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.
Drossel, Barbara
2007-01-01
This review explains in a self-contained way the properties of random Boolean networks and their attractors, with a special focus on critical networks. Using small example networks, analytical calculations, phenomenological arguments, and problems to solve, the basic concepts are introduced and important results concerning phase diagrams, numbers of relevant nodes and attractor properties are derived.
Boolean networks as modelling framework
Florian eGreil
2012-08-01
Full Text Available In a network, the components of a given system are represented as nodes, the interactions are abstracted as links between the nodes. Boolean networks refer to a class of dynamics on networks, in fact it is the simplest possible dynamics where each node has a value 0 or 1. This allows to investigate extensively the dynamics both analytically and by numerical experiments. The present article focuses on the theoretical concept of relevant components and the immediate application in plant biology, references for more in-depths treatment of the mathematical details are also given.
Boolean networks with reliable dynamics
Peixoto, Tiago P
2009-01-01
We investigated the properties of Boolean networks that follow a given reliable trajectory in state space. A reliable trajectory is defined as a sequence of states which is independent of the order in which the nodes are updated. We explored numerically the topology, the update functions, and the state space structure of these networks, which we constructed using a minimum number of links and the simplest update functions. We found that the clustering coefficient is larger than in random networks, and that the probability distribution of three-node motifs is similar to that found in gene regulation networks. Among the update functions, only a subset of all possible functions occur, and they can be classified according to their probability. More homogeneous functions occur more often, leading to a dominance of canalyzing functions. Finally, we studied the entire state space of the networks. We observed that with increasing systems size, fixed points become more dominant, moving the networks close to the frozen...
Forced synchronization of autonomous dynamical Boolean networks
Rivera-Durón, R. R., E-mail: roberto.rivera@ipicyt.edu.mx; Campos-Cantón, E., E-mail: eric.campos@ipicyt.edu.mx [División de Matemáticas Aplicadas, Instituto Potosino de Investigación Científica y Tecnológica A. C., Camino a la Presa San José 2055, Col. Lomas 4 Sección, C.P. 78216, San Luis Potosí, S.L.P. (Mexico); Campos-Cantón, I. [Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Álvaro Obregón 64, C.P. 78000, San Luis Potosí, S.L.P. (Mexico); Gauthier, Daniel J. [Department of Physics and Center for Nonlinear and Complex Systems, Duke University, Box 90305, Durham, North Carolina 27708 (United States)
2015-08-15
We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.
Forced synchronization of autonomous dynamical Boolean networks
We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics
Boolean network robotics: a proof of concept
Roli, Andrea; Pinciroli, Carlo; Birattari, Mauro
2011-01-01
Dynamical systems theory and complexity science provide powerful tools for analysing artificial agents and robots. Furthermore, they have been recently proposed also as a source of design principles and guidelines. Boolean networks are a prominent example of complex dynamical systems and they have been shown to effectively capture important phenomena in gene regulation. From an engineering perspective, these models are very compelling, because they can exhibit rich and complex behaviours, in spite of the compactness of their description. In this paper, we propose the use of Boolean networks for controlling robots' behaviour. The network is designed by means of an automatic procedure based on stochastic local search techniques. We show that this approach makes it possible to design a network which enables the robot to accomplish a task that requires the capability of navigating the space using a light stimulus, as well as the formation and use of an internal memory.
Boolean Factor Analysis by Attractor Neural Network
Frolov, A. A.; Húsek, Dušan; Muraviev, I. P.; Polyakov, P.Y.
2007-01-01
Roč. 18, č. 3 (2007), s. 698-707. ISSN 1045-9227 R&D Projects: GA AV ČR 1ET100300419; GA ČR GA201/05/0079 Institutional research plan: CEZ:AV0Z10300504 Keywords : recurrent neural network * Hopfield-like neural network * associative memory * unsupervised learning * neural network architecture * neural network application * statistics * Boolean factor analysis * dimensionality reduction * features clustering * concepts search * information retrieval Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.769, year: 2007
Effect of memory in non-Markovian Boolean networks
Ebadi, Haleh; Ausloos, Marcel; Jafari, GholamReza
2016-01-01
One successful model of interacting biological systems is the Boolean network. The dynamics of a Boolean network, controlled with Boolean functions, is usually considered to be a Markovian (memory-less) process. However, both self organizing features of biological phenomena and their intelligent nature should raise some doubt about ignoring the history of their time evolution. Here, we extend the Boolean network Markovian approach: we involve the effect of memory on the dynamics. This can be explored by modifying Boolean functions into non-Markovian functions, for example, by investigating the usual non-Markovian threshold function, - one of the most applied Boolean functions. By applying the non-Markovian threshold function on the dynamical process of a cell cycle network, we discover a power law memory with a more robust dynamics than the Markovian dynamics.
The value of less connected agents in Boolean networks
Epstein, Daniel; Bazzan, Ana L. C.
2013-11-01
In multiagent systems, agents often face binary decisions where one seeks to take either the minority or the majority side. Examples are minority and congestion games in general, i.e., situations that require coordination among the agents in order to depict efficient decisions. In minority games such as the El Farol Bar Problem, previous works have shown that agents may reach appropriate levels of coordination, mostly by looking at the history of past decisions. Not many works consider any kind of structure of the social network, i.e., how agents are connected. Moreover, when structure is indeed considered, it assumes some kind of random network with a given, fixed connectivity degree. The present paper departs from the conventional approach in some ways. First, it considers more realistic network topologies, based on preferential attachments. This is especially useful in social networks. Second, the formalism of random Boolean networks is used to help agents to make decisions given their attachments (for example acquaintances). This is coupled with a reinforcement learning mechanism that allows agents to select strategies that are locally and globally efficient. Third, we use agent-based modeling and simulation, a microscopic approach, which allows us to draw conclusions about individuals and/or classes of individuals. Finally, for the sake of illustration we use two different scenarios, namely the El Farol Bar Problem and a binary route choice scenario. With this approach we target systems that adapt dynamically to changes in the environment, including other adaptive decision-makers. Our results using preferential attachments and random Boolean networks are threefold. First we show that an efficient equilibrium can be achieved, provided agents do experimentation. Second, microscopic analysis show that influential agents tend to consider few inputs in their Boolean functions. Third, we have also conducted measurements related to network clustering and centrality
Solving the Satisfiability Problem Through Boolean Networks
Roli, Andrea
2011-01-01
In this paper we present a new approach to solve the satisfiability problem (SAT), based on boolean networks (BN). We define a mapping between a SAT instance and a BN, and we solve SAT problem by simulating the BN dynamics. We prove that BN fixed points correspond to the SAT solutions. The mapping presented allows to develop a new class of algorithms to solve SAT. Moreover, this new approach suggests new ways to combine symbolic and connectionist computation and provides a general framework for local search algorithms.
