Paczuski, Maya; Grassberger, Peter
2011-03-01
We introduce the concept of random sequential renormalization (RSR) for arbitrary networks. RSR is a graph renormalization procedure that locally aggregates nodes to produce a coarse grained network. It is analogous to the (quasi)parallel renormalization schemes introduced by C. Song et al. [C. Song et al., Nature (London) 433, 392 (2005)] and studied by F. Radicchi et al. [F. Radicchi et al., Phys. Rev. Lett. 101, 148701 (2008)], but much simpler and easier to implement. Here we apply RSR to critical trees and derive analytical results consistent with numerical simulations. Critical trees exhibit three regimes in their evolution under RSR. (i) For N₀{ν}≲Nrenormalization and N₀ is the initial size of the tree, RSR is described by a mean-field theory, and fluctuations from one realization to another are small. The exponent ν=1/2 is derived using random walk and other arguments. The degree distribution becomes broader under successive steps, reaching a power law p{k}~1/k{γ} with γ=2 and a variance that diverges as N₀¹/² at the end of this regime. Both of these latter results are obtained from a scaling theory. (ii) For N₀{ν{star}}≲N ≲ N₀¹/², with ν_{star}≈1/4 hubs develop, and fluctuations between different realizations of the RSR are large. Trees are short and fat with an average radius that is O(1). Crossover functions exhibiting finite-size scaling in the critical region N~N₀¹/²→∞ connect the behaviors in the first two regimes. (iii) For N ≲ N₀{ν{star}}, star configurations appear with a central hub surrounded by many leaves. The distribution of stars is broadly distributed over this range. The scaling behaviors found under RSR are identified with a continuous transition in a process called "agglomerative percolation" (AP), with the coarse-grained nodes in RSR corresponding to clusters in AP that grow by simultaneously attaching to all their neighboring clusters.
Complementary feeding: a Global Network cluster randomized controlled trial
Pasha Omrana
2011-01-01
Full Text Available Abstract Background Inadequate and inappropriate complementary feeding are major factors contributing to excess morbidity and mortality in young children in low resource settings. Animal source foods in particular are cited as essential to achieve micronutrient requirements. The efficacy of the recommendation for regular meat consumption, however, has not been systematically evaluated. Methods/Design A cluster randomized efficacy trial was designed to test the hypothesis that 12 months of daily intake of beef added as a complementary food would result in greater linear growth velocity than a micronutrient fortified equi-caloric rice-soy cereal supplement. The study is being conducted in 4 sites of the Global Network for Women's and Children's Health Research located in Guatemala, Pakistan, Democratic Republic of the Congo (DRC and Zambia in communities with toddler stunting rates of at least 20%. Five clusters per country were randomized to each of the food arms, with 30 infants in each cluster. The daily meat or cereal supplement was delivered to the home by community coordinators, starting when the infants were 6 months of age and continuing through 18 months. All participating mothers received nutrition education messages to enhance complementary feeding practices delivered by study coordinators and through posters at the local health center. Outcome measures, obtained at 6, 9, 12, and 18 months by a separate assessment team, included anthropometry; dietary variety and diversity scores; biomarkers of iron, zinc and Vitamin B12 status (18 months; neurocognitive development (12 and 18 months; and incidence of infectious morbidity throughout the trial. The trial was supervised by a trial steering committee, and an independent data monitoring committee provided oversight for the safety and conduct of the trial. Discussion Findings from this trial will test the efficacy of daily intake of meat commencing at age 6 months and, if beneficial, will
Throughput vs. Delay in Lossy Wireless Mesh Networks with Random Linear Network Coding
Hundebøll, Martin; Pahlevani, Peyman; Roetter, Daniel Enrique Lucani
2014-01-01
This work proposes a new protocol applying on– the–fly random linear network coding in wireless mesh net- works. The protocol provides increased reliability, low delay, and high throughput to the upper layers, while being oblivious to their specific requirements. This seemingly conflicting goals ...... and evaluated in a real test bed with Raspberry Pi devices. We show that order of magnitude gains in throughput over plain TCP are possible with moderate losses and up to two fold improvement in per packet delay in our results....
Pei-Yu Chen
2013-01-01
Full Text Available With more and more mobile device users, an increasingly important and critical issue is how to efficiently evaluate mobile network survivability. In this paper, a novel metric called Average Degree of Disconnectivity (Average DOD is proposed, in which the concept of probability is calculated by the contest success function. The DOD metric is used to evaluate the damage degree of the network, where the larger the value of the Average DOD, the more the damage degree of the network. A multiround network attack-defense scenario as a mathematical model is used to support network operators to predict all the strategies both cyber attacker and network defender would likely take. In addition, the Average DOD would be used to evaluate the damage degree of the network. In each round, the attacker could use the attack resources to launch attacks on the nodes of the target network. Meanwhile, the network defender could reallocate its existing resources to recover compromised nodes and allocate defense resources to protect the survival nodes of the network. In the approach to solving this problem, the “gradient method” and “game theory” are adopted to find the optimal resource allocation strategies for both the cyber attacker and mobile network defender.
The multilevel p2 model : A random effects model for the analysis of multiple social networks
Zijlstra, B.J.H.; van Duijn, M.A.J.; Snijders, T.A.B.
2006-01-01
The p2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p2 model is proposed for the case of multiple observations of social networks, for example, in a samp
K B Athreya
2009-09-01
It is shown that (i) every probability density is the unique maximizer of relative entropy in an appropriate class and (ii) in the class of all pdf that satisfy $\\int fh_id_=_i$ for $i=1,2,\\ldots,\\ldots k$ the maximizer of entropy is an $f_0$ that is proportional to $\\exp(\\sum c_i h_i)$ for some choice of $c_i$. An extension of this to a continuum of constraints and many examples are presented.
Lin, Huifa; Shin, Won-Yong
2017-01-01
We study secondary random access in multi-input multi-output cognitive radio networks, where a slotted ALOHA-type protocol and successive interference cancellation are used. We first introduce three types of transmit beamforming performed by secondary users, where multiple antennas are used to suppress the interference at the primary base station and/or to increase the received signal power at the secondary base station. Then, we show a simple decentralized power allocation along with the equivalent single-antenna conversion. To exploit the multiuser diversity gain, an opportunistic transmission protocol is proposed, where the secondary users generating less interference are opportunistically selected, resulting in a further reduction of the interference temperature. The proposed methods are validated via computer simulations. Numerical results show that increasing the number of transmit antennas can greatly reduce the interference temperature, while increasing the number of receive antennas leads to a reduction of the total transmit power. Optimal parameter values of the opportunistic transmission protocol are examined according to three types of beamforming and different antenna configurations, in terms of maximizing the cognitive transmission capacity. All the beamforming, decentralized power allocation, and opportunistic transmission protocol are performed by the secondary users in a decentralized manner, thus resulting in an easy implementation in practice.
Completely random measures for modelling block-structured sparse networks
Herlau, Tue; Schmidt, Mikkel Nørgaard; Mørup, Morten
2016-01-01
Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world network...... is not significantly more difficult to implement than existing approaches to block-modelling and performs well on real network datasets.......Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world networks...... [2014] proposed the use of a different notion of exchangeability due to Kallenberg [2006] and obtained a network model which admits power-law behaviour while retaining desirable statistical properties, however this model does not capture latent vertex traits such as block-structure. In this work we re...
Cannon, Christopher P; Cariou, Bertrand; Blom, Dirk; McKenney, James M; Lorenzato, Christelle; Pordy, Robert; Chaudhari, Umesh; Colhoun, Helen M
2015-05-14
To compare the efficacy [low-density lipoprotein cholesterol (LDL-C) lowering] and safety of alirocumab, a fully human monoclonal antibody to proprotein convertase subtilisin/kexin 9, compared with ezetimibe, as add-on therapy to maximally tolerated statin therapy in high cardiovascular risk patients with inadequately controlled hypercholesterolaemia. COMBO II is a double-blind, double-dummy, active-controlled, parallel-group, 104-week study of alirocumab vs. ezetimibe. Patients (n = 720) with high cardiovascular risk and elevated LDL-C despite maximal doses of statins were enrolled (August 2012-May 2013). This pre-specified analysis was conducted after the last patient completed 52 weeks. Patients were randomized to subcutaneous alirocumab 75 mg every 2 weeks (plus oral placebo) or oral ezetimibe 10 mg daily (plus subcutaneous placebo) on a background of statin therapy. At Week 24, mean ± SE reductions in LDL-C from baseline were 50.6 ± 1.4% for alirocumab vs. 20.7 ± 1.9% for ezetimibe (difference 29.8 ± 2.3%; P < 0.0001); 77.0% of alirocumab and 45.6% of ezetimibe patients achieved LDL-C <1.8 mmol/L (P < 0.0001). Mean achieved LDL-C at Week 24 was 1.3 ± 0.04 mmol/L with alirocumab and 2.1 ± 0.05 mmol/L with ezetimibe, and were maintained to Week 52. Alirocumab was generally well tolerated, with no evidence of an excess of treatment-emergent adverse events. In patients at high cardiovascular risk with inadequately controlled LDL-C, alirocumab achieved significantly greater reductions in LDL-C compared with ezetimibe, with a similar safety profile. clinicaltrials.gov Identifier: NCT01644188. © The Author 2015. Published by Oxford University Press on behalf of the European Society of Cardiology.
一种新型的社会网络影响最大化算法%A New Hybrid Algorithm for Influence Maximization in Social Networks
田家堂; 王轶彤; 冯小军
2011-01-01
社会网络中影响最大化问题是对于给定k值,寻找k个具有最大影响范围的节点集.这是一个优化问题并且是NP-完全的.Kemple和Kleinberg提出具有较好影响范围的贪心算法,但其时间复杂度很高,不能适用在大型社会网络中,并且不能保证最好的影响范围.文中利用线性阈值模型的“影响力积累”特性,提出了一个该模型下影响最大化算法的框架,并在此框架基础上给出一个新的算法HPG.HPG综合考虑网络的结构特性和传播特性,首先启发式选择PI值最大的节点,然后寻找最具影响力的节点.实验结果显示HPG在最终影响范围和运行时间上都获得比贪心算法更好的效果.%Influence maximization is a problem of finding a small subset of nodes (target set) in a social network that could maximize the spread of influence. This optimization problem of influence maximization is NP-hard under several most widely studied diffusion models and is even challenging for current online social networks which contain both positive and negative relations. Kemple and Kleinberg proposed a natural climbing-hill greedy algorithm that chooses the nodes which could provide a good marginal influence. This greedy algorithm has large spread of influence, but is very costly and cannot be applied to large social networks. Also, greedy algorithm could not guarantee the best influence spread. In this paper, we propose a framework on the linear threshold model and a hybrid potential-influence greedy algorithm (HPG) which can make good use of the "influence accumulation" property of the linear threshold model. Considering the network structure and propagation characteristics, HPG algorithm first heuristically choose half of the initial seeds with the biggest potential influence (PI) and then greedily choose the other half initial seeds with the most influence. Experiments are conducted comprehensively on different real datasets (including weighted social
Renormalization procedure for random tensor networks and the canonical tensor model
Sasakura, Naoki
2015-01-01
We discuss a renormalization procedure for random tensor networks, and show that the corresponding renormalization-group flow is given by the Hamiltonian vector flow of the canonical tensor model, which is a discretized model of quantum gravity. The result is the generalization of the previous one concerning the relation between the Ising model on random networks and the canonical tensor model with N=2. We also prove a general theorem which relates discontinuity of the renormalization-group flow and the phase transitions of random tensor networks.
Influence Maximization on Multiple Social Networks%多社交网络的影响力最大化分析
李国良; 楚娅萍; 冯建华; 徐尧强
2016-01-01
影响力最大化旨在从网络中识别 k 个节点，使得通过这 k 个节点产生的影响传播范围最大。该问题在病毒营销领域具有重要的应用背景，它已经引起了学术界和工业界的广泛研究。该文作者观察到已有的研究工作大多数只是针对单一网络，即在给定的一个网络上识别 k 个节点使得其在该网络上产生最大的影响范围；然而，随着社交网络的普及，丰富多样的社交平台不断涌现，以满足不同的社交需求，这使得社交人群不被局限在一个网络内，而是分布在不同的社交网络上。这种变化的一个直接影响是使得基于病毒式营销的应用，例如单一网络上的产品推广愈加不能满足推广的广度需求，很可能是单一网络上的用户量不能达到推广的目标人群数量，又或者广告商期望在多个网络平台上找到 k 个用户以最大化影响传播范围。为此，在文中，作者研究多社交网络上的影响力最大化问题。该文首先仔细地研究了影响力最大化问题在单一网络和多社交网络上的不同，并提出了实体的自传播特性以在多个网络之间建立联系。之后，作者提出了多社交网络上的影响计算模型来建模节点间的影响力，然后扩展了基于树的算法模型以适应多社交网络上的影响力最大化问题。基于所提出的影响计算模型和扩展的基于树的算法模型，作者提出了多种策略的优化算法。例如通过深层次挖掘自模特性来避免冗余计算，通过使用影响增益上界近似准确的增益来加速种子选取过程等，最后通过真实数据集上的实验表明文中所提方法在性能和影响范围上都优于已有的算法。%Influence Maximization aims to identify k nodes from a network such that the influence spread invoked by the k nodes is maximized.It has various real applications in viral-marketing areas and has been
Financial Time Series Prediction Using Elman Recurrent Random Neural Networks.
Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli
2016-01-01
In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.
Universality of the emergent scaling in finite random binary percolation networks
2017-01-01
In this paper we apply lattice models of finite binary percolation networks to examine the effects of network configuration on macroscopic network responses. We consider both square and rectangular lattice structures in which bonds between nodes are randomly assigned to be either resistors or capacitors. Results show that for given network geometries, the overall normalised frequency-dependent electrical conductivities for different capacitor proportions are found to converge at a characteristic frequency. Networks with sufficiently large size tend to share the same convergence point uninfluenced by the boundary and electrode conditions, can be then regarded as homogeneous media. For these networks, the span of the emergent scaling region is found to be primarily determined by the smaller network dimension (width or length). This study identifies the applicability of power-law scaling in random two phase systems of different topological configurations. This understanding has implications in the design and testing of disordered systems in diverse applications. PMID:28207872
Rosvall, M
2010-01-01
To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation that reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network, the optimal number of levels and modular partition at each level, with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines:...
Parameters affecting the resilience of scale-free networks to random failures.
Link, Hamilton E.; LaViolette, Randall A.; Lane, Terran (University of New Mexico, Albuquerque, NM); Saia, Jared (University of New Mexico, Albuquerque, NM)
2005-09-01
It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. in (1) study scale-free networks including some which approximate the measured degree distribution of the Internet. Their results suggest that if each node in this network failed independently with probability 0.99, most of the remaining nodes would still be connected in a giant component. In this paper, we show that a large and important subclass of scale-free networks are not robust to massive numbers of random node deletions. In particular, we study scale-free networks which have minimum node degree of 1 and a power-law degree distribution beginning with nodes of degree 1 (power-law networks). We show that, in a power-law network approximating the Internet's reported distribution, when the probability of deletion of each node is 0.5 only about 25% of the surviving nodes in the network remain connected in a giant component, and the giant component does not persist beyond a critical failure rate of 0.9. The new result is partially due to improved analytical accommodation of the large number of degree-0 nodes that result after node deletions. Our results apply to power-law networks with a wide range of power-law exponents, including Internet-like networks. We give both analytical and empirical evidence that such networks are not generally robust to massive random node deletions.
孙彦景; 田红; 王迎
2012-01-01
According to the problem of energy hole which caused by unbalanced consumption in wireless sensor networks (WSN), the paper proposes the multiple Sinks cooperative mobility optimization algorithm to maximize the lifetime for wireless sensor networks. In this algorithm, the interest region is divided into a quantity of virtual cells. It cooperates with ACO(Ant Colony Optimization) in the mobility of multiple Sinks based on network conditions. The time of Sinks sojourning at optional sites is converted to LP( Linear Program) and extending the lifetime of network. Simulation results indicate that LP-ACO ( Linear Program-Ant Colony Optimization) is effective on balancing the energy consumption It not only makes the network lifetime significantly longer than static, deployment ( STATIC) and random movement( RDM)of Sinks,but also more scalable.%针对无线传感器网络中因能量消耗不平衡造成的“能量洞”问题,提出多Sink协同移动的最大化网络生存期优化算法.该算法将监测区域分割成有限个虚拟单元格,通过蚁群优化算法ACO(Ant Colony Optimization)协同多Sink节点移动;同时,将多Sink节点在备选位置的停留时间归结为LP(Linear Program),最大化网络寿命.仿真结果表明,LP-ACO(Linear Program-Ant Colony Optimization)较好地均衡了传感器网络节点间的负载,网络寿命优于多Sink节点静态部署(STATIC)和随机移动(RDM)时场景,且具有良好的可扩展性.
Electrospun dye-doped fiber networks: lasing emission from randomly distributed cavities
Krammer, Sarah; Vannahme, Christoph; Smith, Cameron
2015-01-01
Dye-doped polymer fiber networks fabricated with electrospinning exhibit comb-like laser emission. We identify randomly distributed ring resonators being responsible for lasing emission by making use of spatially resolved spectroscopy. Numerical simulations confirm this result quantitatively....