Neural Network Boolean Factor Analysis and Applications
Húsek, Dušan; Frolov, A.; Polyakov, P.Y.; Snášel, V.
-: WSEAS Press, 2007 - (Katehakis, M.; And ina, D.; Mastorakis, M.), s. 30-35. (Electrical and Computer Engineering Series). ISBN 978-960-6766-21-3. [CIMMACS'07. WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics. Tenerife (ES), 14.12.2007-16.12.2007] R&D Projects: GA MŠk 1M0567; GA AV ČR 1ET100300414; GA ČR GA201/05/0079 Institutional research plan: CEZ:AV0Z10300504 Keywords : Hopfield neural network * boolean factor analysis * unsupervised learning * dimension reduction * data mining Subject RIV: BB - Applied Statistics, Operational Research
Solving the Satisfiability Problem Through Boolean Networks
Roli, Andrea; Milano, Michela
2011-01-01
In this paper we present a new approach to solve the satisfiability problem (SAT), based on boolean networks (BN). We define a mapping between a SAT instance and a BN, and we solve SAT problem by simulating the BN dynamics. We prove that BN fixed points correspond to the SAT solutions. The mapping presented allows to develop a new class of algorithms to solve SAT. Moreover, this new approach suggests new ways to combine symbolic and connectionist computation and provides a general framework f...
Representations of Boolean Functions by Perceptron Networks
Kůrková, Věra
Prague : Institute of Computer Science AS CR, 2014 - (Kůrková, V.; Bajer, L.; Peška, L.; Vojtáš, R.; Holeňa, M.; Nehéz, M.), s. 68-70 ISBN 978-80-87136-19-5. [ITAT 2014. European Conference on Information Technologies - Applications and Theory /14./. Demänovská dolina (SK), 25.09.2014-29.09.2014] R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : perceptron networks * model complexity * Boolean functions Subject RIV: IN - Informatics, Computer Science
ON REDUCED SCALAR EQUATIONS FOR SYNCHRONOUS BOOLEAN NETWORKS
Ali Muhammad Ali Rushdi; Adnan Ahmad Alsogati
2013-01-01
A total description of a synchronous Boolean network is typically achieved by a matrix recurrence relation. A simpler alternative is to use a scalar equation which is a possibly nonlinear equation that involves two or more instances of a single scalar variable and some Boolean operator(s). Further simplification is possible in terms of a linear reduced scalar equation which is the simplest two-term scalar equation that includes no Boolean operators and equates the value of a scalar variable a...
Consistent stabilizability of switched Boolean networks.
Li, Haitao; Wang, Yuzhen
2013-10-01
This paper investigates the consistent stabilizability of switched Boolean networks (SBNs) by using the semi-tensor product method, and presents a number of new results. First, an algebraic expression of SBNs is obtained by the semi-tensor product, based on which the consistent stabilizability is then studied for SBNs and some necessary and sufficient conditions are presented for the design of free-form and state-feedback switching signals, respectively. Finally, the consistent stabilizability of SBNs with state constraints is considered and some necessary and sufficient conditions are proposed. The study of illustrative examples shows that the new results obtained in this paper are very effective in designing switching signals for the consistent stabilizability of SBNs. PMID:23787170
Boolean network model predicts knockout mutant phenotypes of fission yeast.
Maria I Davidich
Full Text Available BOOLEAN NETWORKS (OR: networks of switches are extremely simple mathematical models of biochemical signaling networks. Under certain circumstances, Boolean networks, despite their simplicity, are capable of predicting dynamical activation patterns of gene regulatory networks in living cells. For example, the temporal sequence of cell cycle activation patterns in yeasts S. pombe and S. cerevisiae are faithfully reproduced by Boolean network models. An interesting question is whether this simple model class could also predict a more complex cellular phenomenology as, for example, the cell cycle dynamics under various knockout mutants instead of the wild type dynamics, only. Here we show that a Boolean network model for the cell cycle control network of yeast S. pombe correctly predicts viability of a large number of known mutants. So far this had been left to the more detailed differential equation models of the biochemical kinetics of the yeast cell cycle network and was commonly thought to be out of reach for models as simplistic as Boolean networks. The new results support our vision that Boolean networks may complement other mathematical models in systems biology to a larger extent than expected so far, and may fill a gap where simplicity of the model and a preference for an overall dynamical blueprint of cellular regulation, instead of biochemical details, are in the focus.
Measuring Mutual Information in Random Boolean Networks
Luque, B; Luque, Bartolo; Ferrera, Antonio
1999-01-01
During the last few years an area of active research in the field of complex systems is that of their information storing and processing abilities. Common opinion has it that the most interesting beaviour of these systems is found ``at the edge of chaos'', which would seem to suggest that complex systems may have inherently non-trivial information proccesing abilities in the vicinity of sharp phase transitions. A comprenhensive, quantitative understanding of why this is the case is however still lacking. Indeed, even ``experimental'' (i.e., often numerical) evidence that this is so has been questioned for a number of systems. In this paper we will investigate, both numerically and analitically, the behavior of Random Boolean Networks (RBN's) as they undergo their order-disorder phase transition. We will use a simple mean field approximation to treat the problem, and without lack of generality we will concentrate on a particular value for the connectivity of the system. In spite of the simplicity of our argume...
Enhancing Boolean networks with fuzzy operators and edge tuning
Poret, Arnaud; Monteiro Sousa, Claudio; Boissel, Jean-Pierre
2014-01-01
Quantitative modeling in systems biology can be difficult due to the scarcity of quantitative details about biological phenomenons, especially at the subcellular scale. An alternative to escape this difficulty is qualitative modeling since it requires few to no quantitative information. Among the qualitative modeling approaches, the Boolean network formalism is one of the most popular. However, Boolean models allow variables to be valued at only true or false, which can appear too simplistic ...
IMS Algorithm for Learning Representations in Boolean Neural Networks
Biswas, Nripendra N; Murthy, TVMK; Chandrasekhar, M.