Enhanced Differentiated Surveillance for Static and Random Mobile Sensor Networks
Xue-Qin Zhu
2011-10-01
Full Text Available Wireless integrated sensor networks, which include collecting, processing data and communication, are used more and more widely for its low cost and convenient deployment. Nowadays the researches of sensor networks are fairly active. The security is one of the key questions in sensor networks. Intrusion detection is a kind of network security technologies used to detect any behavior that will damage or attempt to damage system confidentiality, integrality or availability, and it can provide the reasonable supplement to intrusion prevention mechanism, and construct a second wall of defense for network and system. This paper mainly focuses on the energy efficient intrusion detection technology. According to the characteristics of sensor network and the specialty of the invasions in sensor network, this paper presents an intrusion detection model based on statistics anomaly in sensor networks. The algorithm establishes models for the normal state of the nodes, and makes decisions through the deviation degree of observed value. The algorithm is fault-tolerant for non-invasion anomaly when the communication between nodes break down or the accident wrongly create anomaly.
Synchronization in the random-field Kuramoto model on complex networks
Lopes, M. A.; Lopes, E. M.; Yoon, S.; Mendes, J. F. F.; Goltsev, A. V.
2016-07-01
We study the impact of random pinning fields on the emergence of synchrony in the Kuramoto model on complete graphs and uncorrelated random complex networks. We consider random fields with uniformly distributed directions and homogeneous and heterogeneous (Gaussian) field magnitude distribution. In our analysis, we apply the Ott-Antonsen method and the annealed-network approximation to find the critical behavior of the order parameter. In the case of homogeneous fields, we find a tricritical point above which a second-order phase transition gives place to a first-order phase transition when the network is either fully connected or scale-free with the degree exponent γ >5 . Interestingly, for scale-free networks with 2 networks, although the fields increase the critical coupling for γ >3 . Our simulations support these analytical results.
Performance Evaluation of the Random Replacement Policy for Networks of Caches
Gallo, Massimo; Muscariello, Luca; Simonian, Alain; Tanguy, Christian
2012-01-01
The overall performance of content distribution networks as well as recently proposed information-centric networks rely on both memory and bandwidth capacities. In this framework, the hit ratio is the key performance indicator which captures the bandwidth / memory tradeo? for a given global performance.This paper focuses on the estimation of the hit ratio in a network of caches that employ the Random replacement policy. Assuming that requests are independent and identically distributed, general expressions of miss probabilities for a single Random cache are provided as well as exact results for specif?c popularity distributions. Moreover, for any Zipf popularity distribution with exponent ? > 1, we obtain asymptotic equivalents for the miss probability in the case of large cache size. We extend the analysis to networks of Random caches, when the topology is either a line or a homogeneous tree. In that case, approximations for miss probabilities across the network are derived by assuming that miss events at an...
Modified Random Forest Approach for Resource Allocation in 5G Network
Parnika De
2016-11-01
Full Text Available According to annual visual network index (VNI report by the year 2020, 4G will reach its maturity and incremental approach will not meet demand. Only way is to switch to newer generation of mobile technology called as 5G. Resource allocation is critical problem that impact 5G Network operation critically. Timely and accurate assessment of underutilized bandwidth to primary user is necessary in order to utilize it efficiently for increasing network efficiency. This paper presents a decision making system at Fusion center using modified Random Forest. Modified Random Forest is first trained using Database accumulated by measuring different network parameters and can take decision on allocation of resources. The Random Forest is retrained after fixed time interval, considering dynamic nature of network. We also test its performance in comparison with existing AND/OR logic decision logic at Fusion Center
Effective trapping of random walkers in complex networks
Hwang, S.; Lee, D.-S.; Kahng, B.
2012-04-01
Exploring the World Wide Web has become one of the key issues in information science, specifically in view of its application to the PageRank-like algorithms used in search engines. The random walk approach has been employed to study such a problem. The probability of return to the origin (RTO) of random walks is inversely related to how information can be accessed during random surfing. We find analytically that the RTO probability for a given starting node shows a crossover from a slow to a fast decay behavior with time and the crossover time increases with the degree of the starting node. We remark that the RTO probability becomes almost constant in the early-time regime as the degree exponent approaches two. This result indicates that a random surfer can be effectively trapped at the hub and supports the necessity of the random jump strategy empirically used in the Google's search engine.
Singh, Brijesh; Mahanty, Ranjit; Singh, S. P.
2013-05-01
This paper presents a framework to achieve an optimal power flow solution in a decentralized bilateral multitransaction-based market. An independent optimal dispatch solution has been used for each market. The interior point (IP)-based optimization technique has been used for finding a global economic optimal solution of the whole system. In this method, all the participants try to maximize their own profits with the help of system information announced by the operator. In the present work, a parallel algorithm has been used to find out a global optimum solution in decentralized market model. The study has been carried out on a modified IEEE-30 bus system. The results show that the suggested decentralized approach can provide a better optimal solution. The obtained results show the effectiveness of IP optimization-based optimal generator schedule and congestion management in the decentralized market.
On the hop count statistics for randomly deployed wireless sensor networks
Dulman, S.O.; Rossi, M.; Havinga, Paul J.M.; Zorzi, M.
2006-01-01
In this paper we focus on exploiting the information provided by a generally accepted and largely ignored hypothesis (the random deployment of the nodes of an ad hoc or wireless sensor network) to design improved networking protocols. Specifically, we derive the relationship between the number of ho
Probing the Extent of Randomness in Protein Interaction Networks
2008-07-11
elegans [16], Plasmodium falciparum [17], Campylobacter jejuni [18], and Homo sapiens [7]. A number of efforts to compile and, in some cases, curate the...Weighted Connectivity in Two PPI Networks. (A) Helicobacter pylori and (B) Campylobacter jejuni . For k1k2.10, probabilities of interaction P(k1,k2) were...Four PPI Networks and their DCDW Equivalents. (A) Drosophila melanogaster, (B) Campylobacter jejuni , (C) Escherichia coli (HT2), and (D) Escherichia
Financial Time Series Prediction Using Elman Recurrent Random Neural Networks
Jie Wang
2016-01-01
(ERNN, the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.
$k$-core percolation on complex networks: Comparing random, localized and targeted attacks
Yuan, Xin; Stanley, H Eugene; Havlin, Shlomo
2016-01-01
The type of malicious attack inflicting on networks greatly influences their stability under ordinary percolation in which a node fails when it becomes disconnected from the giant component. Here we study its generalization, $k$-core percolation, in which a node fails when it loses connection to a threshold $k$ number of neighbors. We study and compare analytically and by numerical simulations of $k$-core percolation the stability of networks under random attacks (RA), localized attacks (LA) and targeted attacks (TA), respectively. By mapping a network under LA or TA into an equivalent network under RA, we find that in both single and interdependent networks, TA exerts the greatest damage to the core structure of a network. We also find that for Erd\\H{o}s-R\\'{e}nyi (ER) networks, LA and RA exert equal damage to the core structure whereas for scale-free (SF) networks, LA exerts much more damage than RA does to the core structure.
Ji, Xingpei; Wang, Bo; Liu, Dichen; Dong, Zhaoyang; Chen, Guo; Zhu, Zhenshan; Zhu, Xuedong; Wang, Xunting
2016-10-01
Whether the realistic electrical cyber-physical interdependent networks will undergo first-order transition under random failures still remains a question. To reflect the reality of Chinese electrical cyber-physical system, the "partial one-to-one correspondence" interdependent networks model is proposed and the connectivity vulnerabilities of three realistic electrical cyber-physical interdependent networks are analyzed. The simulation results show that due to the service demands of power system the topologies of power grid and its cyber network are highly inter-similar which can effectively avoid the first-order transition. By comparing the vulnerability curves between electrical cyber-physical interdependent networks and its single-layer network, we find that complex network theory is still useful in the vulnerability analysis of electrical cyber-physical interdependent networks.
Random walks in weighted networks with a perfect trap: an application of Laplacian spectra.
Lin, Yuan; Zhang, Zhongzhi
2013-06-01
Trapping processes constitute a primary problem of random walks, which characterize various other dynamical processes taking place on networks. Most previous works focused on the case of binary networks, while there is much less related research about weighted networks. In this paper, we propose a general framework for the trapping problem on a weighted network with a perfect trap fixed at an arbitrary node. By utilizing the spectral graph theory, we provide an exact formula for mean first-passage time (MFPT) from one node to another, based on which we deduce an explicit expression for average trapping time (ATT) in terms of the eigenvalues and eigenvectors of the Laplacian matrix associated with the weighted graph, where ATT is the average of MFPTs to the trap over all source nodes. We then further derive a sharp lower bound for the ATT in terms of only the local information of the trap node, which can be obtained in some graphs. Moreover, we deduce the ATT when the trap is distributed uniformly in the whole network. Our results show that network weights play a significant role in the trapping process. To apply our framework, we use the obtained formulas to study random walks on two specific networks: trapping in weighted uncorrelated networks with a deep trap, the weights of which are characterized by a parameter, and Lévy random walks in a connected binary network with a trap distributed uniformly, which can be looked on as random walks on a weighted network. For weighted uncorrelated networks we show that the ATT to any target node depends on the weight parameter, that is, the ATT to any node can change drastically by modifying the parameter, a phenomenon that is in contrast to that for trapping in binary networks. For Lévy random walks in any connected network, by using their equivalence to random walks on a weighted complete network, we obtain the optimal exponent characterizing Lévy random walks, which have the minimal average of ATTs taken over all
Multistage Random Growing Small-World Networks with Power-Law Degree Distribution
LIU Jian-Guo; DANG Yan-Zhong; WANG Zhong-Tuo
2006-01-01
@@ We present a simple rule which could generate scale-free networks with very large clustering coefficient and very small average distance. These networks, called the multistage random growing networks (MRGNs), are constructed by a two-stage adding process for each new node. The analytic results of the power-law exponentγ = 3 and the clustering coefficient C = 0.81 are obtained, which agree with the simulation results approximately.
Analysis and Optimization of Sparse Random Linear Network Coding for Reliable Multicast Services
Tassi, Andrea; Chatzigeorgiou, Ioannis; Roetter, Daniel Enrique Lucani
2016-01-01
Point-to-multipoint communications are expected to play a pivotal role in next-generation networks. This paper refers to a cellular system transmitting layered multicast services to a multicast group of users. Reliability of communications is ensured via different random linear network coding (RL...... guarantees to predetermined fractions of users. The performance of the proposed optimization framework is then investigated in a LTE-A eMBMS network multicasting H.264/SVC video services....
Reuveni, Shlomi; Granek, Rony; Klafter, Joseph
2010-10-01
We present an approach to mapping between random walks and vibrational dynamics on general networks. Random walk occupation probabilities, first passage time distributions and passage probabilities between nodes are expressed in terms of thermal vibrational correlation functions. Recurrence is demonstrated equivalent to the Landau-Peierls instability. Fractal networks are analyzed as a case study. In particular, we show that the spectral dimension governs whether or not the first passage time distribution is well represented by its mean. We discuss relevance to universal features arising in protein vibrational dynamics.
Florent Besnier
Full Text Available Lifestyle combined interventions are a key strategy for preventing type-2 diabetes (T2DM in overweight or obese subjects. In this framework, LIPOXmax individualized training, based on maximal fat oxidation [MFO], may be a promising intervention to promote fat mass (FM reduction and prevent T2DM. Our primary objective was to compare three training programs of physical activity combined with a fruit- and vegetable-rich diet in reducing FM in overweight or obese women.A five months non-blinded randomized controlled trial (RCT with three parallel groups in La Réunion Island, a region where metabolic diseases are highly prevalent.One hundred and thirty-six non-diabetic obese (body mass index [BMI]: 27-40 kg/m2 young women (aged 20-40 were randomized (G1: MFO intensity; G2: 60% of VO2-peak intensity; G3: free moderate-intensity at-home exercise following good physical practices.Anthropometry (BMI, bodyweight, FM, fat-free mass, glucose (fasting plasma glucose, insulin, HOMA-IR and lipid (cholesterol and triglycerides profiles, and MFO values were measured at month-0, month-3 and month-5.At month-5, among 109 women assessed on body composition, the three groups exhibited a significant FM reduction over time (G1: -4.1±0.54 kg; G2: -4.7±0.53 kg; G3: -3.5±0.78 kg, p<0.001, respectively without inter-group differences (p = 0.135. All groups exhibited significant reductions in insulin levels or HOMA-IR index, and higher MFO values over time (p<0.001, respectively but glucose control improvement was higher in G1 than in G3 while MFO values were higher in G1 than in G2 and G3. Changes in other outcome measures and inter-group differences were not significant.In our RCT the LIPOXmax intervention did not show a superiority in reducing FM in overweight or obese women but is associated with higher MFO and better glucose control improvements. Other studies are required before proposing LIPOXmax training for the prevention of T2DM in overweight or obese women
Drug-target interaction prediction by random walk on the heterogeneous network.
Chen, Xing; Liu, Ming-Xi; Yan, Gui-Ying
2012-07-01
Predicting potential drug-target interactions from heterogeneous biological data is critical not only for better understanding of the various interactions and biological processes, but also for the development of novel drugs and the improvement of human medicines. In this paper, the method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug-target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. Compared with traditional supervised or semi-supervised methods, NRWRH makes full use of the tool of the network for data integration to predict drug-target associations. It integrates three different networks (protein-protein similarity network, drug-drug similarity network, and known drug-target interaction networks) into a heterogeneous network by known drug-target interactions and implements the random walk on this heterogeneous network. When applied to four classes of important drug-target interactions including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves previous methods in terms of cross-validation and potential drug-target interaction prediction. Excellent performance enables us to suggest a number of new potential drug-target interactions for drug development.
Representation of nonlinear random transformations by non-gaussian stochastic neural networks.
Turchetti, Claudio; Crippa, Paolo; Pirani, Massimiliano; Biagetti, Giorgio
2008-06-01
The learning capability of neural networks is equivalent to modeling physical events that occur in the real environment. Several early works have demonstrated that neural networks belonging to some classes are universal approximators of input-output deterministic functions. Recent works extend the ability of neural networks in approximating random functions using a class of networks named stochastic neural networks (SNN). In the language of system theory, the approximation of both deterministic and stochastic functions falls within the identification of nonlinear no-memory systems. However, all the results presented so far are restricted to the case of Gaussian stochastic processes (SPs) only, or to linear transformations that guarantee this property. This paper aims at investigating the ability of stochastic neural networks to approximate nonlinear input-output random transformations, thus widening the range of applicability of these networks to nonlinear systems with memory. In particular, this study shows that networks belonging to a class named non-Gaussian stochastic approximate identity neural networks (SAINNs) are capable of approximating the solutions of large classes of nonlinear random ordinary differential transformations. The effectiveness of this approach is demonstrated and discussed by some application examples.
Lo, Chun-Yi Zac; Su, Tsung-Wei; Huang, Chu-Chung; Hung, Chia-Chun; Chen, Wei-Ling; Lan, Tsuo-Hung; Lin, Ching-Po; Bullmore, Edward T
2015-07-21
Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients' networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population.
Morphology and linear-elastic moduli of random network solids.
Nachtrab, Susan; Kapfer, Sebastian C; Arns, Christoph H; Madadi, Mahyar; Mecke, Klaus; Schröder-Turk, Gerd E
2011-06-17
The effective linear-elastic moduli of disordered network solids are analyzed by voxel-based finite element calculations. We analyze network solids given by Poisson-Voronoi processes and by the structure of collagen fiber networks imaged by confocal microscopy. The solid volume fraction ϕ is varied by adjusting the fiber radius, while keeping the structural mesh or pore size of the underlying network fixed. For intermediate ϕ, the bulk and shear modulus are approximated by empirical power-laws K(phi)proptophin and G(phi)proptophim with n≈1.4 and m≈1.7. The exponents for the collagen and the Poisson-Voronoi network solids are similar, and are close to the values n=1.22 and m=2.11 found in a previous voxel-based finite element study of Poisson-Voronoi systems with different boundary conditions. However, the exponents of these empirical power-laws are at odds with the analytic values of n=1 and m=2, valid for low-density cellular structures in the limit of thin beams. We propose a functional form for K(ϕ) that models the cross-over from a power-law at low densities to a porous solid at high densities; a fit of the data to this functional form yields the asymptotic exponent n≈1.00, as expected. Further, both the intensity of the Poisson-Voronoi process and the collagen concentration in the samples, both of which alter the typical pore or mesh size, affect the effective moduli only by the resulting change of the solid volume fraction. These findings suggest that a network solid with the structure of the collagen networks can be modeled in quantitative agreement by a Poisson-Voronoi process.
Transition to chaos in random networks with cell-type-specific connectivity
Aljadeff, Johnatan; Stern, Merav; Sharpee, Tatyana
2015-01-01
In neural circuits, statistical connectivity rules strongly depend on cell-type identity. We study dynamics of neural networks with cell-type specific connectivity by extending the dynamic mean field method, and find that these networks exhibit a phase transition between silent and chaotic activity. By analyzing the locus of this transition, we derive a new result in random matrix theory: the spectral radius of a random connectivity matrix with block-structured variances. We apply our results to show how a small group of hyper-excitable neurons within the network can significantly increase the network’s computational capacity by bringing it into the chaotic regime. PMID:25768781
A new neural network model for solving random interval linear programming problems.
Arjmandzadeh, Ziba; Safi, Mohammadreza; Nazemi, Alireza
2017-05-01
This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem. Several illustrative examples are solved in support of this technique.
A Cluster-Based Random Key Revocation Protocol for Wireless Sensor Networks
Yi Jiang; Hao-Shan Shi
2008-01-01
In recent years, several random key pre distribution schemes have been proposed to bootstrap keys for encryption, but the problem of key and node revocation has received relatively little attention. In this paper, based on a random key pre-distribution scheme using clustering, we present a novel random key revocation protocol, which is suitable for large scale networks greatly and removes compromised information efficiently. The revocation protocol can guarantee network security by using less memory consumption and communication load, and combined by centralized and distributed revocation, having virtues of timeliness and veracity for revocation at the same time.