1991-01-01
A new algorithm for learning representations in Boolean neural networks, where the inputs and outputs are binary bits, is presented. The algorithm has become feasible because of a newly discovered theorem which states that any non-linearly separable Boolean function can be expressed as a convergent series of linearly separable functions connected by the logical OR (+) and the logical INHIBIT (-) operators. The formation of the series is carried out by many important properties exhibited by th...
The Influence of Canalization on the Robustness of Boolean Networks
Kadelka, Claus; Laubenbacher, Reinhard
2016-01-01
Time- and state-discrete dynamical systems are frequently used to model molecular networks. This paper provides a collection of mathematical and computational tools for the study of robustness in Boolean network models. The focus is on networks governed by $k$-canalizing functions, a recently introduced class of Boolean functions that contains the well-studied class of nested canalizing functions. The activities and sensitivity of a function quantify the impact of input changes on the function output. This paper generalizes the latter concept to $c$-sensitivity and provides formulas for the activities and $c$-sensitivity of general $k$-canalizing functions as well as canalizing functions with more precisely defined structure. A popular measure for the robustness of a network, the Derrida value, can be expressed as a weighted sum of the $c$-sensitivities of the governing canalizing functions, and can also be calculated for a stochastic extension of Boolean networks. These findings provide a computationally eff...
Binary higher order neural networks for realizing Boolean functions.
Zhang, Chao; Yang, Jie; Wu, Wei
2011-05-01
In order to more efficiently realize Boolean functions by using neural networks, we propose a binary product-unit neural network (BPUNN) and a binary π-ς neural network (BPSNN). The network weights can be determined by one-step training. It is shown that the addition " σ," the multiplication " π," and two kinds of special weighting operations in BPUNN and BPSNN can implement the logical operators " ∨," " ∧," and " ¬" on Boolean algebra 〈Z(2),∨,∧,¬,0,1〉 (Z(2)={0,1}), respectively. The proposed two neural networks enjoy the following advantages over the existing networks: 1) for a complete truth table of N variables with both truth and false assignments, the corresponding Boolean function can be realized by accordingly choosing a BPUNN or a BPSNN such that at most 2(N-1) hidden nodes are needed, while O(2(N)), precisely 2(N) or at most 2(N), hidden nodes are needed by existing networks; 2) a new network BPUPS based on a collaboration of BPUNN and BPSNN can be defined to deal with incomplete truth tables, while the existing networks can only deal with complete truth tables; and 3) the values of the weights are all simply -1 or 1, while the weights of all the existing networks are real numbers. Supporting numerical experiments are provided as well. Finally, we present the risk bounds of BPUNN, BPSNN, and BPUPS, and then analyze their probably approximately correct learnability. PMID:21427020
ON REDUCED SCALAR EQUATIONS FOR SYNCHRONOUS BOOLEAN NETWORKS
Ali Muhammad Ali Rushdi
2013-01-01
Full Text Available A total description of a synchronous Boolean network is typically achieved by a matrix recurrence relation. A simpler alternative is to use a scalar equation which is a possibly nonlinear equation that involves two or more instances of a single scalar variable and some Boolean operator(s. Further simplification is possible in terms of a linear reduced scalar equation which is the simplest two-term scalar equation that includes no Boolean operators and equates the value of a scalar variable at a latter instance t2 to its value at an earlier instance t1. This equation remains valid when the times t1 and t2 are both augmented by any integral multiple of the underlying time period. In other words, there are infinitely many versions of a reduced scalar equation, any of which is useful for deducing information about the cyclic behavior of the network. However, to obtain correct information about the transient behavior of the network, one must find the true reduced scalar equation for which instances t1 and t2 are minimal. This study investigates the nature, derivation and utilization of reduced scalar equations. It relies on Boolean-algebraic manipulations for the derivation of such equations and suggests that this derivation can be facilitated by seeking certain orthogonality relations among certain successive (albeit not necessarily consecutive instances of the same scalar variable. We demonstrate, contrary to previously published assumptions or assertions, that there is typically no common reduced scalar equation for all the scalar variables. Each variable usually satisfies its own distinct reduced scalar equation. We also demonstrate that the derivation of a reduced scalar equation is achieved not only by proving it but also by disproving an immediately preceding version of it when such a version might exist. We also demonstrate that, despite the useful insight supplied by the reduced scalar equations, they do not provide a total solution like the
Optimal Computation of Symmetric Boolean Functions in Collocated Networks
Kowshik, Hemant
2011-01-01
We consider collocated wireless sensor networks, where each node has a Boolean measurement and the goal is to compute a given Boolean function of these measurements. We first consider the worst case setting and study optimal block computation strategies for computing symmetric Boolean functions. We study three classes of functions: threshold functions, delta functions and interval functions. We provide exactly optimal strategies for the first two classes, and a scaling law order-optimal strategy with optimal preconstant for interval functions. We also extend the results to the case of integer measurements and certain integer-valued functions. We use lower bounds from communication complexity theory, and provide an achievable scheme using information theoretic tools. Next, we consider the case where nodes measurements are random and drawn from independent Bernoulli distributions. We address the problem of optimal function computation so as to minimize the expected total number of bits that are transmitted. In ...
Boolean network representation of contagion dynamics during a financial crisis
Caetano, Marco Antonio Leonel; Yoneyama, Takashi
2015-01-01
This work presents a network model for representation of the evolution of certain patterns of economic behavior. More specifically, after representing the agents as points in a space in which each dimension associated to a relevant economic variable, their relative "motions" that can be either stationary or discordant, are coded into a boolean network. Patterns with stationary averages indicate the maintenance of status quo, whereas discordant patterns represent aggregation of new agent into the cluster or departure from the former policies. The changing patterns can be embedded into a network representation, particularly using the concept of autocatalytic boolean networks. As a case study, the economic tendencies of the BRIC countries + Argentina were studied. Although Argentina is not included in the cluster formed by BRIC countries, it tends to follow the BRIC members because of strong commercial ties.
Algorithms for Finding Small Attractors in Boolean Networks
Hayashida Morihiro
2007-01-01
Full Text Available A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.
Comparison of Two Neural Networks Approaches to Boolean Matrix Factorization
Polyakov, P.Y.; Frolov, A. A.; Húsek, Dušan
Los Alamitos: IEEE Computer Society, 2009 - (Snášel, V.; Pokorný, J.; Pichappan, P.; El-Qawasmeh, E.), s. 316-321 ISBN 978-1-4244-4614-8. [NDT 2009. International Conference on Networked Digital Technologies /1./. Ostrava (CZ), 29.07.2009-31.07.2009] R&D Projects: GA ČR GA205/09/1079; GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : data mining * artificial inteligence * neural networks * multivariate statistics * Boolean factor analysis * Hopfield-like neural networks * feed forward neural network Subject RIV: BB - Applied Statistics, Operational Research
Self-organized networks of competing boolean agents
Paczuski; Bassler; Corral
2000-04-01
A model of Boolean agents competing in a market is presented where each agent bases his action on information obtained from a small group of other agents. The agents play a competitive game that rewards those in the minority. After a long time interval, the poorest player's strategy is changed randomly, and the process is repeated. Eventually the network evolves to a stationary but intermittent state where random mutation of the worst strategy can change the behavior of the entire network, often causing a switch in the dynamics between attractors of vastly different lengths. PMID:11019043
Neural Network Based Boolean Factor Analysis of Parliament Voting
Frolov, A. A.; Polyakov, P.Y.; Húsek, Dušan; Řezanková, H.
Heidelberg : Springer, 2006 - (Rizzi, A.; Vichi, M.), s. 861-868 ISBN 3-7908-1708-2. [COMPSTAT 2006. Symposium /17./. Rome (IN), 28.08.2006-01.09.2006] R&D Projects: GA AV ČR 1ET100300419; GA ČR GA201/05/0079 Grant ostatní: RFBR(RU) 05-07-90049 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * neural networks * social networks Subject RIV: BB - Applied Statistics, Operational Research
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.
Harmonic Analysis of Boolean Networks: Determinative Power and Perturbations
Heckel, Reinhard; Bossert, Martin
2011-01-01
Consider a large Boolean network with a feed forward structure. Given a probability distribution for the inputs, can one find-possibly small-collections of input nodes that determine the states of most other nodes in the network? To identify these nodes, a notion that quantifies the determinative power of an input over states in the network is needed. We argue that the mutual information (MI) between a subset of the inputs X = {X_1, ..., X_n} of node i and the function f_i(X)$ associated with node i quantifies the determinative power of this subset of inputs over node i. To study the relation of determinative power to sensitivity to perturbations, we relate the MI to measures of perturbations, such as the influence of a variable, in terms of inequalities. The result shows that, maybe surprisingly, an input that has large influence does not necessarily have large determinative power. The main tool for the analysis is Fourier analysis of Boolean functions. Whether a function is sensitive to perturbations or not...
Evolution of a designless nanoparticle network into reconfigurable Boolean logic
Bose, S. K.; Lawrence, C. P.; Liu, Z.; Makarenko, K. S.; van Damme, R. M. J.; Broersma, H. J.; van der Wiel, W. G.
2015-12-01
Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.
Evolution of a designless nanoparticle network into reconfigurable Boolean logic.
Bose, S K; Lawrence, C P; Liu, Z; Makarenko, K S; van Damme, R M J; Broersma, H J; van der Wiel, W G
2015-12-01
Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures. PMID:26389658
Evolution and Controllability of Cancer Networks: A Boolean Perspective.
Srihari, Sriganesh; Raman, Venkatesh; Leong, Hon Wai; Ragan, Mark A
2014-01-01
Cancer forms a robust system capable of maintaining stable functioning (cell sustenance and proliferation) despite perturbations. Cancer progresses as stages over time typically with increasing aggressiveness and worsening prognosis. Characterizing these stages and identifying the genes driving transitions between them is critical to understand cancer progression and to develop effective anti-cancer therapies. In this work, we propose a novel model for the `cancer system' as a Boolean state space in which a Boolean network, built from protein-interaction and gene-expression data from different stages of cancer, transits between Boolean satisfiability states by "editing" interactions and "flipping" genes. Edits reflect rewiring of the PPI network while flipping of genes reflect activation or silencing of genes between stages. We formulate a minimization problem min flip to identify these genes driving the transitions. The application of our model (called BoolSpace) on three case studies-pancreatic and breast tumours in human and post spinal-cord injury (SCI) in rats-reveals valuable insights into the phenomenon of cancer progression: (i) interactions involved in core cell-cycle and DNA-damage repair pathways are significantly rewired in tumours, indicating significant impact to key genome-stabilizing mechanisms; (ii) several of the genes flipped are serine/threonine kinases which act as biological switches, reflecting cellular switching mechanisms between stages; and (iii) different sets of genes are flipped during the initial and final stages indicating a pattern to tumour progression. Based on these results, we hypothesize that robustness of cancer partly stems from "passing of the baton" between genes at different stages-genes from different biological processes and/or cellular components are involved in different stages of tumour progression thereby allowing tumour cells to evade targeted therapy, and therefore an effective therapy should target a "cover set" of
Simulating Quantitative Cellular Responses Using Asynchronous Threshold Boolean Network Ensembles
Shah Imran
2011-07-01
Full Text Available Abstract Background With increasing knowledge about the potential mechanisms underlying cellular functions, it is becoming feasible to predict the response of biological systems to genetic and environmental perturbations. Due to the lack of homogeneity in living tissues it is difficult to estimate the physiological effect of chemicals, including potential toxicity. Here we investigate a biologically motivated model for estimating tissue level responses by aggregating the behavior of a cell population. We assume that the molecular state of individual cells is independently governed by discrete non-deterministic signaling mechanisms. This results in noisy but highly reproducible aggregate level responses that are consistent with experimental data. Results We developed an asynchronous threshold Boolean network simulation algorithm to model signal transduction in a single cell, and then used an ensemble of these models to estimate the aggregate response across a cell population. Using published data, we derived a putative crosstalk network involving growth factors and cytokines - i.e., Epidermal Growth Factor, Insulin, Insulin like Growth Factor Type 1, and Tumor Necrosis Factor α - to describe early signaling events in cell proliferation signal transduction. Reproducibility of the modeling technique across ensembles of Boolean networks representing cell populations is investigated. Furthermore, we compare our simulation results to experimental observations of hepatocytes reported in the literature. Conclusion A systematic analysis of the results following differential stimulation of this model by growth factors and cytokines suggests that: (a using Boolean network ensembles with asynchronous updating provides biologically plausible noisy individual cellular responses with reproducible mean behavior for large cell populations, and (b with sufficient data our model can estimate the response to different concentrations of extracellular ligands. Our
A SAT-based algorithm for finding attractors in synchronous Boolean networks.