Janssen, Hans-Karl; Stenull, Olaf
2004-02-01
We investigate corrections to scaling induced by irrelevant operators in randomly diluted systems near the percolation threshold. The specific systems that we consider are the random resistor network and a class of continuous spin systems, such as the x-y model. We focus on a family of least irrelevant operators and determine the corrections to scaling that originate from this family. Our field theoretic analysis carefully takes into account that irrelevant operators mix under renormalization. It turns out that long standing results on corrections to scaling are respectively incorrect (random resistor networks) or incomplete (continuous spin systems).
Stevens, R; Reber, E
1993-01-01
The design, development and implementation of medical education software often occurs without sufficient consideration of the potential benefits that can be realized by making the software network aware. These benefits can be considerable and can greatly enhance the utilization and potential impact of the software. This article details how multiple aspects of the IMMEX problem solving project have benefited from taking maximum advantage of LAN resources.
冯凤香
2013-01-01
The weak law of large lumbers,Lp convergence and complete convergence for maximal weighted sums of （φ） mixing random matrix sequences are discussed.The discussion generalizes corresponding limit results for independent random matrix sequences to （φ） mixing random matrix sequences.%讨论了(φ)混合阵列行加权和最大值的弱收敛性、Lp收敛性和完全收敛性定理,将独立阵列的相关极限定理推广到了(φ)混合随机阵列情形.
Random Walks on Directed Networks: Inference and Respondent-driven Sampling
Malmros, Jens; Britton, Tom
2013-01-01
Respondent driven sampling (RDS) is a method often used to estimate population properties (e.g. sexual risk behavior) in hard-to-reach populations. It combines an effective modified snowball sampling methodology with an estimation procedure that yields unbiased population estimates under the assumption that the sampling process behaves like a random walk on the social network of the population. Current RDS estimation methodology assumes that the social network is undirected, i.e. that all edges are reciprocal. However, empirical social networks in general also have non-reciprocated edges. To account for this fact, we develop a new estimation method for RDS in the presence of directed edges on the basis of random walks on directed networks. We distinguish directed and undirected edges and consider the possibility that the random walk returns to its current position in two steps through an undirected edge. We derive estimators of the selection probabilities of individuals as a function of the number of outgoing...
USING THE RANDOM OF QUANTIZATION IN THE SIMULATION OF NETWORKED CONTROL SYSTEMS
V. K. Bitiukov
2014-01-01
Full Text Available Network control systems using a network channel for communication between the elements. This approach has several advantages: lower installation costs, ease of configuration, ease of diagnostics and maintenance. The use of networks in control systems poses new problems. The network characteristics make the analysis, modeling, and control of networked control systems more complex and challenging. In the simulation must consider the following factors: packet loss, packet random time over the network, the need for location records in a channel simultaneously multiple data packets with sequential transmission. Attempts to account at the same time all of these factors lead to a significant increase in the dimension of the mathematical model and, as a con-sequence, a significant computational challenges. Such models tend to have a wide application in research. However, for engineering calculations required mathematical models of small dimension, but at the same time having sufficient accuracy. Considered the networks channels with random delays and packet loss. Random delay modeled by appropriate distribution the Erlang. The probability of packet loss depends on the arrival rate of data packets in the transmission channel, and the parameters of the distribution Erlang. We propose a model of the channel in the form of a serial connection of discrete elements. Discrete elements produce independents quantization of the input signal. To change the probability of packet loss is proposed to use a random quantization input signal. Obtained a formula to determine the probability of packet loss during transmission.
Analysis of Multipath Routing in Random Ad Hoc Networks Scenario
Indrani Das
2014-10-01
Full Text Available In this study, we have proposed a multipath routing protocol for Mobile Ad Hoc Networks. Multipath routing overcomes various problems that occur in data delivery through a single path. The proposed protocol selects multiple neighbor nodes of source node to establish multiple paths towards destination. These nodes are selected based on their minimum remaining distance from destination. We have computed the length of various paths and average hops count for different node density in the network. We have considered only three paths for our evaluation. The results show that path-2 gives better results in term of hop count and path length among three paths.
On Natural Genetic Engineering: Structural Dynamism in Random Boolean Networks
Bull, Larry
2012-01-01
This short paper presents an abstract, tunable model of genomic structural change within the cell lifecycle and explores its use with simulated evolution. A well-known Boolean model of genetic regulatory networks is extended to include changes in node connectivity based upon the current cell state, e.g., via transposable elements. The underlying behaviour of the resulting dynamical networks is investigated before their evolvability is explored using a version of the NK model of fitness landscapes. Structural dynamism is found to be selected for in non-stationary environments and subsequently shown capable of providing a mechanism for evolutionary innovation when such reorganizations are inherited.
A Random-Walk Based Privacy-Preserving Access Control for Online Social Networks
You-sheng Zhou
2016-02-01
Full Text Available Online social networks are popularized with people to connect friends, share resources etc. Meanwhile, the online social networks always suffer the problem of privacy exposure. The existing methods to prevent exposure are to enforce access control provided by the social network providers or social network users. However, those enforcements are impractical since one of essential goal of social network application is to share updates freely and instantly. To better the security and availability in social network applications, a novel random walking based access control of social network is proposed in this paper. Unlike using explicit attribute based match in the existing schemes, the results from random walking are employed to securely compute L1 distance between two social network users in the presented scheme, which not only avoids the leakage of private attributes, but also enables each social network user to define access control policy independently. The experimental results show that the proposed scheme can facilitate the access control for online social network.
Analysis of Greedy Decision Making for Geographic Routing for Networks of Randomly Moving Objects
Amber Israr
2016-04-01
Full Text Available Autonomous and self-organizing wireless ad-hoc communication networks for moving objects consist of nodes, which use no centralized network infrastructure. Examples of moving object networks are networks of flying objects, networks of vehicles, networks of moving people or robots. Moving object networks have to face many critical challenges in terms of routing because of dynamic topological changes and asymmetric networks links. A suitable and effective routing mechanism helps to extend the deployment of moving nodes. In this paper an attempt has been made to analyze the performance of the Greedy Decision method (position aware distance based algorithm for geographic routing for network nodes moving according to the random waypoint mobility model. The widely used GPSR (Greedy Packet Stateless Routing protocol utilizes geographic distance and position based data of nodes to transmit packets towards destination nodes. In this paper different scenarios have been tested to develop a concrete set of recommendations for optimum deployment of distance based Greedy Decision of Geographic Routing in randomly moving objects network
Peer-Assisted Content Distribution with Random Linear Network Coding
Hundebøll, Martin; Ledet-Pedersen, Jeppe; Sluyterman, Georg
2014-01-01
Peer-to-peer networks constitute a widely used, cost-effective and scalable technology to distribute bandwidth-intensive content. The technology forms a great platform to build distributed cloud storage without the need of a central provider. However, the majority of todays peer-to-peer systems r...
Jasmine Norman
2011-10-01
Full Text Available Random Geometric Graphs have been a very influential and well-studied model of large networks, such assensor networks, where the network nodes are represented by the vertices of the RGG, and the direct connectivity between nodes is represented by the edges. This assumes homogeneous wireless nodes with uniform transmission ranges. In real life, there exist heterogeneous wireless networks in which devices have dramatically different capabilities. The connectivity of a WSN is related to the positions of nodes, and those positions are heavily affected by the method of sensor deployment. As sensors may be spread in an arbitrary manner, one of the fundamental issues in a wireless sensor network is the coverage problem. In this paper, I study connectivity and coverage in hybrid WSN based on dynamic random geometric graph.
Random-resistor network description for hopping transport in the presence of Hubbard interaction
Bleibaum, O; Bryksin, V V
2003-01-01
On the basis of the linearized rate equations for hopping electrons in the presence of Hubbard interaction we derive a random resistor network analogue of the transport equations. In contrast to the ordinary Miller-Abraham network our network has two nodes per site. The occurrence of the second node is related to the capability of the system to propagate excitations, and thus is characteristic for the interacting situation. Our random resistor network can be used for the investigation of the transport properties in alternating electric fields and for the investigation of properties of excitations. The network analogue is applied to the calculation of the dynamical conductivity in the nearest-neighbour hopping regime for all Hubbard-interaction strength.
Random-resistor network description for hopping transport in the presence of Hubbard interaction
Bleibaum, O [Department of Physics and Materials Science Institute, University of Oregon, Eugene, OR 97403 (United States); Boettger, H [Institut fuer Theoretische Physik, Otto-von-Guericke, Universitaet Magdeburg, 399016 Magdeburg (Germany); Bryksin, V V [A F Ioffe Physico-Technical Institute, 194021 St Petersburg (Russian Federation)
2003-03-19
On the basis of the linearized rate equations for hopping electrons in the presence of Hubbard interaction we derive a random resistor network analogue of the transport equations. In contrast to the ordinary Miller-Abraham network our network has two nodes per site. The occurrence of the second node is related to the capability of the system to propagate excitations, and thus is characteristic for the interacting situation. Our random resistor network can be used for the investigation of the transport properties in alternating electric fields and for the investigation of properties of excitations. The network analogue is applied to the calculation of the dynamical conductivity in the nearest-neighbour hopping regime for all Hubbard-interaction strength.
A random walk evolution model of wireless sensor networks and virus spreading
Wang Ya-Qi; Yang Xiao-Yuan
2013-01-01
In this paper,considering both cluster heads and sensor nodes,we propose a novel evolving a network model based on a random walk to study the fault tolerance decrease of wireless sensor networks (WSNs) due to node failure,and discuss the spreading dynamic behavior of viruses in the evolution model.A theoretical analysis shows that the WSN generated by such an evolution model not only has a strong fault tolerance,but also can dynamically balance the energy loss of the entire network.It is also found that although the increase of the density of cluster heads in the network reduces the network efficiency,it can effectively inhibit the spread of viruses.In addition,the heterogeneity of the network improves the network efficiency and enhances the virus prevalence.We confirm all the theoretical results with sufficient numerical simulations.
Random Matrices, Combinatorics, Numerical Linear Algebra and Complex Networks
2012-02-16
Littlewood-Offord theorems and the condition number of random discrete matrices, Annals of Mathematics , to appear. [29] T. Tao and V. Vu, The condition...Wigner. On the distribution of the roots of certain symmetric matrices. Annals of Mathematics , 67(2):325327, 1958. Department of Mathematics, Yale, New Haven, CT 06520 E-mail address: van.vu@yale.edu
Du, Guanyao; Yu, Jianjun
2016-01-01
This paper investigates the system achievable rate for the multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system with an energy harvesting (EH) relay. Firstly we propose two protocols, time switching-based decode-and-forward relaying (TSDFR) and a flexible power splitting-based DF relaying (PSDFR) protocol by considering two practical receiver architectures, to enable the simultaneous information processing and energy harvesting at the relay. In PSDFR protocol, we introduce a temporal parameter to describe the time division pattern between the two phases which makes the protocol more flexible and general. In order to explore the system performance limit, we discuss the system achievable rate theoretically and formulate two optimization problems for the proposed protocols to maximize the system achievable rate. Since the problems are non-convex and difficult to solve, we first analyze them theoretically and get some explicit results, then design an augmented Lagrangian penalty function (ALPF) based algorithm for them. Numerical results are provided to validate the accuracy of our analytical results and the effectiveness of the proposed ALPF algorithm. It is shown that, PSDFR outperforms TSDFR to achieve higher achievable rate in such a MIMO-OFDM relaying system. Besides, we also investigate the impacts of the relay location, the number of antennas and the number of subcarriers on the system performance. Specifically, it is shown that, the relay position greatly affects the system performance of both protocols, and relatively worse achievable rate is achieved when the relay is placed in the middle of the source and the destination. This is different from the MIMO-OFDM DF relaying system without EH. Moreover, the optimal factor which indicates the time division pattern between the two phases in the PSDFR protocol is always above 0.8, which means that, the common division of the total transmission time into two equal phases in
Eigentime identities for random walks on a family of treelike networks and polymer networks
Dai, Meifeng; Wang, Xiaoqian; Sun, Yanqiu; Sun, Yu; Su, Weiyi
2017-10-01
In this paper, we investigate the eigentime identities quantifying as the sum of reciprocals of all nonzero normalized Laplacian eigenvalues on a family of treelike networks and the polymer networks. Firstly, for a family of treelike networks, it is shown that all their eigenvalues can be obtained by computing the roots of several small-degree polynomials defined recursively. We obtain the scalings of the eigentime identity on a family of treelike with network size Nn is Nn lnNn. Then, for the polymer networks, we apply the spectral decimation approach to determine analytically all the eigenvalues and their corresponding multiplicities. Using the relationship between the generation and the next generation of eigenvalues we obtain the scalings of the eigentime identity on the polymer networks with network size Nn is Nn lnNn. By comparing the eigentime identities on these two kinds of networks, their scalings with network size Nn are all Nn lnNn.
Network motif identification and structure detection with exponential random graph models
Munni Begum
2014-12-01
Full Text Available Local regulatory motifs are identified in the transcription regulatory network of the most studied model organism Escherichia coli (E. coli through graphical models. Network motifs are small structures in a network that appear more frequently than expected by chance alone. We apply social network methodologies such as p* models, also known as Exponential Random Graph Models (ERGMs, to identify statistically significant network motifs. In particular, we generate directed graphical models that can be applied to study interaction networks in a broad range of databases. The Markov Chain Monte Carlo (MCMC computational algorithms are implemented to obtain the estimates of model parameters to the corresponding network statistics. A variety of ERGMs are fitted to identify statistically significant network motifs in transcription regulatory networks of E. coli. A total of nine ERGMs are fitted to study the transcription factor - transcription factor interactions and eleven ERGMs are fitted for the transcription factor-operon interactions. For both of these interaction networks, arc (a directed edge in a directed network and k-istar (or incoming star structures, for values of k between 2 and 10, are found to be statistically significant local structures or network motifs. The goodness of fit statistics are provided to determine the quality of these models.
Stability analysis and stabilization of networked linear systems with random packet losses
XIE LiHua
2009-01-01
This paper Is concerned with the stability analysis and stabilization of networked discrete-time and sampled-data linear systems with random packet losses.Asymptotic stability,mean-square stability,and stochastic stability are considered.For networked discrete-time linear systems,the packet loss period is assumed to be a finite-state Markov chain.We establish that the mean-square stability of a related discrete-time system which evolves in random time Implies the mean-square stability of the system in deterministic time by using the equivalence of stability properties of Markovian jump linear systems in random time.We also establish the equivalence of asymptotic stability for the systems in deterministic discrete time and in random time.For networked sampled-data systems,a binary Markov chain Is used to characterize the packet loss phenomenon of the network.In this case,the packet loss period between two transmission instants is driven by an identically Independently distributed sequence assuming any positive values.Two approaches,namely the Markov jump linear system approach and randomly sampled system approach,are introduced.Based on the stability results derived,we present methods for stabilization of networked sampled-data systems in terms of matrix inequalities.Numerical examples are given to Illustrate the design methods of stabilizing controllers.
Enrique Gonzalez
2015-05-01
Full Text Available This survey aims to encourage the multidisciplinary communities to join forces for innovation in the mobile health monitoring area. Specifically, multidisciplinary innovations in medical emergency scenarios can have a significant impact on the effectiveness and quality of the procedures and practices in the delivery of medical care. Wireless body sensor networks (WBSNs are a promising technology capable of improving the existing practices in condition assessment and care delivery for a patient in a medical emergency. This technology can also facilitate the early interventions of a specialist physician during the pre-hospital period. WBSNs make possible these early interventions by establishing remote communication links with video/audio support and by providing medical information such as vital signs, electrocardiograms, etc. in real time. This survey focuses on relevant issues needed to understand how to setup a WBSN for medical emergencies. These issues are: monitoring vital signs and video transmission, energy efficient protocols, scheduling, optimization and energy consumption on a WBSN.
Random access to mobile networks with advanced error correction
Dippold, Michael
1990-01-01
A random access scheme for unreliable data channels is investigated in conjunction with an adaptive Hybrid-II Automatic Repeat Request (ARQ) scheme using Rate Compatible Punctured Codes (RCPC) Forward Error Correction (FEC). A simple scheme with fixed frame length and equal slot sizes is chosen and reservation is implicit by the first packet transmitted randomly in a free slot, similar to Reservation Aloha. This allows the further transmission of redundancy if the last decoding attempt failed. Results show that a high channel utilization and superior throughput can be achieved with this scheme that shows a quite low implementation complexity. For the example of an interleaved Rayleigh channel and soft decision utilization and mean delay are calculated. A utilization of 40 percent may be achieved for a frame with the number of slots being equal to half the station number under high traffic load. The effects of feedback channel errors and some countermeasures are discussed.
Fully-distributed randomized cooperation in wireless sensor networks
Bader, Ahmed
2015-01-07
When marrying randomized distributed space-time coding (RDSTC) to geographical routing, new performance horizons can be created. In order to reach those horizons however, routing protocols must evolve to operate in a fully distributed fashion. In this letter, we expose a technique to construct a fully distributed geographical routing scheme in conjunction with RDSTC. We then demonstrate the performance gains of this novel scheme by comparing it to one of the prominent classical schemes.
Martin Rosvall
Full Text Available To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation, which reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network--the optimal number of levels and modular partition at each level--with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines: life sciences, physical sciences, ecology and earth sciences, and social sciences. In general, we find shallow hierarchical structures in globally interconnected systems, such as neural networks, and rich multilevel organizations in systems with highly separated regions, such as road networks.