Dubrova, Elena; Teslenko, Maxim
2011-01-01
This paper addresses the problem of finding attractors in synchronous Boolean networks. The existing Boolean decision diagram-based algorithms have limited capacity due to the excessive memory requirements of decision diagrams. The simulation-based algorithms can be applied to larger networks, however, they are incomplete. We present an algorithm, which uses a SAT-based bounded model checking to find all attractors in a Boolean network. The efficiency of the presented algorithm is evaluated by analyzing seven networks models of real biological processes, as well as 150,000 randomly generated Boolean networks of sizes between 100 and 7,000. The results show that our approach has a potential to handle an order of magnitude larger models than currently possible. PMID:21778527
Modeling integrated cellular machinery using hybrid Petri-Boolean networks.
Natalie Berestovsky
Full Text Available The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them
Characterizing short-term stability for Boolean networks over any distribution of transfer functions
Seshadhri, C.; Smith, Andrew M.; Vorobeychik, Yevgeniy; Mayo, Jackson R.; Armstrong, Robert C.
2016-07-01
We present a characterization of short-term stability of Kauffman's N K (random) Boolean networks under arbitrary distributions of transfer functions. Given such a Boolean network where each transfer function is drawn from the same distribution, we present a formula that determines whether short-term chaos (damage spreading) will happen. Our main technical tool which enables the formal proof of this formula is the Fourier analysis of Boolean functions, which describes such functions as multilinear polynomials over the inputs. Numerical simulations on mixtures of threshold functions and nested canalyzing functions demonstrate the formula's correctness.
Dynamical modeling of the cholesterol regulatory pathway with Boolean networks
Corcos Laurent
2008-11-01
Full Text Available Abstract Background Qualitative dynamics of small gene regulatory networks have been studied in quite some details both with synchronous and asynchronous analysis. However, both methods have their drawbacks: synchronous analysis leads to spurious attractors and asynchronous analysis lacks computational efficiency, which is a problem to simulate large networks. We addressed this question through the analysis of a major biosynthesis pathway. Indeed the cholesterol synthesis pathway plays a pivotal role in dislypidemia and, ultimately, in cancer through intermediates such as mevalonate, farnesyl pyrophosphate and geranyl geranyl pyrophosphate, but no dynamic model of this pathway has been proposed until now. Results We set up a computational framework to dynamically analyze large biological networks. This framework associates a classical and computationally efficient synchronous Boolean analysis with a newly introduced method based on Markov chains, which identifies spurious cycles among the results of the synchronous simulation. Based on this method, we present here the results of the analysis of the cholesterol biosynthesis pathway and its physiological regulation by the Sterol Response Element Binding Proteins (SREBPs, as well as the modeling of the action of statins, inhibitor drugs, on this pathway. The in silico experiments show the blockade of the cholesterol endogenous synthesis by statins and its regulation by SREPBs, in full agreement with the known biochemical features of the pathway. Conclusion We believe that the method described here to identify spurious cycles opens new routes to compute large and biologically relevant models, thanks to the computational efficiency of synchronous simulation. Furthermore, to the best of our knowledge, we present here the first dynamic systems biology model of the human cholesterol pathway and several of its key regulatory control elements, hoping it would provide a good basis to perform in silico
Propagation of external regulation and asynchronous dynamics in random Boolean networks
Mahmoudi, Hamed; Pagnani, Andrea; Weigt, Martin; Zecchina, Riccardo
2007-01-01
Boolean Networks and their dynamics are of great interest as abstract modeling schemes in various disciplines, ranging from biology to computer science. Whereas parallel update schemes have been studied extensively in past years, the level of understanding of asynchronous updates schemes is still very poor. In this paper we study the propagation of external information given by regulatory input variables into a random Boolean network. We compute both analytically and numerically the time evol...
SAT-based Distributed Reactive Control Protocol Synthesis for Boolean Networks
Sahin, Yunus Emre; Ozay, Necmiye
2016-01-01
This paper considers the synthesis of distributed reactive control protocols for a Boolean network in a distributed manner. We start with a directed acyclic graph representing a network of Boolean subsystems and a global contract, given as an assumption-guarantee pair. Assumption captures the environment behavior, and guarantee is the requirements to be satisfied by the system. Local assumption-guarantee contracts, together with local control protocols ensuring these local contracts, are comp...
Decisional Processes with Boolean Neural Network: the Emergence of Mental Schemes
Barnabei, Graziano; Conversano, Ciro; Lensi, Elena
2010-01-01
Human decisional processes result from the employment of selected quantities of relevant information, generally synthesized from environmental incoming data and stored memories. Their main goal is the production of an appropriate and adaptive response to a cognitive or behavioral task. Different strategies of response production can be adopted, among which haphazard trials, formation of mental schemes and heuristics. In this paper, we propose a model of Boolean neural network that incorporates these strategies by recurring to global optimization strategies during the learning session. The model characterizes as well the passage from an unstructured/chaotic attractor neural network typical of data-driven processes to a faster one, forward-only and representative of schema-driven processes. Moreover, a simplified version of the Iowa Gambling Task (IGT) is introduced in order to test the model. Our results match with experimental data and point out some relevant knowledge coming from psychological domain.
Decisional Processes with Boolean Neural Network: The Emergence of Mental Schemes
Human decisional processes result from the employment of selected quantities of relevant information, generally synthesized from environmental incoming data and stored memories. Their main goal is the production of an appropriate and adaptive response to a cognitive or behavioral task. Different strategies of response production can be adopted, among which haphazard trials, formation of mental schemes and heuristics. In this paper, we propose a model of Boolean neural network that incorporates these strategies by recurring to global optimization strategies during the learning session. The model characterizes as well the passage from an unstructured/chaotic attractor neural network typical of data-driven processes to a faster one, forward-only and representative of schema-driven processes. Moreover, a simplified version of the Iowa Gambling Task (IGT) is introduced in order to test the model. Our results match with experimental data and point out some relevant knowledge coming from psychological domain. (authors)
Polynomial-Time Algorithm for Controllability Test of a Class of Boolean Biological Networks
Koichi Kobayashi
2010-01-01
Full Text Available In recent years, Boolean-network-model-based approaches to dynamical analysis of complex biological networks such as gene regulatory networks have been extensively studied. One of the fundamental problems in control theory of such networks is the problem of determining whether a given substance quantity can be arbitrarily controlled by operating the other substance quantities, which we call the controllability problem. This paper proposes a polynomial-time algorithm for solving this problem. Although the algorithm is based on a sufficient condition for controllability, it is easily computable for a wider class of large-scale biological networks compared with the existing approaches. A key to this success in our approach is to give up computing Boolean operations in a rigorous way and to exploit an adjacency matrix of a directed graph induced by a Boolean network. By applying the proposed approach to a neurotransmitter signaling pathway, it is shown that it is effective.