Kosmidis, Kosmas; Hütt, Marc-Thorsten
2015-01-01
Random walks are one of the best investigated dynamical processes on graphs. A particularly fascinating phenomenon is the scaling relationship of fluctuations $\\sigma $ with the average flux $\\langle f \\rangle $. Here we analyze how network topology and nodes with finite capacity lead to deviations from a simple scaling law $\\sigma \\sim \\langle f \\rangle ^\\alpha$. Sources of randomness are the random walk itself (internal noise) and the fluctuation of the number of walkers (external noise). We obtained exact results for the extreme case of a star network which are indicative of the behavior of large scale systems with a broad degree distribution.The latter are subsequently studied using Monte Carlo simulations. We find that the network heterogeneity amplifies the effects of external noise. By computing the `effective' scaling of each node we show that multiple scaling relationships can coexist in a graph with a heterogeneous degree distribution at an intermediate level of external noise. Finally, we analyze t...
Bouamrane, R
2003-01-01
An efficient algorithm, based on the Frank-Lobb reduction scheme, for calculating the equivalent dielectric properties of very large random resistor-capacitor (R-C) networks has been developed. It has been used to investigate the network size and composition dependence of dielectric properties and their statistical variability. The dielectric properties of 256 samples of random networks containing: 512, 2048, 8192 and 32 768 components distributed randomly in the ratios 60% R-40% C, 50% R-50% C and 40% R-60% C have been computed. It has been found that these properties exhibit the anomalous power law dependences on frequency known as the 'universal dielectric response' (UDR). Attention is drawn to the contrast between frequency ranges across which percolation determines dielectric response, where considerable variability is found amongst the samples, and those across which power laws define response where very little variability is found between samples. It is concluded that the power law UDRs are emergent pr...
Size effects and internal length scales in the elasticity of random fiber networks
Picu, Catalin; Berkache, Kamel; Shahsavari, Ali; Ganghoffer, Jean-Francois
Random fiber networks are the structural element of many biological and man-made materials, including connective tissue, various consumer products and packaging materials. In all cases of practical interest the scale at which the material is used and the scale of the fiber diameter or the mean segment length of the network are separated by several orders of magnitude. This precludes solving boundary value problems defined on the scale of the application while resolving every fiber in the system, and mandates the development of continuum equivalent models. To this end, we study the intrinsic geometric and mechanical length scales of the network and the size effect associated with them. We consider both Cauchy and micropolar continuum models and calibrate them based on the discrete network behavior. We develop a method to predict the characteristic length scales of the problem and the minimum size of a representative element of the network based on network structural parameters and on fiber properties.
Do scale-free regulatory networks allow more expression than random ones?
Fortuna, Miguel A; Melián, Carlos J
2007-07-21
In this paper, we compile the network of software packages with regulatory interactions (dependences and conflicts) from Debian GNU/Linux operating system and use it as an analogy for a gene regulatory network. Using a trace-back algorithm we assemble networks from the pool of packages with both scale-free (real data) and exponential (null model) topologies. We record the maximum number of packages that can be functionally installed in the system (i.e., the active network size). We show that scale-free regulatory networks allow a larger active network size than random ones. This result might have implications for the number of expressed genes at steady state. Small genomes with scale-free regulatory topologies could allow much more expression than large genomes with exponential topologies. This may have implications for the dynamics, robustness and evolution of genomes.
Complex Network Information Exchange in Random Wireless Environments
2012-06-30
the problem as an infinite horizon average cost Markov decision problem ( MDP ). Our MPNUM work thus developed theoretical methods to find optimal...scales than those used by the physical layer. The associated MDP is of the form lim N→∞ 1 N E [ N−1 ∑ t=0 ( ∑ m Um(r t m)− νTφ(Gt, rt)) ] , (19) where...number of transmissions of a packet). The FSM tracking the state of the overall network is the composition of the FSMs of the individual terminals. The
Random Access in Wireless Networks With Overlapping Cells
2010-06-01
Downloaded on May 19,2010 at 21:13:10 UTC from IEEE Xplore . Restrictions apply. Report Documentation Page Form ApprovedOMB No. 0704-0188 Public...Authorized licensed use limited to: NRL. Downloaded on May 19,2010 at 21:13:10 UTC from IEEE Xplore . Restrictions apply. NGUYEN et al.: RANDOM ACCESS IN...21:13:10 UTC from IEEE Xplore . Restrictions apply. 2890 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 56, NO. 6, JUNE 2010 5) For a single
Blake, R W; Ng, H; Chan, K H S; Li, J
2008-09-01
Recent developments in the design and propulsion of biomimetic autonomous underwater vehicles (AUVs) have focused on boxfish as models (e.g. Deng and Avadhanula 2005 Biomimetic micro underwater vehicle with oscillating fin propulsion: system design and force measurement Proc. 2005 IEEE Int. Conf. Robot. Auto. (Barcelona, Spain) pp 3312-7). Whilst such vehicles have many potential advantages in operating in complex environments (e.g. high manoeuvrability and stability), limited battery life and payload capacity are likely functional disadvantages. Boxfish employ undulatory median and paired fins during routine swimming which are characterized by high hydromechanical Froude efficiencies (approximately 0.9) at low forward speeds. Current boxfish-inspired vehicles are propelled by a low aspect ratio, 'plate-like' caudal fin (ostraciiform tail) which can be shown to operate at a relatively low maximum Froude efficiency (approximately 0.5) and is mainly employed as a rudder for steering and in rapid swimming bouts (e.g. escape responses). Given this and the fact that bioinspired engineering designs are not obligated to wholly duplicate a biological model, computer chips were developed using a multilayer perception neural network model of undulatory fin propulsion in the knifefish Xenomystus nigri that would potentially allow an AUV to achieve high optimum values of propulsive efficiency at any given forward velocity, giving a minimum energy drain on the battery. We envisage that externally monitored information on flow velocity (sensory system) would be conveyed to the chips residing in the vehicle's control unit, which in turn would signal the locomotor unit to adopt kinematics (e.g. fin frequency, amplitude) associated with optimal propulsion efficiency. Power savings could protract vehicle operational life and/or provide more power to other functions (e.g. communications).
Variability of Fiber Elastic Moduli in Composite Random Fiber Networks Makes the Network Softer
Ban, Ehsan; Picu, Catalin
2015-03-01
Athermal fiber networks are assemblies of beams or trusses. They have been used to model mechanics of fibrous materials such as biopolymer gels and synthetic nonwovens. Elasticity of these networks has been studied in terms of various microstructural parameters such as the stiffness of their constituent fibers. In this work we investigate the elasticity of composite fiber networks made from fibers with moduli sampled from a distribution function. We use finite elements simulations to study networks made by 3D Voronoi and Delaunay tessellations. The resulting data collapse to power laws showing that variability in fiber stiffness makes fiber networks softer. We also support the findings by analytical arguments. Finally, we apply these results to a network with curved fibers to explain the dependence of the network's modulus on the variation of its structural parameters.
Estimation of random fields from network observations. Technical report No. 26
Cabannes, A F
1979-06-01
When one has observed a random field Z at some points and recorded its values (network observations), a natural problem is to estimate Z at points where there are no observations. This dissertation deals first with this problem in an abstract setting, in m dimensions; later, it considers the estimation of a spatial two-dimensional random field. The problem then is one of constructing an estimated map of Z over a geographic area. For a given network of stations the quality of a map depends on the method of estimation. But for the given method of estimation the quality of a map depends on the choice of locations for the stations. This is the problem of network design. Both the study of methods of estimation and the problem of network design are addressed. 16 figures. (RWR)
Active Build-Model Random Forest Method for Network Traffic Classification
Alhamza Munther
2014-05-01
Full Text Available Network traffic classification continues to be an interesting subject among numerous networking communities. This method introduces multi-beneficial solutions in different avenues, such as network security, network management, anomaly detection, and quality-of-service. In this paper, we propose a supervised machine learning method that efficiently classifies different types of applications using the Active Build-Model Random Forest (ABRF method. This method constructs a new build model for the original Random Forest (RF method to decrease processing time. This build model includes only the active trees (i.e., trees with high accuracy, whereas the passive trees are excluded from the forest. The passive trees were excluded without any negative effect on classification accuracy. Results show that the ABRF method decreases the processing time by up to 37.5% compared with the original RF method. Our model has an overall accuracy of 98.66% based on the benchmark dataset considered in this paper.
Formation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs
Yasin Yazicioǧlu, A.
2015-11-25
Multi-Agent networks are often modeled as interaction graphs, where the nodes represent the agents and the edges denote some direct interactions. The robustness of a multi-Agent network to perturbations such as failures, noise, or malicious attacks largely depends on the corresponding graph. In many applications, networks are desired to have well-connected interaction graphs with relatively small number of links. One family of such graphs is the random regular graphs. In this paper, we present a decentralized scheme for transforming any connected interaction graph with a possibly non-integer average degree of k into a connected random m-regular graph for some m ϵ [k+k ] 2. Accordingly, the agents improve the robustness of the network while maintaining a similar number of links as the initial configuration by locally adding or removing some edges. © 2015 IEEE.
Chen, Chao; Yan, Xuefeng
2015-06-01
In this paper, an optimized multilayer feed-forward network (MLFN) is developed to construct a soft sensor for controlling naphtha dry point. To overcome the two main flaws in the structure and weight of MLFNs, which are trained by a back-propagation learning algorithm, minimal redundancy maximal relevance-partial mutual information clustering (mPMIc) integrated with least square regression (LSR) is proposed to optimize the MLFN. The mPMIc can determine the location of hidden layer nodes using information in the hidden and output layers, as well as remove redundant hidden layer nodes. These selected nodes are highly related to output data, but are minimally correlated with other hidden layer nodes. The weights between the selected hidden layer nodes and output layer are then updated through LSR. When the redundant nodes from the hidden layer are removed, the ideal MLFN structure can be obtained according to the test error results. In actual applications, the naphtha dry point must be controlled accurately because it strongly affects the production yield and the stability of subsequent operational processes. The mPMIc-LSR MLFN with a simple network size performs better than other improved MLFN variants and existing efficient models.
Decentralized formation of random regular graphs for robust multi-agent networks
Yazicioglu, A. Yasin
2014-12-15
Multi-agent networks are often modeled via interaction graphs, where the nodes represent the agents and the edges denote direct interactions between the corresponding agents. Interaction graphs have significant impact on the robustness of networked systems. One family of robust graphs is the random regular graphs. In this paper, we present a locally applicable reconfiguration scheme to build random regular graphs through self-organization. For any connected initial graph, the proposed scheme maintains connectivity and the average degree while minimizing the degree differences and randomizing the links. As such, if the average degree of the initial graph is an integer, then connected regular graphs are realized uniformly at random as time goes to infinity.
Coevolution of information processing and topology in hierarchical adaptive random Boolean networks
Górski, Piotr J.; Czaplicka, Agnieszka; Hołyst, Janusz A.
2016-02-01
Random Boolean Networks (RBNs) are frequently used for modeling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive random Boolean Network (HARBN) as a system consisting of distinct adaptive RBNs (ARBNs) - subnetworks - connected by a set of permanent interlinks. We investigate mean node information, mean edge information as well as mean node degree. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. The main natural feature of ARBNs, i.e. their adaptability, is preserved in HARBNs and they evolve towards critical configurations which is documented by power law distributions of network attractor lengths. The mean information processed by a single node or a single link increases with the number of interlinks added to the system. The mean length of network attractors and the mean steady-state connectivity possess minima for certain specific values of the quotient between the density of interlinks and the density of all links in networks. It means that the modular network displays extremal values of its observables when subnetworks are connected with a density a few times lower than a mean density of all links.
Control of random Boolean networks via average sensitivity of Boolean functions
Chen Shi-Jian; Hong Yi-Guang
2011-01-01
In this paper, we discuss how to transform the disordered phase into an ordered phase in random Boolean networks. To increase the effectiveness, a control scheme is proposed, which periodically freezes a fraction of the network based on the average sensitivity of Boolean functions of the nodes. Theoretical analysis is carried out to estimate the expected critical value of the fraction, and shows that the critical value is reduced using this scheme compared to that of randomly freezing a fraction of the nodes. Finally, the simulation is given for illustrating the effectiveness of the proposed method.
Outage Analysis of Opportunistic Cooperative Ad Hoc Networks with Randomly Located Nodes
Cheng-Wen Xing; Hai-Chuan Ding; Guang-Hua Yang; Shao-Dan Ma; Ze-Song Fei
2013-01-01
In this paper,an opportunistic cooperative ad hoc sensor network with randomly located nodes is analyzed.The randomness of nodes' locations is captured by a homogeneous Poisson point process.The effect of imperfect interference cancellation is also taken into account in the analysis.Based on the theory of stochastic geometry,outage probability and cooperative gain are derived.It is demonstrated that explicit performance gain can be achieved through cooperation.The analyses are corroborated by extensive simulation results and the analytical results can thus serve as a guideline for wireless sensor network design.
Risk Assessment of Distribution Network Based on Random set Theory and Sensitivity Analysis
Zhang, Sh; Bai, C. X.; Liang, J.; Jiao, L.; Hou, Z.; Liu, B. Zh
2017-05-01
Considering the complexity and uncertainty of operating information in distribution network, this paper introduces the use of random set for risk assessment. The proposed method is based on the operating conditions defined in the random set framework to obtain the upper and lower cumulative probability functions of risk indices. Moreover, the sensitivity of risk indices can effectually reflect information about system reliability and operating conditions, and by use of these information the bottlenecks that suppress system reliability can be found. The analysis about a typical radial distribution network shows that the proposed method is reasonable and effective.
Mean First Passage Time of Preferential Random Walks on Complex Networks with Applications
Zhongtuan Zheng
2017-01-01
Full Text Available This paper investigates, both theoretically and numerically, preferential random walks (PRW on weighted complex networks. By using two different analytical methods, two exact expressions are derived for the mean first passage time (MFPT between two nodes. On one hand, the MFPT is got explicitly in terms of the eigenvalues and eigenvectors of a matrix associated with the transition matrix of PRW. On the other hand, the center-product-degree (CPD is introduced as one measure of node strength and it plays a main role in determining the scaling of the MFPT for the PRW. Comparative studies are also performed on PRW and simple random walks (SRW. Numerical simulations of random walks on paradigmatic network models confirm analytical predictions and deepen discussions in different aspects. The work may provide a comprehensive approach for exploring random walks on complex networks, especially biased random walks, which may also help to better understand and tackle some practical problems such as search and routing on networks.
Tristable and multiple bistable activity in complex random binary networks of two-state units
Christ, Simon; Schimansky-Geier, Lutz
2016-01-01
We study complex networks of stochastic two-state units. Our aim is to model discrete stochastic excitable dynamics with a rest and an excited state. These two states are assumed to possess different waiting time distributions. The rest state is treated as an activation process with an exponentially distributed life time, whereas the latter in the excited state shall have a constant mean which may originate from any distribution. The activation rate of any single unit is determined by its neighbors according to a random complex network structure. In order to treat this problem in an analytical way, we use a heterogeneous mean-field approximation yielding a set of equations general valid for uncorrelated random networks. Based on this derivation we focus on random binary networks where the network is solely comprised of nodes with either of two degrees. The ratio between the two degrees is shown to be a crucial parameter. Dependent on the composition of the network the steady states show the usual transition f...
Dynamics of comb-of-comb-network polymers in random layered flows
Katyal, Divya; Kant, Rama
2016-12-01
We analyze the dynamics of comb-of-comb-network polymers in the presence of external random flows. The dynamics of such structures is evaluated through relevant physical quantities, viz., average square displacement (ASD) and the velocity autocorrelation function (VACF). We focus on comparing the dynamics of the comb-of-comb network with the linear polymer. The present work displays an anomalous diffusive behavior of this flexible network in the random layered flows. The effect of the polymer topology on the dynamics is analyzed by varying the number of generations and branch lengths in these networks. In addition, we investigate the influence of external flow on the dynamics by varying flow parameters, like the flow exponent α and flow strength Wα. Our analysis highlights two anomalous power-law regimes, viz., subdiffusive (intermediate-time polymer stretching and flow-induced diffusion) and superdiffusive (long-time flow-induced diffusion). The anomalous long-time dynamics is governed by the temporal exponent ν of ASD, viz., ν =2 -α /2 . Compared to a linear polymer, the comb-of-comb network shows a shorter crossover time (from the subdiffusive to superdiffusive regime) but a reduced magnitude of ASD. Our theory displays an anomalous VACF in the random layered flows that scales as t-α /2. We show that the network with greater total mass moves faster.
Wang, Rong; Wang, Li; Yang, Yong; Li, Jiajia; Wu, Ying; Lin, Pan
2016-11-01
Attention deficit hyperactivity disorder (ADHD) is the most common childhood neuropsychiatric disorder and affects approximately 6-7% of children worldwide. Here, we investigate the statistical properties of undirected and directed brain functional networks in ADHD patients based on random matrix theory (RMT), in which the undirected functional connectivity is constructed based on correlation coefficient and the directed functional connectivity is measured based on cross-correlation coefficient and mutual information. We first analyze the functional connectivity and the eigenvalues of the brain functional network. We find that ADHD patients have increased undirected functional connectivity, reflecting a higher degree of linear dependence between regions, and increased directed functional connectivity, indicating stronger causality and more transmission of information among brain regions. More importantly, we explore the randomness of the undirected and directed functional networks using RMT. We find that for ADHD patients, the undirected functional network is more orderly than that for normal subjects, which indicates an abnormal increase in undirected functional connectivity. In addition, we find that the directed functional networks are more random, which reveals greater disorder in causality and more chaotic information flow among brain regions in ADHD patients. Our results not only further confirm the efficacy of RMT in characterizing the intrinsic properties of brain functional networks but also provide insights into the possibilities RMT offers for improving clinical diagnoses and treatment evaluations for ADHD patients.
Wang, Rong; Wang, Li; Yang, Yong; Li, Jiajia; Wu, Ying; Lin, Pan
2016-11-01
Attention deficit hyperactivity disorder (ADHD) is the most common childhood neuropsychiatric disorder and affects approximately 6 -7 % of children worldwide. Here, we investigate the statistical properties of undirected and directed brain functional networks in ADHD patients based on random matrix theory (RMT), in which the undirected functional connectivity is constructed based on correlation coefficient and the directed functional connectivity is measured based on cross-correlation coefficient and mutual information. We first analyze the functional connectivity and the eigenvalues of the brain functional network. We find that ADHD patients have increased undirected functional connectivity, reflecting a higher degree of linear dependence between regions, and increased directed functional connectivity, indicating stronger causality and more transmission of information among brain regions. More importantly, we explore the randomness of the undirected and directed functional networks using RMT. We find that for ADHD patients, the undirected functional network is more orderly than that for normal subjects, which indicates an abnormal increase in undirected functional connectivity. In addition, we find that the directed functional networks are more random, which reveals greater disorder in causality and more chaotic information flow among brain regions in ADHD patients. Our results not only further confirm the efficacy of RMT in characterizing the intrinsic properties of brain functional networks but also provide insights into the possibilities RMT offers for improving clinical diagnoses and treatment evaluations for ADHD patients.
Dynamics of comb-of-comb-network polymers in random layered flows.
Katyal, Divya; Kant, Rama
2016-12-01
We analyze the dynamics of comb-of-comb-network polymers in the presence of external random flows. The dynamics of such structures is evaluated through relevant physical quantities, viz., average square displacement (ASD) and the velocity autocorrelation function (VACF). We focus on comparing the dynamics of the comb-of-comb network with the linear polymer. The present work displays an anomalous diffusive behavior of this flexible network in the random layered flows. The effect of the polymer topology on the dynamics is analyzed by varying the number of generations and branch lengths in these networks. In addition, we investigate the influence of external flow on the dynamics by varying flow parameters, like the flow exponent α and flow strength W_{α}. Our analysis highlights two anomalous power-law regimes, viz., subdiffusive (intermediate-time polymer stretching and flow-induced diffusion) and superdiffusive (long-time flow-induced diffusion). The anomalous long-time dynamics is governed by the temporal exponent ν of ASD, viz., ν=2-α/2. Compared to a linear polymer, the comb-of-comb network shows a shorter crossover time (from the subdiffusive to superdiffusive regime) but a reduced magnitude of ASD. Our theory displays an anomalous VACF in the random layered flows that scales as t^{-α/2}. We show that the network with greater total mass moves faster.
Stability of Dominating Sets in Complex Networks against Random and Targeted Attacks
Molnar, F.; Derzsy, N.; Szymanski, B. K.; Korniss, G.
2014-03-01
Minimum dominating sets (MDS) are involved in efficiently controlling and monitoring many social and technological networks. However, MDS influence over the entire network may be significantly reduced when some MDS nodes are disabled due to random breakdowns or targeted attacks against nodes in the network. We investigate the stability of domination in scale-free networks in such scenarios. We define stability as the fraction of nodes in the network that are still dominated after some nodes have been removed, either randomly, or by targeting the highest-degree nodes. We find that although the MDS is the most cost-efficient solution (requiring the least number of nodes) for reaching every node in an undamaged network, it is also very sensitive to damage. Further, we investigate alternative methods for finding dominating sets that are less efficient (more costly) than MDS but provide better stability. Finally we construct an algorithm based on greedy node selection that allows us to precisely control the balance between domination stability and cost, to achieve any desired stability at minimum cost, or the best possible stability at any given cost. Analysis of our method shows moderate improvement of domination cost efficiency against random breakdowns, but substantial improvements against targeted attacks. Supported by DARPA, DTRA, ARL NS-CTA, ARO, and ONR.
Bayesian network meta-analysis for cluster randomized trials with binary outcomes.
Uhlmann, Lorenz; Jensen, Katrin; Kieser, Meinhard
2017-06-01
Network meta-analysis is becoming a common approach to combine direct and indirect comparisons of several treatment arms. In recent research, there have been various developments and extensions of the standard methodology. Simultaneously, cluster randomized trials are experiencing an increased popularity, especially in the field of health services research, where, for example, medical practices are the units of randomization but the outcome is measured at the patient level. Combination of the results of cluster randomized trials is challenging. In this tutorial, we examine and compare different approaches for the incorporation of cluster randomized trials in a (network) meta-analysis. Furthermore, we provide practical insight on the implementation of the models. In simulation studies, it is shown that some of the examined approaches lead to unsatisfying results. However, there are alternatives which are suitable to combine cluster randomized trials in a network meta-analysis as they are unbiased and reach accurate coverage rates. In conclusion, the methodology can be extended in such a way that an adequate inclusion of the results obtained in cluster randomized trials becomes feasible. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Gibson, Scott M; Ficklin, Stephen P; Isaacson, Sven; Luo, Feng; Feltus, Frank A; Smith, Melissa C
2013-01-01
The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.
Scott M Gibson
Full Text Available The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT, is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens, rice (Oryza sativa and budding yeast (Saccharomyces cerevisiae. We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.
Ban, Ehsan; Barocas, Victor H; Shephard, Mark S; Picu, Catalin R
2016-04-01
Fiber networks are assemblies of one-dimensional elements representative of materials with fibrous microstructures such as collagen networks and synthetic nonwovens. The mechanics of random fiber networks has been the focus of numerous studies. However, fiber crimp has been explicitly represented only in few cases. In the present work, the mechanics of cross-linked networks with crimped athermal fibers, with and without an embedding elastic matrix, is studied. The dependence of the effective network stiffness on the fraction of nonstraight fibers and the relative crimp amplitude (or tortuosity) is studied using finite element simulations of networks with sinusoidally curved fibers. A semi-analytic model is developed to predict the dependence of network modulus on the crimp amplitude and the bounds of the stiffness reduction associated with the presence of crimp. The transition from the linear to the nonlinear elastic response of the network is rendered more gradual by the presence of crimp, and the effect of crimp on the network tangent stiffness decreases as strain increases. If the network is embedded in an elastic matrix, the effect of crimp becomes negligible even for very small, biologically relevant matrix stiffness values. However, the distribution of the maximum principal stress in the matrix becomes broader in the presence of crimp relative to the similar system with straight fibers, which indicates an increased probability of matrix failure.
Berdahl, Andrew; Shreim, Amer; Sood, Vishal; Davidsen, Joern; Paczuski, Maya [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4 (Canada)], E-mail: aberdahl@phas.ucalgary.ca
2008-06-15
We discuss basic features of emergent complexity in dynamical systems far from equilibrium by focusing on the network structure of their state space. We start by measuring the distributions of avalanche and transient times in random Boolean networks (RBNs) and in the Drosophila polarity network by exact enumeration. A transient time is the duration of the transient from a starting state to an attractor. An avalanche is a special transient which starts as a single Boolean element perturbation of an attractor state. Significant differences at short times between the avalanche and the transient times for RBNs with small connectivity K-compared to the number of elements N-indicate that attractors tend to cluster in configuration space. In addition, one bit flip has a non-negligible chance to put an attractor state directly onto another attractor. This clustering is also present in the segment polarity gene network of Drosophila melanogaster, suggesting that this may be a robust feature of biological regulatory networks. We also define and measure a branching ratio for the state space networks and find evidence for a new timescale that diverges roughly linearly with N for 2{<=}K<
Coevolution of Information Processing and Topology in Hierarchical Adaptive Random Boolean Networks
Gorski, Piotr J; Holyst, Janusz A
2015-01-01
Random Boolean networks (RBNs) are frequently employed for modelling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive RBN (HARBN) as a system consisting of distinct adaptive RBNs - subnetworks - connected by a set of permanent interlinks. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. We investigate mean node information, mean edge information as well as a mean node degree as functions of model parameters and demonstrate HARBN's ability to describe complex hierarchical systems.
Gao Haichun
2007-08-01
Full Text Available Abstract Background Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT, which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. Results Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under
Han, Zhao Jun; Levchenko, Igor; Yick, Samuel; Ostrikov, Kostya Ken
2011-11-01
Tailoring the density of random single-walled carbon nanotube (SWCNT) networks is of paramount importance for various applications, yet it remains a major challenge due to the insufficient catalyst activation in most growth processes. Here we report on a simple and effective method to maximise the number of active catalyst nanoparticles using catalytic chemical vapor deposition (CCVD). By modulating short pulses of acetylene into a methane-based CCVD growth process, the density of SWCNTs is dramatically increased by up to three orders of magnitude without increasing the catalyst density and degrading the nanotube quality. In the framework of a vapor-liquid-solid model, we attribute the enhanced growth to the high dissociation rate of acetylene at high temperatures at the nucleation stage, which can be effective in both supersaturating the larger catalyst nanoparticles and overcoming the nanotube nucleation energy barrier of the smaller catalyst nanoparticles. These results are highly relevant to numerous applications of random SWCNT networks in next-generation energy, sensing and biomedical devices.
Deciphering the global organization of clustering in real complex networks
Colomer-de-Simon, Pol; Beiro, Mariano G; Alvarez-Hamelin, J Ignacio; Boguna, Marian
2013-01-01
We uncover the global organization of clustering in real complex networks. As it happens with other fundamental properties of networks such as the degree distribution, we find that real networks are neither completely random nor ordered with respect to clustering, although they tend to be closer to maximally random architectures. We reach this conclusion by comparing the global structure of clustering in real networks with that in maximally random and in maximally ordered clustered graphs. The former are produced with an exponential random graph model that maintains correlations among adjacent edges at the minimum needed to conform with the expected clustering spectrum; the later with a random model that arranges triangles in cliques inducing highly ordered structures. To compare the global organization of clustering in real and model networks, we compute $m$-core landscapes, where the $m$-core is defined, akin to the $k$-core, as the maximal subgraph with edges participating at least in $m$ triangles. This p...
Ma, Jun; Xu, Ying; Wang, Chunni; Jin, Wuyin
2016-11-01
Regular spatial patterns could be observed in spatiotemporal systems far from equilibrium states. Artificial networks with different topologies are often designed to reproduce the collective behaviors of nodes (or neurons) which the local kinetics of node is described by kinds of oscillator models. It is believed that the self-organization of network much depends on the bifurcation parameters and topology connection type. Indeed, the boundary effect is every important on the pattern formation of network. In this paper, a regular network of Hindmarsh-Rose neurons is designed in a two-dimensional square array with nearest-neighbor connection type. The neurons on the boundary are excited with random stimulus. It is found that spiral waves, even a pair of spiral waves could be developed in the network under appropriate coupling intensity. Otherwise, the spatial distribution of network shows irregular states. A statistical variable is defined to detect the collective behavior by using mean field theory. It is confirmed that regular pattern could be developed when the synchronization degree is low. The potential mechanism could be that random perturbation on the boundary could induce coherence resonance-like behavior thus spiral wave could be developed in the network.
The phase diagram of random Boolean networks with nested canalizing functions
Peixoto, Tiago P
2010-01-01
We obtain the phase diagram of random Boolean networks with nested canalizing functions. Using the annealed approximation, we obtain the evolution of the number $b_t$ of nodes with value one, and the network sensitivity $\\lambda$, and we compare with numerical simulations of quenched networks. We find that, contrary to what was reported by Kauffman et al. [Proc. Natl. Acad. Sci. 2004 101 49 17102-7], these networks have a rich phase diagram, were both the "chaotic" and frozen phases are present, as well as an oscillatory regime of the value of $b_t$. We argue that the presence of only the frozen phase in the work of Kauffman et al. was due simply to the specific parametrization used, and is not an inherent feature of this class of functions. However, these networks are significantly more stable than the variants where all possible Boolean functions are allowed.
Hundebøll, Martin; Pedersen, Morten Videbæk; Roetter, Daniel Enrique Lucani
2014-01-01
This work studies the potential and impact of the FRANC network coding protocol for delivering high quality Dynamic Adaptive Streaming over HTTP (DASH) in wireless networks. Although DASH aims to tailor the video quality rate based on the available throughput to the destination, it relies...
2012-02-28
IEEE Transactions on Automatic Control (to appear). • A. Chapman and M. Mesbahi, Influence models for consensus-type networks, IEEE Transactions on Automatic Control (to...analysis and synthesis of relative sensing networks, IEEE Transactions on Automatic control , 56 (5): 971-982, 2011. • D. Zelazo and M. Mesbahi, Edge...agreement: graph-theoretic performance bounds and passivity anal- ysis, IEEE
Brandes, U; Gaertler, M; Goerke, R; Hoefer, M; Nikoloski, Z; Wagner, D
2006-01-01
Several algorithms have been proposed to compute partitions of networks into communities that score high on a graph clustering index called modularity. While publications on these algorithms typically contain experimental evaluations to emphasize the plausibility of results, none of these algorithms has been shown to actually compute optimal partitions. We here settle the unknown complexity status of modularity maximization by showing that the corresponding decision version is NP-complete in the strong sense. As a consequence, any efficient, i.e. polynomial-time, algorithm is only heuristic and yields suboptimal partitions on many instances.
Microscopic Evaluation of Electrical and Thermal Conduction in Random Metal Wire Networks.
Gupta, Ritu; Kumar, Ankush; Sadasivam, Sridhar; Walia, Sunil; Kulkarni, Giridhar U; Fisher, Timothy S; Marconnet, Amy
2017-04-05
Ideally, transparent heaters exhibit uniform temperature, fast response time, high achievable temperatures, low operating voltage, stability across a range of temperatures, and high optical transmittance. For metal network heaters, unlike for uniform thin-film heaters, all of these parameters are directly or indirectly related to the network geometry. In the past, at equilibrium, the temperature distributions within metal networks have primarily been studied using either a physical temperature probe or direct infrared (IR) thermography, but there are limits to the spatial resolution of these cameras and probes, and thus, only average regional temperatures have typically been measured. However, knowledge of local temperatures within the network with a very high spatial resolution is required for ensuring a safe and stable operation. Here, we examine the thermal properties of random metal network thin-film heaters fabricated from crack templates using high-resolution IR microscopy. Importantly, the heaters achieve predominantly uniform temperatures throughout the substrate despite the random crack network structure (e.g., unequal sized polygons created by metal wires), but the temperatures of the wires in the network are observed to be significantly higher than the substrate because of the significant thermal contact resistance at the interface between the metal and the substrate. Last, the electrical breakdown mechanisms within the network are examined through transient IR imaging. In addition to experimental measurements of temperatures, an analytical model of the thermal properties of the network is developed in terms of geometrical parameters and material properties, providing insights into key design rules for such transparent heaters. Beyond this work, the methods and the understanding developed here extend to other network-based heaters and conducting films, including those that are not transparent.
Huang, Rimao; Qiu, Xuesong; Rui, Lanlan
2011-01-01
Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate.
Scaling laws for file dissemination in P2P networks with random contacts
Núñez-Queija, R.; Prabhu, B.
2008-01-01
In this paper we obtain the scaling law for the mean broadcast time of a file in a P2P network with an initial population of N nodes. In the model, at Poisson rate lambda a node initiates a contact with another node chosen uniformly at random. This contact is said to be successful if the contacted n
Relay-aided multi-cell broadcasting with random network coding
Lu, Lu; Sun, Fan; Xiao, Ming
2010-01-01
We investigate a relay-aided multi-cell broadcasting system using random network codes, where the focus is on devising efficient scheduling algorithms between relay and base stations. Two scheduling algorithms are proposed based on different feedback strategies; namely, a one-step scheduling...
Scaling laws for file dissemination in P2P networks with random contacts
Núñez-Queija, R.; Prabhu, B.
2008-01-01
In this paper we obtain the scaling law for the mean broadcast time of a file in a P2P network with an initial population of N nodes. In the model, at Poisson rate lambda a node initiates a contact with another node chosen uniformly at random. This contact is said to be successful if the contacted n
Jogendra Kushwah
2013-06-01
Full Text Available The free radical gene classification of cancer diseases is challenging job in biomedical data engineering. The improving of classification of gene selection of cancer diseases various classifier are used, but the classification of classifier are not validate. So ensemble classifier is used for cancer gene classification using neural network classifier with random forest tree. The random forest tree is ensembling technique of classifier in this technique the number of classifier ensemble of their leaf node of class of classifier. In this paper we combined neural network with random forest ensemble classifier for classification of cancer gene selection for diagnose analysis of cancer diseases. The proposed method is different from most of the methods of ensemble classifier, which follow an input output paradigm of neural network, where the members of the ensemble are selected from a set of neural network classifier. the number of classifiers is determined during the rising procedure of the forest. Furthermore, the proposed method produces an ensemble not only correct, but also assorted, ensuring the two important properties that should characterize an ensemble classifier. For empirical evaluation of our proposed method we used UCI cancer diseases data set for classification. Our experimental result shows that better result in compression of random forest tree classification.
Best Spatiotemporal Performance Sustained by Optimal Number of Random Shortcuts on Complex Networks
WANG Mao-Sheng; HOU Zhong-Huai; XIN Hou-Wen
2006-01-01
@@ The spatial synchronization and temporal coherence of FitzHugh-Nagumo (FHN) neurons on complex networks are numerically investigated. When an optimal number of random shortcuts are added to a regular neural chain,the system can reach a state which is nearly periodic in time and almost synchronized in space.
Scaling laws for file dissemination in P2P networks with random contacts
Nunez-Queija, R.; Prabhu, B.