Relative stability of network states in Boolean network models of gene regulation in development.
Zhou, Joseph Xu; Samal, Areejit; d'Hérouël, Aymeric Fouquier; Price, Nathan D; Huang, Sui
2016-01-01
Progress in cell type reprogramming has revived the interest in Waddington's concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington's landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols. PMID:26965665
Autonomous Modeling, Statistical Complexity and Semi-annealed Treatment of Boolean Networks
Gong, Xinwei
This dissertation presents three studies on Boolean networks. Boolean networks are a class of mathematical systems consisting of interacting elements with binary state variables. Each element is a node with a Boolean logic gate, and the presence of interactions between any two nodes is represented by directed links. Boolean networks that implement the logic structures of real systems are studied as coarse-grained models of the real systems. Large random Boolean networks are studied with mean field approximations and used to provide a baseline of possible behaviors of large real systems. This dissertation presents one study of the former type, concerning the stable oscillation of a yeast cell-cycle oscillator, and two studies of the latter type, respectively concerning the statistical complexity of large random Boolean networks and an extension of traditional mean field techniques that accounts for the presence of short loops. In the cell-cycle oscillator study, a novel autonomous update scheme is introduced to study the stability of oscillations in small networks. A motif that corrects pulse-growing perturbations and a motif that grows pulses are identified. A combination of the two motifs is capable of sustaining stable oscillations. Examining a Boolean model of the yeast cell-cycle oscillator using an autonomous update scheme yields evidence that it is endowed with such a combination. Random Boolean networks are classified as ordered, critical or disordered based on their response to small perturbations. In the second study, random Boolean networks are taken as prototypical cases for the evaluation of two measures of complexity based on a criterion for optimal statistical prediction. One measure, defined for homogeneous systems, does not distinguish between the static spatial inhomogeneity in the ordered phase and the dynamical inhomogeneity in the disordered phase. A modification in which complexities of individual nodes are calculated yields vanishing
Mapping Complex Networks: Exploring Boolean Modeling of Signal Transduction Pathways
Bhardwaj, Gaurav; Wells, Christine P.; Albert, Reka; van Rossum, Damian B.; Patterson, Randen L
2009-01-01
In this study, we explored the utility of a descriptive and predictive bionetwork model for phospholipase C-coupled calcium signaling pathways, built with non-kinetic experimental information. Boolean models generated from these data yield oscillatory activity patterns for both the endoplasmic reticulum resident inositol-1,4,5-trisphosphate receptor (IP3R) and the plasma-membrane resident canonical transient receptor potential channel 3 (TRPC3). These results are specific as randomization of ...
Learning and Unlearning in Hopfield-Like Neural Network Performing Boolean Factor Analysis
Frolov, A. A.; Húsek, Dušan; Muraviev, I. P.; Polyakov, P.Y.
Berlin : Springer, 2010 - (Koronacki, J.; Ras, Z.; Wierzchon, S.; Kacprzyk, J.), s. 501-518 ISBN 978-3-642-05176-0. - (Studies in Computational Intelligence. 262) Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * Hopfield-like neural network * spurious attractors * statistics * bingy data Subject RIV: IN - Informatics, Computer Science
Analysis and control of Boolean networks a semi-tensor product approach
Cheng, Daizhan; Li, Zhiqiang
2010-01-01
This book presents a new approach to the investigation of Boolean control networks, using the semi-tensor product (STP), which can express a logical function as a conventional discrete-time linear system. This makes it possible to analyze basic control problems.
Reverse engineering Boolean networks: from Bernoulli mixture models to rule based systems.
Mehreen Saeed
Full Text Available A Boolean network is a graphical model for representing and analyzing the behavior of gene regulatory networks (GRN. In this context, the accurate and efficient reconstruction of a Boolean network is essential for understanding the gene regulation mechanism and the complex relations that exist therein. In this paper we introduce an elegant and efficient algorithm for the reverse engineering of Boolean networks from a time series of multivariate binary data corresponding to gene expression data. We call our method ReBMM, i.e., reverse engineering based on Bernoulli mixture models. The time complexity of most of the existing reverse engineering techniques is quite high and depends upon the indegree of a node in the network. Due to the high complexity of these methods, they can only be applied to sparsely connected networks of small sizes. ReBMM has a time complexity factor, which is independent of the indegree of a node and is quadratic in the number of nodes in the network, a big improvement over other techniques and yet there is little or no compromise in accuracy. We have tested ReBMM on a number of artificial datasets along with simulated data derived from a plant signaling network. We also used this method to reconstruct a network from real experimental observations of microarray data of the yeast cell cycle. Our method provides a natural framework for generating rules from a probabilistic model. It is simple, intuitive and illustrates excellent empirical results.
From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits
Bin Shao; Xiang Liu; Dongliang Zhang; Jiayi Wu; Qi Ouyang
2015-01-01
Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, ...
Exploring phospholipase C-coupled Ca(2+) signalling networks using Boolean modelling.
Bhardwaj, G; Wells, C P; Albert, R; van Rossum, D B; Patterson, R L
2011-05-01
In this study, the authors explored the utility of a descriptive and predictive bionetwork model for phospholipase C-coupled calcium signalling pathways, built with non-kinetic experimental information. Boolean models generated from these data yield oscillatory activity patterns for both the endoplasmic reticulum resident inositol-1,4,5-trisphosphate receptor (IP(3)R) and the plasma-membrane resident canonical transient receptor potential channel 3 (TRPC3). These results are specific as randomisation of the Boolean operators ablates oscillatory pattern formation. Furthermore, knock-out simulations of the IP(3)R, TRPC3 and multiple other proteins recapitulate experimentally derived results. The potential of this approach can be observed by its ability to predict previously undescribed cellular phenotypes using in vitro experimental data. Indeed, our cellular analysis of the developmental and calcium-regulatory protein, DANGER1a, confirms the counter-intuitive predictions from our Boolean models in two highly relevant cellular models. Based on these results, the authors theorise that with sufficient legacy knowledge and/or computational biology predictions, Boolean networks can provide a robust method for predictive modelling of any biological system. [Includes supplementary material]. PMID:21639591
Detecting small attractors of large Boolean networks by function-reduction-based strategy.