2008-01-01
In this paper we obtain the scaling law for the mean broadcast time of a file in a P2P network with an initial population of N nodes. In the model, at Poisson rate λ a node initiates a contact with another node chosen uniformly at random. This contact is said to be successful if the contacted node p
High Performance Ambipolar Field-Effect Transistor of Random Network Carbon Nanotubes
Bisri, Satria Zulkarnaen; Gao, Jia; Derenskyi, Vladimir; Gomulya, Widianta; Iezhokin, Igor; Gordiichuk, Pavlo; Herrmann, Andreas; Loi, Maria Antonietta
2012-01-01
Ambipolar field-effect transistors of random network carbon nanotubes are fabricated from an enriched dispersion utilizing a conjugated polymer as the selective purifying medium. The devices exhibit high mobility values for both holes and electrons (3 cm(2)/V.s) with a high on/off ratio (10(6)). The
Scaling laws for file dissemination in P2P networks with random contacts
Núñez-Queija, R.; Prabhu, B.
2008-01-01
In this paper we obtain the scaling law for the mean broadcast time of a file in a P2P network with an initial population of N nodes. In the model, at Poisson rate lambda a node initiates a contact with another node chosen uniformly at random. This contact is said to be successful if the contacted
Quasi-Random Resistor Network Model for Linear Magnetoresistance of Metal-Semiconductor Composite
XU Jie; ZHANG Duan-Ming; DENG Zong-Wei; YANG Feng-Xia; LI Zhi-Hua; PAN Yuan
2008-01-01
A new model for the linear magentoresistance (MR) of the Ag2+δ Se and Ag2+δ Te thin films is proposed. The thin film is considered as a metal-semiconductor composite and dispersed into an N×N quasi-random resistor network. The network is constructed from four-terminal resistors and the mobility of carries μ within the network has a quasi-random distribution, i.e. a Gaussian distribution with two constraint conditions. The model predicts that the MR increases with the increasing magnetic fields, and increases linearly at high field. Moreover, the MR decreases with the increasing temperatures. A good agreement between the theoretical MR and the available experimental data is found.
Ahmed, Faraz; Liu, Alex X
2013-01-01
Online social networks are being increasingly used for analyzing various societal phenomena such as epidemiology, information dissemination, marketing and sentiment flow. Popular analysis techniques such as clustering and influential node analysis, require the computation of eigenvectors of the real graph's adjacency matrix. Recent de-anonymization attacks on Netflix and AOL datasets show that an open access to such graphs pose privacy threats. Among the various privacy preserving models, Differential privacy provides the strongest privacy guarantees. In this paper we propose a privacy preserving mechanism for publishing social network graph data, which satisfies differential privacy guarantees by utilizing a combination of theory of random matrix and that of differential privacy. The key idea is to project each row of an adjacency matrix to a low dimensional space using the random projection approach and then perturb the projected matrix with random noise. We show that as compared to existing approaches for ...
Self-avoiding walks on random networks of resistors and diodes
Marković, D.; Milošević, S.; Stanley, H. E.
1987-07-01
We study the self-avoiding walks (SAW) on a square lattice whose various degrees of randomness encompasses many different random networks, including the incipient clusters of the directed, mixed and isotropic bond percolation. We apply the position-space renormalization group (PSRG) method and demonstrate that within the framework of this method one is bound to find that the critical exponent v of the mean end-to-end distance of SAW on various two-dimensional random networks should be equal to the critical exponent of SAW on the ordinary square lattice. A detailed analysis of this finding, and similar findings of other authors, lead us to conclude that a debatable opposite finding, which has been predicted on the basis of different approaches, could be attained after a substantial refinement of the method applied.
Shi, Cindy
2015-07-17
The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.
Zak, Michail
2008-01-01
A report discusses an algorithm for a new kind of dynamics based on a quantum- classical hybrid-quantum-inspired maximizer. The model is represented by a modified Madelung equation in which the quantum potential is replaced by different, specially chosen 'computational' potential. As a result, the dynamics attains both quantum and classical properties: it preserves superposition and entanglement of random solutions, while allowing one to measure its state variables, using classical methods. Such optimal combination of characteristics is a perfect match for quantum-inspired computing. As an application, an algorithm for global maximum of an arbitrary integrable function is proposed. The idea of the proposed algorithm is very simple: based upon the Quantum-inspired Maximizer (QIM), introduce a positive function to be maximized as the probability density to which the solution is attracted. Then the larger value of this function will have the higher probability to appear. Special attention is paid to simulation of integer programming and NP-complete problems. It is demonstrated that the problem of global maximum of an integrable function can be found in polynomial time by using the proposed quantum- classical hybrid. The result is extended to a constrained maximum with applications to integer programming and TSP (Traveling Salesman Problem).
Engineering Online and In-Person Social Networks for Physical Activity: A Randomized Trial.
Rovniak, Liza S; Kong, Lan; Hovell, Melbourne F; Ding, Ding; Sallis, James F; Ray, Chester A; Kraschnewski, Jennifer L; Matthews, Stephen A; Kiser, Elizabeth; Chinchilli, Vernon M; George, Daniel R; Sciamanna, Christopher N
2016-12-01
Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. The purpose of this study was to conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively measured outcomes. Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3 % male, 83.4 % overweight/obese) were randomized to one of three groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking as well as prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Participants increased their MVPA by 21.0 min/week, 95 % CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. The trial was registered with the ClinicalTrials.gov (NCT01142804).
Chaos Control in Random Boolean Networks by Reducing Mean Damage Percolation Rate
JIANG Nan; CHEN Shi-Jian
2011-01-01
Chaos control in random Boolean networks is implemented by freezing part of the network to drive it from chaotic to ordered phase. However, controlled nodes are only viewed as passive blocks to prevent perturbation spread. We propose a new control method in which controlled nodes can exert an active impact on the network.Controlled nodes and frozen values are deliberately selected according to the information of connection and Boolean functions. Simulation results showy that the number of nodes needed to achieve control is largely reduced compared to the previous method. Theoretical analysis is also given to estimate the least fraction of nodes needed to achieve control.%Chaos control in random Boolean networks is implemented by freezing part of the network to drive it from chaotic to ordered phase.However, controlled nodes are only viewed as passive blocks to prevent perturbation spread.We propose a new control method in which controlled nodes can exert an active impact on the network.Controlled nodes and frozen values are deliberately selected according to the information of connection and Boolean functions.Simulation results show that the number of nodes needed to achieve control is largely reduced compared to the previous method.Theoretical analysis is also given to estimate the least fraction of nodes needed to achieve control
GENERAL: Mean-field Theory for Some Bus Transport Networks with Random Overlapping Clique Structure
Yang, Xu-Hua; Sun, Bao; Wang, Bo; Sun, You-Xian
2010-04-01
Transport networks, such as railway networks and airport networks, are a kind of random network with complex topology. Recently, more and more scholars paid attention to various kinds of transport networks and try to explore their inherent characteristics. Here we study the exponential properties of a recently introduced Bus Transport Networks (BTNs) evolution model with random overlapping clique structure, which gives a possible explanation for the observed exponential distribution of the connectivities of some BTNs of three major cities in China. Applying mean-field theory, we analyze the BTNs model and prove that this model has the character of exponential distribution of the connectivities, and develop a method to predict the growth dynamics of the individual vertices, and use this to calculate analytically the connectivity distribution and the exponents. By comparing mean-field based theoretic results with the statistical data of real BTNs, we observe that, as a whole, both of their data show similar character of exponential distribution of the connectivities, and their exponents have same order of magnitude, which show the availability of the analytical result of this paper.
Random and Directed Walk-Based Top- Queries in Wireless Sensor Networks
Jun-Song Fu
2015-05-01
Full Text Available In wireless sensor networks, filter-based top- query approaches are the state-of-the-art solutions and have been extensively researched in the literature, however, they are very sensitive to the network parameters, including the size of the network, dynamics of the sensors’ readings and declines in the overall range of all the readings. In this work, a random walk-based top- query approach called RWTQ and a directed walk-based top- query approach called DWTQ are proposed. At the beginning of a top- query, one or several tokens are sent to the specific node(s in the network by the base station. Then, each token walks in the network independently to record and process the readings in a random or directed way. A strategy of choosing the “right” way in DWTQ is carefully designed for the token(s to arrive at the high-value regions as soon as possible. When designing the walking strategy for DWTQ, the spatial correlations of the readings are also considered. Theoretical analysis and simulation results indicate that RWTQ and DWTQ both are very robust against these parameters discussed previously. In addition, DWTQ outperforms TAG, FILA and EXTOK in transmission cost, energy consumption and network lifetime.
Potential Maximizers and Network Formation
Slikker, M.; Dutta, P.K.; van den Nouweland, C.G.A.M.; Tijs, S.H.
1998-01-01
In this paper we study the formation of cooperation structures in superadditive cooperative TU-games.Cooperation structures are represented by hypergraphs.The formation process is modelled as a game in strategic form, where the payoffs are determined according to a weighted (extended) Myerson value.
H∞ Networked Cascade Control System Design for Turboshaft Engines with Random Packet Dropouts
Xiaofeng Liu
2017-01-01
Full Text Available The distributed control architecture becomes more and more important in future gas turbine engine control systems, in which the sensors and actuators will be connected to the controllers via a network. Therefore, the control problem of network-enabled high-performance distributed engine control (DEC has come to play an important role in modern gas turbine control systems, while, due to the properties of the network, the packet dropouts must be considered. This study introduces a distributed control system architecture based on a networked cascade control system (NCCS. Typical turboshaft engine distributed controllers are designed based on the NCCS framework with H∞ state feedback under random packet dropouts. The sufficient robust stable conditions are derived via the Lyapunov stability theory and linear matrix inequality approach. Simulations illustrate the effectiveness of the presented method.
Optimal paths planning in dynamic transportation networks with random link travel times
孙世超; 段征宇; 杨东援
2014-01-01
A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent (STD). The methodology of relative robust optimization was applied as measures for comparing time-varying, random path travel times for a priori optimization. In accordance with the situation in real world, a stochastic consistent condition was provided for the STD networks and under this condition, a mathematical proof was given that the STD robust optimal path problem can be simplified into a minimum problem in specific time-dependent networks. A label setting algorithm was designed and tested to find travelers’ robust optimal path in a sampled STD network with computation complexity of O(n2+n·m). The validity of the robust approach and the designed algorithm were confirmed in the computational tests. Compared with conventional probability approach, the proposed approach is simple and efficient, and also has a good application prospect in navigation system.
A minimal model for the emergence of cooperation in randomly growing networks
Miller, Steve
2016-01-01
Cooperation is observed widely in nature and is thought an essential component of many evolutionary processes, yet the mechanisms by which it arises and persists are still unclear. Among several theories, network reciprocity -- a model of inhomogeneous social interactions -- has been proposed as an enabling mechanism to explain the emergence of cooperation. Existing evolutionary models of this mechanism have tended to focus on highly heterogeneous (scale-free) networks, hence typically assume preferential attachment mechanisms, and consequently the prerequisite that individuals have global network knowledge. Within an evolutionary game theoretic context, using the weak prisoner's dilemma as a metaphor for cooperation, we present a minimal model which describes network growth by chronological random addition of new nodes, combined with regular attrition of less fit members of the population. Specifically our model does not require that agents have access to global information and does not assume scale-free net...
Fitzek, Frank; Toth, Tamas; Szabados, Áron
2014-01-01
This paper advocates the use of random linear network coding for storage in distributed clouds in order to reduce storage and traffic costs in dynamic settings, i.e. when adding and removing numerous storage devices/clouds on-the-fly and when the number of reachable clouds is limited. We introduce...... various network coding approaches that trade-off reliability, storage and traffic costs, and system complexity relying on probabilistic recoding for cloud regeneration. We compare these approaches with other approaches based on data replication and Reed-Solomon codes. A simulator has been developed...... to carry out a thorough performance evaluation of the various approaches when relying on different system settings, e.g., finite fields, and network/storage conditions, e.g., storage space used per cloud, limited network use, and limited recoding capabilities. In contrast to standard coding approaches, our...
Degree-correlation, omniscience, and randomized immunization protocols in finite networks
Alm, Jeremy F
2016-01-01
Many naturally occurring networks have a power-law degree distribution as well as a non-zero degree correlation. Despite this, most studies analyzing the efficiency of immunization strategies in networks have concentrated only on power-law degree distribution and ignored degree correlation. This study looks specifically at the effect degree-correlation has on the efficiency of several immunization strategies in scale-free networks. Generally, we found that positive degree correlation raises the number of immunized individuals needed to stop the spread of the infection. Importantly, we found that in networks with positive degree correlation, immunization strategies that utilize knowledge of initial popularity actually perform worse on average than random immunization strategies.
Multiple random walks on complex networks: A harmonic law predicts search time
Weng, Tongfeng; Zhang, Jie; Small, Michael; Hui, Pan
2017-05-01
We investigate multiple random walks traversing independently and concurrently on complex networks and introduce the concept of mean first parallel passage time (MFPPT) to quantify their search efficiency. The mean first parallel passage time represents the expected time required to find a given target by one or some of the multiple walkers. We develop a general theory that allows us to calculate the MFPPT analytically. Interestingly, we find that the global MFPPT follows a harmonic law with respect to the global mean first passage times of the associated walkers. Remarkably, when the properties of multiple walkers are identical, the global MFPPT decays in a power law manner with an exponent of unity, irrespective of network structure. These findings are confirmed by numerical and theoretical results on various synthetic and real networks. The harmonic law reveals a universal principle governing multiple random walks on networks that uncovers the contribution and role of the combined walkers in a target search. Our paradigm is also applicable to a broad range of random search processes.
Analysis of Fracturing Network Evolution Behaviors in Random Naturally Fractured Rock Blocks
Wang, Y.; Li, X.; Zhang, B.
2016-11-01
Shale gas has been discovered in the Upper Triassic Yanchang Formation, Ordos Basin, China. Due to the weak tectonic activities in the shale plays, core observations indicate abundant random non-tectonic micro-fractures in the producing shales. The role of micro-fractures in hydraulic fracturing for shale gas development is currently poorly understood yet potentially critical. In a series of scaled true triaxial laboratory experiments, we investigate the interaction of propagating fracturing network with natural fractures. The influence of dominating factors was studied and analyzed, with an emphasis on non-tectonic fracture density, injection rate, and stress ratio. A new index of P-SRV is proposed to evaluate the fracturing effectiveness. From the test results, three types of fracturing network geometry of radial random net-fractures, partly vertical fracture with random branches, and vertical main fracture with multiple branches were observed. It is suggested from qualitative and quantitative analysis that great micro-fracture density and injection rate tend to maximum the fracturing network; however, it tends to decrease the fracturing network with the increase in horizontal stress ratio. The function fitting results further proved that the injection rate has the most obvious influence on fracturing effectiveness.
Variance-Constrained State Estimation for Complex Networks With Randomly Varying Topologies.
Dong, Hongli; Hou, Nan; Wang, Zidong; Ren, Weijian
2017-05-23
This paper investigates the variance-constrained H∞ state estimation problem for a class of nonlinear time-varying complex networks with randomly varying topologies, stochastic inner coupling, and measurement quantization. A Kronecker delta function and Markovian jumping parameters are utilized to describe the random changes of network topologies. A Gaussian random variable is introduced to model the stochastic disturbances in the inner coupling of complex networks. As a kind of incomplete measurements, measurement quantization is taken into consideration so as to account for the signal distortion phenomenon in the transmission process. Stochastic nonlinearities with known statistical characteristics are utilized to describe the stochastic evolution of the complex networks. We aim to design a finite-horizon estimator, such that in the simultaneous presence of quantized measurements and stochastic inner coupling, the prescribed variance constraints on the estimation error and the desired H∞ performance requirements are guaranteed over a finite horizon. Sufficient conditions are established by means of a series of recursive linear matrix inequalities, and subsequently, the estimator gain parameters are derived. A simulation example is presented to illustrate the effectiveness and applicability of the proposed estimator design algorithm.
Jogendra Kushwah
2013-06-01
Full Text Available The free radical gene classification of cancerdiseasesis challenging job in biomedical dataengineering. The improving of classification of geneselection of cancer diseases various classifier areused, but the classification of classifier are notvalidate. So ensemble classifier is used for cancergene classification using neural network classifierwith random forest tree. The random forest tree isensembling technique of classifier in this techniquethe number of classifier ensemble of their leaf nodeof class of classifier. In this paper we combinedneuralnetwork with random forest ensembleclassifier for classification of cancer gene selectionfor diagnose analysis of cancer diseases.Theproposed method is different from most of themethods of ensemble classifier, which follow aninput output paradigm ofneural network, where themembers of the ensemble are selected from a set ofneural network classifier. the number of classifiersis determined during the rising procedure of theforest. Furthermore, the proposed method producesan ensemble not only correct, but also assorted,ensuring the two important properties that shouldcharacterize an ensemble classifier. For empiricalevaluation of our proposed method we used UCIcancer diseases data set for classification. Ourexperimental result shows that betterresult incompression of random forest tree classification
David A Rolls
Full Text Available We compare two broad types of empirically grounded random network models in terms of their abilities to capture both network features and simulated Susceptible-Infected-Recovered (SIR epidemic dynamics. The types of network models are exponential random graph models (ERGMs and extensions of the configuration model. We use three kinds of empirical contact networks, chosen to provide both variety and realistic patterns of human contact: a highly clustered network, a bipartite network and a snowball sampled network of a "hidden population". In the case of the snowball sampled network we present a novel method for fitting an edge-triangle model. In our results, ERGMs consistently capture clustering as well or better than configuration-type models, but the latter models better capture the node degree distribution. Despite the additional computational requirements to fit ERGMs to empirical networks, the use of ERGMs provides only a slight improvement in the ability of the models to recreate epidemic features of the empirical network in simulated SIR epidemics. Generally, SIR epidemic results from using configuration-type models fall between those from a random network model (i.e., an Erdős-Rényi model and an ERGM. The addition of subgraphs of size four to edge-triangle type models does improve agreement with the empirical network for smaller densities in clustered networks. Additional subgraphs do not make a noticeable difference in our example, although we would expect the ability to model cliques to be helpful for contact networks exhibiting household structure.