Zheng, Qiben; Shen, Liangzhong; Shang, Xuequn; Liu, Wenbin
2016-04-01
Boolean networks (BNs) are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long-term behaviour of systems. A central aim of Boolean-network analysis is to find attractors that correspond to various cellular states, such as cell types or the stage of cell differentiation. This problem is NP-hard and various algorithms have been used to tackle it with considerable success. The idea is that a singleton attractor corresponds to n consistent subsequences in the truth table. To find these subsequences, the authors gradually reduce the entire truth table of Boolean functions by extending a partial gene activity profile (GAP). Not only does this process delete inconsistent subsequences in truth tables, it also directly determines values for some nodes not extended, which means it can abandon the partial GAPs that cannot lead to an attractor as early as possible. The results of simulation show that the proposed algorithm can detect small attractors with length p = 4 in BNs of up to 200 nodes with average indegree K = 2. PMID:26997659
Random Boolean Networks and Attractors of their Intersecting Circuits
Demongeot, Jacques; Elena, Adrien; Noual, Mathilde; Sené, Sylvain
2011-01-01
International audience The multi-scale strategy in studying biological regulatory networks analysis is based on two level of analysis. The first level is structural and consists in examining the architecture of the interaction graph underlying the network and the second level is functional and analyse the regulatory properties of the network. We apply this dual approach to the "immunetworks" involved in the control of the immune system. As a result, we show that the small number of attract...
Dynamical modeling of the cholesterol regulatory pathway with Boolean networks
Corcos Laurent; Kervizic Gwenael
2008-01-01
Abstract Background Qualitative dynamics of small gene regulatory networks have been studied in quite some details both with synchronous and asynchronous analysis. However, both methods have their drawbacks: synchronous analysis leads to spurious attractors and asynchronous analysis lacks computational efficiency, which is a problem to simulate large networks. We addressed this question through the analysis of a major biosynthesis pathway. Indeed the cholesterol synthesis pathway plays a pivo...
Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean Networks
Wai-Ki Ching
2008-09-01
Full Text Available A Boolean network (BN is a mathematical model of genetic networks. We propose several algorithms for control of singleton attractors in BN. We theoretically estimate the average-case time complexities of the proposed algorithms, and confirm them by computer experiments. The results suggest the importance of gene ordering. Especially, setting internal nodes ahead yields shorter computational time than setting external nodes ahead in various types of algorithms. We also present a heuristic algorithm which does not look for the optimal solution but for the solution whose computational time is shorter than that of the exact algorithms.
Claudia Stötzel
Full Text Available In this paper, we present a systematic transition scheme for a large class of ordinary differential equations (ODEs into Boolean networks. Our transition scheme can be applied to any system of ODEs whose right hand sides can be written as sums and products of monotone functions. It performs an Euler-like step which uses the signs of the right hand sides to obtain the Boolean update functions for every variable of the corresponding discrete model. The discrete model can, on one hand, be considered as another representation of the biological system or, alternatively, it can be used to further the analysis of the original ODE model. Since the generic transformation method does not guarantee any property conservation, a subsequent validation step is required. Depending on the purpose of the model this step can be based on experimental data or ODE simulations and characteristics. Analysis of the resulting Boolean model, both on its own and in comparison with the ODE model, then allows to investigate system properties not accessible in a purely continuous setting. The method is exemplarily applied to a previously published model of the bovine estrous cycle, which leads to new insights regarding the regulation among the components, and also indicates strongly that the system is tailored to generate stable oscillations.
HSP70 mediates survival in apoptotic cells – Boolean network prediction and experimental validation
Suhas Vasaikar
2015-08-01
Full Text Available Neuronal stress or injury results in the activation of proteins, which regulate the balance between survival and apoptosis. However, the complex mechanism of cell signalling involving cell death and survival, activated in response to cellular stress is not yet completely understood. To bring more clarity about these mechanisms, a Boolean network was constructed that represented the apoptotic pathway in neuronal cells. FasL and neurotrophic growth factor (NGF were considered as inputs in the absence and presence of heat shock proteins known to shift the balance towards survival by rescuing pro-apoptotic cells. The probabilities of survival, DNA repair and apoptosis as cellular fates, in the presence of either the growth factor or FasL, revealed a survival bias encoded in the network. Boolean predictions tested by measuring the mRNA expression level of caspase-3, caspase-8 and BAX in neuronal Neuro2a (N2a cell line with NGF and FasL as external input, showed positive correlation with the observed experimental results for survival and apoptotic states. It was observed that HSP70 contributed more towards rescuing cells from apoptosis in comparison to HSP27, HSP40 and HSP90. Overexpression of HSP70 in N2a transfected cells showed reversal of cellular fate from FasL-induced apoptosis to survival. Further, the pro-survival role of the proteins BCL2, IAP, cFLIP and NFκB determined by vertex perturbation analysis was experimentally validated through protein inhibition experiments using EM20-25, Embelin and Wedelolactone, which resulted in 1.27-fold, 1.26-fold and 1.46-fold increase in apoptosis of N2a cells. The existence of a one-to-one correspondence between cellular fates and attractor states shows that Boolean networks may be employed with confidence in qualitative analytical studies of biological networks.
Critical line in undirected Kauffman Boolean networks - the role of percolation
We show that to describe correctly the position of the critical line in Kauffman random Boolean networks one must take into account percolation phenomena underlying the process of damage spreading. For this reason, since the issue of percolation transition is much simpler in random undirected networks than in the directed ones, we study the Kauffman model in undirected networks. We derive the mean field formula for the critical line in the giant components of these networks, and show that the critical line characterizing the whole network results from the fact that the ordered behavior of small clusters shields the chaotic behavior of the giant component. We also show a possible attitude towards the analytical description of the shielding effect. The theoretical derivations given in this paper very much tally with the numerical simulations done for classical random graphs
The behavior of noise-resilient Boolean networks with diverse topologies
The dynamics of noise-resilient Boolean networks with majority functions and diverse topologies is investigated. A wide class of possible topological configurations is parametrized as a stochastic blockmodel. For this class of networks, the dynamics always undergoes a phase transition from a non-ergodic regime, where a memory of its past states is preserved, to an ergodic regime, where no such memory exists and every microstate is equally probable. Both the average error on the network and the critical value of noise where the transition occurs are investigated analytically, and compared to numerical simulations. The results for 'partially dense' networks, comprising relatively few, but dynamically important nodes, which have a number of inputs that greatly exceeds the average for the entire network, give very general upper bounds on the maximum resilience against noise attainable on globally sparse systems
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.