Thierry Géraud
2004-12-01
Full Text Available We present a fast method for road network extraction in satellite images. It can be seen as a transposition of the segmentation scheme Ã¢Â€Âœwatershed transform + region adjacency graph + Markov random fieldsÃ¢Â€Â to the extraction of curvilinear objects. Many road extractors which are composed of two stages can be found in the literature. The first one acts like a filter that can decide from a local analysis, at every image point, if there is a road or not. The second stage aims at obtaining the road network structure. In the method we propose to rely on a Ã¢Â€ÂœpotentialÃ¢Â€Â image, that is, unstructured image data that can be derived from any road extractor filter. In such a potential image, the value assigned to a point is a measure of its likelihood to be located in the middle of a road. A filtering step applied on the potential image relies on the area closing operator followed by the watershed transform to obtain a connected line which encloses the road network. Then a graph describing adjacency relationships between watershed lines is built. Defining Markov random fields upon this graph, associated with an energetic model of road networks, leads to the expression of road network extraction as a global energy minimization problem. This method can easily be adapted to other image processing fields, where the recognition of curvilinear structures is involved.
Paul eMiller
2013-05-01
Full Text Available Randomly connected recurrent networks of excitatory groups of neurons can possess a multitude of attractor states. When the internal excitatory synapses of these networks are depressing, the attractor states can be destabilized with increasing input. This leads to an itinerancy, where with either repeated transient stimuli, or increasing duration of a single stimulus, the network activity advances through sequences of attractor states. We find that the resulting network state, which persists beyond stimulus offset, can encode the number of stimuli presented via a distributed representation of neural activity with non-monotonic tuning curves for most neurons. Increased duration of a single stimulus is encoded via different distributed representations, so unlike an integrator, the network distinguishes separate successive presentations of a short stimulus from a single presentation of a longer stimulus with equal total duration. Moreover, different amplitudes of stimulus cause new, distinct activity patterns, such that changes in stimulus number, duration and amplitude can be distinguished from each other. These properties of the network depend on dynamic depressing synapses, as they disappear if synapses are static. Thus short-term synaptic depression allows a network to store separately the different dynamic properties of a spatially constant stimulus.
Scaling of Peak Flows with Constant Flow Velocity in Random Self-Similar Networks
Mantilla, R.; Gupta, V. K.; Troutman, B. M.
2010-12-01
We present a methodology to understand the role of the statistical self-similar topology of real river networks on flow hydrographs for rainfall-runoff events. Monte Carlo generated ensembles of 1000 Random Self-similar Networks (RSNs) with geometrically distributed interior and exterior generators are created. We show how these networks emulate the statistical self-similarity present in real networks by presenting results for 30 river networks in the continental USA. Hydrographs for every link in each of these networks are obtained by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated hydrographs for an ensemble of RSNs, the scaling parameters for the peak of the width function (β) and the hydrograph peak flow (φ) are estimated. It was found that φ > β, which supports a similar finding first reported for the Walnut Gulch basin, Arizona, and that is qualitatively different from previous results on idealized river networks (e.g. Peano Network, Mandelbrot- Viscek Network). Scaling of peak flows for individual rainfall runoff events is a new area of research that offers a path to physically understand regional scaling of flood quantiles. It addresses an important open problem in river network hydrology through studying the statistics of ensembles of multiple events in RSNs. In addition, our methodology provides a reference framework to study scaling exponents and intercepts under more complex scenarios of flow dynamics and runoff generation processes using ensembles of RSNs. Preliminary examples of such scenarios will also be given.
Using Layer Recurrent Neural Network to Generate Pseudo Random Number Sequences
Veena Desai
2012-03-01
Full Text Available Pseudo Random Numbers (PRNs are required for many cryptographic applications. This paper proposes a new method for generating PRNs using Layer Recurrent Neural Network (LRNN. The proposed technique generates PRNs from the weight matrix obtained from the layer weights of the LRNN. The LRNN random number generator (RNG uses a short keyword as a seed and generates a long sequence as a pseudo PRN sequence. The number of bits generated in the PRN sequence depends on the number of neurons in the input layer of the LRNN. The generated PRN sequence changes, with a change in the training function of the LRNN .The sequences generated are a function of the keyword, initial state of network and the training function. In our implementation the PRN sequences have been generated using 3 training functions: 1Scaled Gradient Descent 2Levenberg-Marquartz (TRAINLM and 3 TRAINBGF. The generated sequences are tested for randomness using ENT and NIST test suites. The ENT test can be applied for sequences of small size. NIST has 16 tests to test random numbers. The LRNN generated PRNs pass in 11 tests, show no observations for 4 tests, and fail in 1 test when subjected to NIST .This paper presents the test results for random number sequence ranging from 25 bits to 1000 bits, generated using LRNN.
An adaptive random search for short term generation scheduling with network constraints.
Marmolejo, J A; Velasco, Jonás; Selley, Héctor J
2017-01-01
This paper presents an adaptive random search approach to address a short term generation scheduling with network constraints, which determines the startup and shutdown schedules of thermal units over a given planning horizon. In this model, we consider the transmission network through capacity limits and line losses. The mathematical model is stated in the form of a Mixed Integer Non Linear Problem with binary variables. The proposed heuristic is a population-based method that generates a set of new potential solutions via a random search strategy. The random search is based on the Markov Chain Monte Carlo method. The main key of the proposed method is that the noise level of the random search is adaptively controlled in order to exploring and exploiting the entire search space. In order to improve the solutions, we consider coupling a local search into random search process. Several test systems are presented to evaluate the performance of the proposed heuristic. We use a commercial optimizer to compare the quality of the solutions provided by the proposed method. The solution of the proposed algorithm showed a significant reduction in computational effort with respect to the full-scale outer approximation commercial solver. Numerical results show the potential and robustness of our approach.
Sensor selection for parameterized random field estimation in wireless sensor networks
无
2011-01-01
We consider the random field estimation problem with parametric trend in wireless sensor networks where the field can be described by unknown parameters to be estimated. Due to the limited resources, the network selects only a subset of the sensors to perform the estimation task with a desired performance under the D-optimal criterion. We propose a greedy sampling scheme to select the sensor nodes according to the information gain of the sensors. A distributed algorithm is also developed by consensus-based ...
Structure-properties relation for random networks of fibers with noncircular cross section
Deogekar, S.; Picu, R. C.
2017-03-01
The mechanical behavior of three-dimensional cross-linked random fiber networks composed from fibers of noncircular cross section characterized by two principal moments of inertia is studied in this work. Such fibers store energy in the axial deformation mode and two bending modes of unequal stiffness. We show that the torsional stiffness of fibers becomes important as it determines the relative contribution of the two bending modes to the overall deformation. The scaling of the small strain modulus with the network parameters is established. The large strain deformation of these structures is less sensitive to the shape of the cross section.
Stenull, O; Janssen, H K
2001-03-01
We study the multifractal moments of the current distribution in randomly diluted resistor networks near the percolation threshold. When an external current is applied between two terminals x and x(') of the network, the lth multifractal moment scales as M((l))(I)(x,x(')) approximately equal /x-x'/(psi(l)/nu), where nu is the correlation length exponent of the isotropic percolation universality class. By applying our concept of master operators [Europhys. Lett. 51, 539 (2000)] we calculate the family of multifractal exponents [psi(l)] for l>or=0 to two-loop order. We find that our result is in good agreement with numerical data for three dimensions.
Self-organized Criticality in an Earthquake Model on Random Network
无
2006-01-01
A simplified Olami-Feder-Christensen model on a random network has been studied. We propose a new toppling rule - when there is an unstable site toppling, the energy of the site is redistributed to its nearest neighbors randomly not averagely. The simulation results indicate that the model displays self-organized criticality when the system is conservative, and the avalanche size probability distribution of the system obeys finite size scaling. When the system is nonconservative, the model does not display scaling behavior. Simulation results of our model with different nearest neighbors q is also compared, which indicates that the spatialtopology does not alter the critical behavior of the system.
Performance Analysis for Mobile Ad Hoc Network in Random Graph Models with Spatial Reuse
Han-xing Wang; Xi Hu; Qin Zhang
2007-01-01
In this paper,we present a random graph model with spatial reuse for a mobile ad hoc network (MANET) based on the dynamic source routing protocol.Many important performance parameters of the MANET are obtained,such as the average flooding distance (AFD),the probability generating function of the flooding distance,and the probability of a flooding route to be symmetric.Compared with the random graph model without spatial reuse,this model is much more effective because it has a smaller value of AFD and a larger probability for finding a symmetric valid route.
Study of charge transport in highly conducting polymers based on a random resistor network
Zhou Liping [Department of Physics, Suzhou University, Suzhou 215006 (China)]. E-mail: lipichow@hotmail.com; Liu Bo [Department of Physics, Suzhou University, Suzhou 215006 (China); Department of Physics, Jiangsu Teachers University of Technology, Changzhou 213001 (China); Li Zhenya [CCAST (World Laboratory), P.O. Box 8730, Beijing 100080 (China) and Department of Physics, Suzhou University, Suzhou 215006 (China)]. E-mail: zyli@suda.edu.cn
2004-12-06
Based on a random resistor network (RRN), we study the unusual ac conductivity {sigma}({omega}) of highly conducting polymer such as PF{sub 6} doped polypyrrole. The system is modeled as a composite medium consisting of metallic regions randomly distributed in the amorphous parts. Within the metallic regions, the polymer chains are regularly and densely packed, outside which the poorly arranged chains form amorphous host. The metallic grains are connected by resonance quantum tunneling, which occurs through the strongly localized states in the amorphous media. {sigma}({omega}), calculated from this model, reproduces the main experimental features associated with the metal-insulator transition in these polymers.
Luo, Chao; Wang, Xingyuan
2013-01-01
A novel algebraic approach is proposed to study dynamics of asynchronous random Boolean networks where a random number of nodes can be updated at each time step (ARBNs). In this article, the logical equations of ARBNs are converted into the discrete-time linear representation and dynamical behaviors of systems are investigated. We provide a general formula of network transition matrices of ARBNs as well as a necessary and sufficient algebraic criterion to determine whether a group of given states compose an attractor of length in ARBNs. Consequently, algorithms are achieved to find all of the attractors and basins in ARBNs. Examples are showed to demonstrate the feasibility of the proposed scheme. PMID:23785502
Liu, Chengyin; Xu, Chunchuan; Teng, Jun
2016-09-01
The Random Decrement Technique (RDT), based on decentralized computing approaches implemented in wireless sensor networks (WSNs), has shown advantages for modal parameter and data aggregation identification. However, previous studies of RDT-based approaches from ambient vibration data are based on the assumption of a broad-band stochastic process input excitation. The process normally is modeled by filtered white or white noise. In addition, the choice of the triggering condition in RDT is closely related to data communication. In this project, research has been conducted to study the nonstationary white noise excitations as the input to verify the random decrement technique. A local extremum triggering condition is chosen and implemented for the purpose of minimum data communication in a RDT-based distributed computing strategy. Numerical simulation results show that the proposed technique is capable of minimizing the amount of data transmitted over the network with accuracy in modal parameters identification.
Inference of biological networks using Bi-directional Random Forest Granger causality.
Furqan, Mohammad Shaheryar; Siyal, Mohammad Yakoob
2016-01-01
The standard ordinary least squares based Granger causality is one of the widely used methods for detecting causal interactions between time series data. However, recent developments in technology limit the utilization of some existing implementations due to the availability of high dimensional data. In this paper, we are proposing a technique called Bi-directional Random Forest Granger causality. This technique uses the random forest regularization together with the idea of reusing the time series data by reversing the time stamp to extract more causal information. We have demonstrated the effectiveness of our proposed method by applying it to simulated data and then applied it to two real biological datasets, i.e., fMRI and HeLa cell. fMRI data was used to map brain network involved in deductive reasoning while HeLa cell dataset was used to map gene network involved in cancer.
Kalnaus, Sergiy [ORNL; Sabau, Adrian S [ORNL; Newman, Sarah M [ORNL; Tenhaeff, Wyatt E [ORNL; Daniel, Claus [ORNL; Dudney, Nancy J [ORNL
2011-01-01
The effective DC conductivity of particulate composite electrolytes was obtained by solving electrostatics equations using random resistors network method in three dimensions. The composite structure was considered to consist of three phases: matrix, particulate filler, and conductive shell that surrounded each particle; each phase possessing a different conductivity. Different particle size distributions were generated using Monte Carlo simulations. Unlike effective medium formulations, it was shown that the random resistors network method was able to predict percolation thresholds for the effective composite conductivity. It was found that the mean particle radius has a higher influence on the effective composite conductivity compared to the effect of type of the particle size distributions that were considered. The effect of the shell thickness on the composite conductivity has been investigated. It was found that the conductivity enhancement due to the presence of the conductive shell phase becomes less evident as the shell thickness increases.
Jayaweera SudharmanK
2010-01-01
Full Text Available Performance gain achieved by adding mobile nodes to a stationary sensor network for target detection depends on factors such as the number of mobile nodes deployed, mobility patterns, speed and energy constraints of mobile nodes, and the nature of the target locations (deterministic or random. In this paper, we address the problem of distributed detection of a randomly located target by a hybrid sensor network. Specifically, we develop two decision-fusion architectures for detection where in the first one, impact of node mobility is taken into account for decisions updating at the fusion center, while in the second model the impact of node mobility is taken at the node level decision updating. The cost of deploying mobile nodes is analyzed in terms of the minimum fraction of mobile nodes required to achieve the desired performance level within a desired delay constraint. Moreover, we consider managing node mobility under given constraints.
Physical states in the canonical tensor model from the perspective of random tensor networks
Narain, Gaurav; Sato, Yuki
2014-01-01
Tensor models, generalization of matrix models, are studied aiming for quantum gravity in dimensions larger than two. Among them, the canonical tensor model is formulated as a totally constrained system with first-class constraints, the algebra of which resembles the Dirac algebra of general relativity. When quantized, the physical states are defined to be vanished by the quantized constraints. In explicit representations, the constraint equations are a set of partial differential equations for physical wave-functions, which do not seem straightforward to solve due to their non-linear character. In this paper, after providing some explicit solutions for N = 2,3, we show that certain scale-free integration of partition functions of statistical systems on random networks, or random tensor networks more generally, provides a series of solutions for general N. Then, by generalizing this form, we also obtain various solutions for general N. Moreover, we show that the solutions for the cases with a cosmological con...
Step-Step Random Walk Network with Power-Law Clique-Degree Distribution
YANG Han-Xin; WANG Bing-Hong; LIU Jian-Guo; HAN Xiao-Pu; ZHOU Tao
2008-01-01
We propose a simple mechanism for generating scale-free networks with degree exponent γ=3, where the new node is connected to the existing nodes by step-by-step random walk. It is found that the clique-degree distribution based on our model obeys a power-law form, which is in agreement with the recently empirical evidences. In addition, our model displays the small-world effect and the hierarchical structure.
Chih-Hsueh Lin
2016-04-01
Full Text Available In wireless sensor networks, sensing information must be transmitted from sensor nodes to the base station by multiple hopping. Every sensor node is a sender and a relay node that forwards the sensing information that is sent by other nodes. Under an attack, the sensing information may be intercepted, modified, interrupted, or fabricated during transmission. Accordingly, the development of mutual trust to enable a secure path to be established for forwarding information is an important issue. Random key pre-distribution has been proposed to establish mutual trust among sensor nodes. This article modifies the random key pre-distribution to a random secret pre-distribution and incorporates identity-based cryptography to establish an effective method of establishing mutual trust for a wireless sensor network. In the proposed method, base station assigns an identity and embeds n secrets into the private secret keys for every sensor node. Based on the identity and private secret keys, the mutual trust method is utilized to explore the types of trust among neighboring sensor nodes. The novel method can resist malicious attacks and satisfy the requirements of wireless sensor network, which are resistance to compromising attacks, masquerading attacks, forger attacks, replying attacks, authentication of forwarding messages, and security of sensing information.
Distributed Synchronization in Networks of Agent Systems With Nonlinearities and Random Switchings.
Tang, Yang; Gao, Huijun; Zou, Wei; Kurths, Jürgen
2013-02-01
In this paper, the distributed synchronization problem of networks of agent systems with controllers and nonlinearities subject to Bernoulli switchings is investigated. Controllers and adaptive updating laws injected in each vertex of networks depend on the state information of its neighborhood. Three sets of Bernoulli stochastic variables are introduced to describe the occurrence probabilities of distributed adaptive controllers, updating laws and nonlinearities, respectively. By the Lyapunov functions method, we show that the distributed synchronization of networks composed of agent systems with multiple randomly occurring nonlinearities, multiple randomly occurring controllers, and multiple randomly occurring updating laws can be achieved in mean square under certain criteria. The conditions derived in this paper can be solved by semi-definite programming. Moreover, by mathematical analysis, we find that the coupling strength, the probabilities of the Bernoulli stochastic variables, and the form of nonlinearities have great impacts on the convergence speed and the terminal control strength. The synchronization criteria and the observed phenomena are demonstrated by several numerical simulation examples. In addition, the advantage of distributed adaptive controllers over conventional adaptive controllers is illustrated.
Raquel Caballero-Águila
2015-01-01
Full Text Available The distributed fusion state estimation problem is addressed for sensor network systems with random state transition matrix and random measurement matrices, which provide a unified framework to consider some network-induced random phenomena. The process noise and all the sensor measurement noises are assumed to be one-step autocorrelated and different sensor noises are one-step cross-correlated; also, the process noise and each sensor measurement noise are two-step cross-correlated. These correlation assumptions cover many practical situations, where the classical independence hypothesis is not realistic. Using an innovation methodology, local least-squares linear filtering estimators are recursively obtained at each sensor. The distributed fusion method is then used to form the optimal matrix-weighted sum of these local filters according to the mean squared error criterion. A numerical simulation example shows the accuracy of the proposed distributed fusion filtering algorithm and illustrates some of the network-induced stochastic uncertainties that can be dealt with in the current system model, such as sensor gain degradation, missing measurements, and multiplicative noise.
Arboleda Serna, Víctor Hugo; Arango Vélez, Elkin Fernando; Gómez Arias, Rubén Darío; Feito, Yuri
2016-08-18
Participation in aerobic exercise generates increased cardiorespiratory fitness, which results in a protective factor for cardiovascular disease and all-cause mortality. High-intensity interval training might cause higher increases in cardiorespiratory fitness in comparison with moderate-intensity continuous training; nevertheless, current evidence is not conclusive. To our knowledge, this is the first study to test the effect of high-intensity interval training with total load duration of 7.5 min per session. A randomized controlled trial will be performed on two groups of healthy, sedentary male volunteers (n = 44). The study protocol will include 24 exercise sessions, three times a week, including aerobic training on a treadmill and strength training exercises. The intervention group will perform 15 bouts of 30 s, each at an intensity between 90 % and 95 % of maximal heart rate. The control group will complete 40 min of continuous exercise, ranging between 65 % and 75 % of maximal heart rate. The primary outcome measure to be evaluated will be maximal oxygen uptake (VO2max), and systolic and diastolic blood pressure will be evaluated as secondary outcome measures. Waist circumference, body mass index, and body composition will also be evaluated. Epidemiological evidence shows the link between VO2max and its association with chronic conditions that trigger CVD. Therefore, finding ways to improve VO2max and reduce blood pressure it is of vital importance to public health. NCT02288403 . Registered on 4 November 2014.
Exact Solutions of Degree Distributions for Random Birth-and-Death Networks
Zhang, Xiaojun; Rayman-Bacchus, Lez
2015-01-01
In this paper, a general random birth-and-death network model (RBDN) is considered, in which at each time step, a new node is added into the network with probability p or an existing node is deleted from the network with probability q=1-p. For different p (1>p>1/2, 0
Self-organized Balanced Resources in Random Networks with Transportation Bandwidths
Yeung, Chi Ho; Wong, K. Y. Michael
We apply statistical physics to study the task of resource allocation in random networks with limited bandwidths for the transportation of resources along the links. We derive algorithms which searches the optimal solution without the need of a global optimizer. For networks with uniformly high connectivity, the resource shortage of a node becomes a well-defined function of its capacity. An efficient profile of the allocated resources is found, with clusters of node interconnected by an extensive fraction of unsaturated links, enabling the resource shortages among the nodes to remain balanced. The capacity-shortage relation exhibits features similar to the Maxwell’s construction. For scale-free networks, such an efficient profile is observed even for nodes of low connectivity.
An Effective Randomized QoS Routing Algorithm on Networks with Inaccurate Parameters
WANG Jianxin(王建新); CHEN Jian'er(陈建二); CHEN Songqiao(陈松乔)
2002-01-01
This paper develops an effective randomized on-demand QoS routing algorithm on networks with inaccurate link-state information. Several new techniques are proposed in the algorithm. First, the maximum safety rate and the minimum delay for each node in the network are pre-computed, which simplify the network complexity and provide the routing process with useful information. The routing process is dynamically directed by the safety rate and delay of the partial routing path developed so far and by the maximum safety rate and the minimum delay of the next node. Randomness is used at the link level and depends dynamically on the routing configuration. This provides great flexibility for the routing process, prevents the routing process from overusing certain fixed routing paths, and adequately balances the safety rate and delay of the routing path. A network testing environment has been established and five parameters are introduced to measure the performance of QoS routing algorithms.Experimental results demonstrate that in terms of the proposed parameters, the algorithm outperforms existing QoS algorithms appearing in the literature.
Estimating the Size of a Large Network and its Communities from a Random Sample
Chen, Lin; Crawford, Forrest W
2016-01-01
Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = (V;E) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W and letting G(W) be the induced subgraph in G of the vertices in W. In addition to G(W), we observe the total degree of each sampled vertex and its block membership. Given this partial information, we propose an efficient PopULation Size Estimation algorithm, called PULSE, that correctly estimates the size of the whole population as well as the size of each community. To support our theoretical analysis, we perform an exhausti...
Synaptic signal streams generated by ex vivo neuronal networks contain non-random, complex patterns.
Lee, Sangmook; Zemianek, Jill M; Shultz, Abraham; Vo, Anh; Maron, Ben Y; Therrien, Mikaela; Courtright, Christina; Guaraldi, Mary; Yanco, Holly A; Shea, Thomas B
2014-11-01
Cultured embryonic neurons develop functional networks that transmit synaptic signals over multiple sequentially connected neurons as revealed by multi-electrode arrays (MEAs) embedded within the culture dish. Signal streams of ex vivo networks contain spikes and bursts of varying amplitude and duration. Despite the random interactions inherent in dissociated cultures, neurons are capable of establishing functional ex vivo networks that transmit signals among synaptically connected neurons, undergo developmental maturation, and respond to exogenous stimulation by alterations in signal patterns. These characteristics indicate that a considerable degree of organization is an inherent property of neurons. We demonstrate herein that (1) certain signal types occur more frequently than others, (2) the predominant signal types change during and following maturation, (3) signal predominance is dependent upon inhibitory activity, and (4) certain signals preferentially follow others in a non-reciprocal manner. These findings indicate that the elaboration of complex signal streams comprised of a non-random distribution of signal patterns is an emergent property of ex vivo neuronal networks.
Profit maximization mitigates competition
Dierker, Egbert; Grodal, Birgit
1996-01-01
We consider oligopolistic markets in which the notion of shareholders' utility is well-defined and compare the Bertrand-Nash equilibria in case of utility maximization with those under the usual profit maximization hypothesis. Our main result states that profit maximization leads to less price...... competition than utility maximization. Since profit maximization tends to raise prices, it may be regarded as beneficial for the owners as a whole. Moreover, if profit maximization is a good proxy for utility maximization, then there is no need for a general equilibrium analysis that takes the distribution...... of profits among consumers fully into account and partial equilibrium analysis suffices...
Ansari, Imran Shafique
2012-09-08
The probability distribution function (PDF) and cumulative density function of the sum of L independent but not necessarily identically distributed gamma variates, applicable to maximal ratio combining receiver outputs or in other words applicable to the performance analysis of diversity combining receivers operating over Nakagami-m fading channels, is presented in closed form in terms of Meijer G-function and Fox H-bar-function for integer valued fading parameters and non-integer valued fading parameters, respectively. Further analysis, particularly on bit error rate via PDF-based approach, too is represented in closed form in terms of Meijer G-function and Fox H-bar-function for integer-order fading parameters, and extended Fox H-bar-function (H-hat) for non-integer-order fading parameters. The proposed results complement previous results that are either evolved in closed-form, or expressed in terms of infinite sums or higher order derivatives of the fading parameter m.
Biased random walk in spatially embedded networks with total cost constraint
Niu, Rui-Wu; Pan, Gui-Jun
2016-11-01
We investigate random walk with a bias toward a target node in spatially embedded networks with total cost restriction introduced by Li et al. (2010). Precisely, The network is built from a two-dimension regular lattice to be improved by adding long-range shortcuts with probability P(rij) ∼rij-α, where rij is the Manhattan distance between sites i and j, and α is a variable exponent, the total length of the long-range connections is restricted. Bias is represented as a probability p of the packet or particle to travel at every hop toward the node which has the smallest Manhattan distance to the target node. By studying the mean first passage time (MFPT) for different exponent log , we find that the best transportation condition is obtained with an exponent α = d + 1(d = 2) for all p. The special phenomena can be possibly explained by the theory of information entropy, we find that when α = d + 1(d = 2), the spatial network with total cost restriction becomes an optimal network which has a maximum information entropy. In addition, the scaling of the MFPT with the size of the network is also investigated, and finds that the scaling of the MFPT with L follows a linear distribution for all p > 0.
Paule-Mandel estimators for network meta-analysis with random inconsistency effects.
Jackson, Dan; Veroniki, Areti Angeliki; Law, Martin; Tricco, Andrea C; Baker, Rose
2017-06-05
Network meta-analysis is used to simultaneously compare multiple treatments in a single analysis. However, network meta-analyses may exhibit inconsistency, where direct and different forms of indirect evidence are not in agreement with each other, even after allowing for between-study heterogeneity. Models for network meta-analysis with random inconsistency effects have the dual aim of allowing for inconsistencies and estimating average treatment effects across the whole network. To date, two classical estimation methods for fitting this type of model have been developed: a method of moments that extends DerSimonian and Laird's univariate method and maximum likelihood estimation. However, the Paule and Mandel estimator is another recommended classical estimation method for univariate meta-analysis. In this paper, we extend the Paule and Mandel method so that it can be used to fit models for network meta-analysis with random inconsistency effects. We apply all three estimation methods to a variety of examples that have been used previously and we also examine a challenging new dataset that is highly heterogenous. We perform a simulation study based on this new example. We find that the proposed Paule and Mandel method performs satisfactorily and generally better than the previously proposed method of moments because it provides more accurate inferences. Furthermore, the Paule and Mandel method possesses some advantages over likelihood-based methods because it is both semiparametric and requires no convergence diagnostics. Although restricted maximum likelihood estimation remains the gold standard, the proposed methodology is a fully viable alternative to this and other estimation methods. © 2017 The Authors. Research Synthesis Methods Published by John Wiley & Sons Ltd.
Wang, Na; Zeng, Jiwen
2017-03-17
Wireless sensor networks are deployed to monitor the surrounding physical environments and they also act as the physical environments of parasitic sensor networks, whose purpose is analyzing the contextual privacy and obtaining valuable information from the original wireless sensor networks. Recently, contextual privacy issues associated with wireless communication in open spaces have not been thoroughly addressed and one of the most important challenges is protecting the source locations of the valuable packages. In this paper, we design an all-direction random routing algorithm (ARR) for source-location protecting against parasitic sensor networks. For each package, the routing process of ARR is divided into three stages, i.e., selecting a proper agent node, delivering the package to the agent node from the source node, and sending it to the final destination from the agent node. In ARR, the agent nodes are randomly chosen in all directions by the source nodes using only local decisions, rather than knowing the whole topology of the networks. ARR can control the distributions of the routing paths in a very flexible way and it can guarantee that the routing paths with the same source and destination are totally different from each other. Therefore, it is extremely difficult for the parasitic sensor nodes to trace the packages back to the source nodes. Simulation results illustrate that ARR perfectly confuses the parasitic nodes and obviously outperforms traditional routing-based schemes in protecting source-location privacy, with a marginal increase in the communication overhead and energy consumption. In addition, ARR also requires much less energy than the cloud-based source-location privacy protection schemes.
Wang, Na; Zeng, Jiwen
2017-01-01
Wireless sensor networks are deployed to monitor the surrounding physical environments and they also act as the physical environments of parasitic sensor networks, whose purpose is analyzing the contextual privacy and obtaining valuable information from the original wireless sensor networks. Recently, contextual privacy issues associated with wireless communication in open spaces have not been thoroughly addressed and one of the most important challenges is protecting the source locations of the valuable packages. In this paper, we design an all-direction random routing algorithm (ARR) for source-location protecting against parasitic sensor networks. For each package, the routing process of ARR is divided into three stages, i.e., selecting a proper agent node, delivering the package to the agent node from the source node, and sending it to the final destination from the agent node. In ARR, the agent nodes are randomly chosen in all directions by the source nodes using only local decisions, rather than knowing the whole topology of the networks. ARR can control the distributions of the routing paths in a very flexible way and it can guarantee that the routing paths with the same source and destination are totally different from each other. Therefore, it is extremely difficult for the parasitic sensor nodes to trace the packages back to the source nodes. Simulation results illustrate that ARR perfectly confuses the parasitic nodes and obviously outperforms traditional routing-based schemes in protecting source-location privacy, with a marginal increase in the communication overhead and energy consumption. In addition, ARR also requires much less energy than the cloud-based source-location privacy protection schemes. PMID:28304367
Satoh, Kazuhiro
1989-08-01
Numerical studies are made out the behavior of a random neural network in which each neuron is coupled to a certain number of randomly chosen neurons. Such a random-net serves as a simple model for an elemental sub-network of the cortex. Neurons are regarded as binary decision elements, and they synchronously update their values in discrete time steps according to a deterministic equation (the McCulloch-Pitts model). It is found that each random-net containing one hundred neurons has only a few kinds of characteristic modes of excitation. Periods of these modes are usually less than ten steps when the number of connections per neuron is two to five. For the random-net containing one thousand neurons, an excited mode is practically aperiodic. When the refractory period is introduced, however, a nearly periodic oscillation takes place in the activity of the network.
Ilday, Serim; Ilday, F Ömer; Hübner, René; Prosa, Ty J; Martin, Isabelle; Nogay, Gizem; Kabacelik, Ismail; Mics, Zoltan; Bonn, Mischa; Turchinovich, Dmitry; Toffoli, Hande; Toffoli, Daniele; Friedrich, David; Schmidt, Bernd; Heinig, Karl-Heinz; Turan, Rasit
2016-03-09
Multiscale self-assembly is ubiquitous in nature but its deliberate use to synthesize multifunctional three-dimensional materials remains rare, partly due to the notoriously difficult problem of controlling topology from atomic to macroscopic scales to obtain intended material properties. Here, we propose a simple, modular, noncolloidal methodology that is based on exploiting universality in stochastic growth dynamics and driving the growth process under far-from-equilibrium conditions toward a preplanned structure. As proof of principle, we demonstrate a confined-but-connected solid structure, comprising an anisotropic random network of silicon quantum-dots that hierarchically self-assembles from the atomic to the microscopic scales. First, quantum-dots form to subsequently interconnect without inflating their diameters to form a random network, and this network then grows in a preferential direction to form undulated and branching nanowire-like structures. This specific topology simultaneously achieves two scale-dependent features, which were previously thought to be mutually exclusive: good electrical conduction on the microscale and a bandgap tunable over a range of energies on the nanoscale.
Improved Neural Networks with Random Weights for Short-Term Load Forecasting.
Kun Lang
Full Text Available An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW. The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.
Improved Neural Networks with Random Weights for Short-Term Load Forecasting.
Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo
2015-01-01
An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.
Ge Chen; Tian-de Guo; Chang-long Yao
2009-01-01
In this paper we consider the standard Poisson Boolean model of random geometric graphs G(Hλ,s; 1) in Rd and study the properties of the order of the largest component L1(G(Hλ,s; 1)) .We prove that E[L1(G(Hλ,s; 1))]is smooth with respect to λ,and is derivable with respect to s.Also,we give the expression of these derivatives.These studies provide some new methods for the theory of the largest component of finite random geometric graphs (not asymptotic graphs as s→∞) in the high dimensional space (d≥2).Moreover,we investigate the convergence rate of E[L1(G(Hλ,s; 1))].These results have significance for theory develop-ment of random geometric graphs and its practical application.Using our theories,we construct and solve a new optimal energy-efficient topology control model of wireless sensor networks,which has the significance of theoretical foundation and guidance for the design of network layout.
Zhuo Qi Lee
Full Text Available Biased random walk has been studied extensively over the past decade especially in the transport and communication networks communities. The mean first passage time (MFPT of a biased random walk is an important performance indicator in those domains. While the fundamental matrix approach gives precise solution to MFPT, the computation is expensive and the solution lacks interpretability. Other approaches based on the Mean Field Theory relate MFPT to the node degree alone. However, nodes with the same degree may have very different local weight distribution, which may result in vastly different MFPT. We derive an approximate bound to the MFPT of biased random walk with short relaxation time on complex network where the biases are controlled by arbitrarily assigned node weights. We show that the MFPT of a node in this general case is closely related to not only its node degree, but also its local weight distribution. The MFPTs obtained from computer simulations also agree with the new theoretical analysis. Our result enables fast estimation of MFPT, which is useful especially to differentiate between nodes that have very different local node weight distribution even though they share the same node degrees.
Maximal inequalities for demimartingales and their applications
WANG XueJun; HU ShuHe
2009-01-01
In this paper,we establish some maximal inequalities for demimartingales which generalize and improve the results of Christofides.The maximal inequalities for demimartingales are used as key inequalities to establish other results including Doob's type maximal inequality for demimartingales,strong laws of large numbers and growth rate for demimartingales and associated random variables.At last,we give an equivalent condition of uniform integrability for demisubmartingales.
Maximal inequalities for demimartingales and their applications
无
2009-01-01
In this paper, we establish some maximal inequalities for demimartingales which generalize and improve the results of Christofides. The maximal inequalities for demimartingales are used as key inequalities to establish other results including Doob’s type maximal inequality for demimartingales, strong laws of large numbers and growth rate for demimartingales and associated random variables. At last, we give an equivalent condition of uniform integrability for demisubmartingales.