Variances as order parameter and complexity measure for random Boolean networks
Luque, Bartolo [Departamento de Matematica Aplicada y EstadIstica, Escuela Superior de Ingenieros Aeronauticos, Universidad Politecnica de Madrid, Plaza Cardenal Cisneros 3, Madrid 28040 (Spain); Ballesteros, Fernando J [Observatori Astronomic, Universitat de Valencia, Ed. Instituts d' Investigacio, Pol. La Coma s/n, E-46980 Paterna, Valencia (Spain); Fernandez, Manuel [Departamento de Matematica Aplicada y EstadIstica, Escuela Superior de Ingenieros Aeronauticos, Universidad Politecnica de Madrid, Plaza Cardenal Cisneros 3, Madrid 28040 (Spain)
2005-02-04
Several order parameters have been considered to predict and characterize the transition between ordered and disordered phases in random Boolean networks, such as the Hamming distance between replicas or the stable core, which have been successfully used. In this work, we propose a natural and clear new order parameter: the temporal variance. We compute its value analytically and compare it with the results of numerical experiments. Finally, we propose a complexity measure based on the compromise between temporal and spatial variances. This new order parameter and its related complexity measure can be easily applied to other complex systems.
The receptor mosaic hypothesis of the engram: possible relevance of Boolean network modeling.
Zoli, M; Guidolin, D; Fuxe, K; Agnati, L F
1996-09-01
In the past 15 years, several lines of evidence have shown that receptors for chemical signals can interact in domains of the plasma membrane and possibly form molecular circuits encoding logical operators. In this frame, the receptor mosaic hypothesis of the engram was advanced. According to this proposal, aggregates of different receptor species (mosaics) may form in neuronal membranes (typically synapses) and constitute a memory trace (engram) of its activity. In the present paper, we present an attempt to model the functioning of aggregates of interacting receptors in membrane domains by means of random Boolean networks. PMID:8968825
Húsek, Dušan; Frolov, A. A.; Polyakov, P.Y.; Řezanková, H.; Snášel, V.
Lisabon : Instituto Nacional de Estatística, 2008 - (Gomes, M.; Pinto Martins, J.; Silva, J.), s. 3739-3742 ISBN 978-972-673-992-0. [ISI 2007. Session of the International Statistical Institute /56./. Lisboa (PT), 22.08.2007-29.08.2007] R&D Projects: GA AV ČR 1ET100300414 Grant ostatní: RFBR(RU) 05-07-90049 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * document classification * automatic concepts search * unsupervised learning * neural network Subject RIV: BB - Applied Statistics, Operational Research
Neural Network Based Boolean Factor Analysis: Efficient Tool for Automated Topics Search.
Húsek, Dušan; Frolov, A. A.; Polyakov, P.Y.; Řezanková, H.
Amman: Applied Science Private University, 2006 - (Issa, G.; El-Qawasmeh, E.; Raho, G.), s. 321-327 ISBN 9957-8592-0-X. [CSIT 2006. International Multiconference on Computer Science and Information Technology /4./. Amman (JO), 05.04.2006-07.04.2006] R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * neural networks * associative memory * clustering * web searching * semantic web * information retrieval * document indexing * document classification * document processing * data mining * machine learning Subject RIV: BB - Applied Statistics, Operational Research
Damage spreading in spatial and small-world random Boolean networks
Lu, Qiming; Teuscher, Christof
2014-02-01
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 as 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 nonrandom 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 Hamming distance at very low connectivities (K¯≪1) and that the critical connectivity of stability Ks changes compared to random networks. At higher K¯, this scaling remains unchanged. We also show that the Hamming distance 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 arguments for small-world networks are obtained with respect to the system sizes and strength of spatially local connections. 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.
高阶布尔网络的结构%Structure of higher order Boolean networks*
李志强; 赵寅; 程代展
2011-01-01
The higher order Boolean (control) network is introduced and its topological structure is studied.Using semi-tensor product of matrices,its dynamics is converted into two algebraic forms,which are standard discrete-time dynamic systems.The one-to-one correspondence of the network dynamics and its first algebraic form is proved,and certain topological structures,including fixed points,cycles,and transient time,of higher order Boolean (control) networks are revealed.The relationship between the original system and its second algebraic form is also studied.%介绍高阶布尔（控制）网络,并研究了其拓扑结构.以矩阵的半张量积作为工具,把高阶布尔网络的动态过程转化为2种标准离散事件动态系统的代数形式.证明了高阶布尔网络和第1代数形式的一一对应关系,并由此得到其拓扑结构（不动点、极限圈以及暂态期等）.还研究了高阶布尔网络系统与它第2代数形式的关系.
Diffusion Adaptation over Networks
Sayed, Ali H
2012-01-01
Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with processing and learning abilities. The agents are linked together through a connection topology, and they cooperate with each other through local interactions to solve distributed inference problems in real-time. The continuous diffusion of information across the network enables agents to adapt their performance in relation to changing data and network conditions; it also results in improved adaptation and learning performance relative to non-cooperative networks. This article provides an overview of diffusion strategies for adaptation and learning over networks. The article is divided into several sections: 1. Motivation; 2. Mean-Square-Error Estimation; 3. Distributed Optimization via Diffusion Strategies; 4. Adaptive Diffusion Strategies; 5. Performance of Ste...
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.
Modeling and controlling the two-phase dynamics of the p53 network: a Boolean network approach
Lin, Guo-Qiang; Ao, Bin; Chen, Jia-Wei; Wang, Wen-Xu; Di, Zeng-Ru
2014-12-01
Although much empirical evidence has demonstrated that p53 plays a key role in tumor suppression, the dynamics and function of the regulatory network centered on p53 have not yet been fully understood. Here, we develop a Boolean network model to reproduce the two-phase dynamics of the p53 network in response to DNA damage. In particular, we map the fates of cells into two types of Boolean attractors, and we find that the apoptosis attractor does not exist for minor DNA damage, reflecting that the cell is reparable. As the amount of DNA damage increases, the basin of the repair attractor shrinks, accompanied by the rising of the apoptosis attractor and the expansion of its basin, indicating that the cell becomes more irreparable with more DNA damage. For severe DNA damage, the repair attractor vanishes, and the apoptosis attractor dominates the state space, accounting for the exclusive fate of death. Based on the Boolean network model, we explore the significance of links, in terms of the sensitivity of the two-phase dynamics, to perturbing the weights of links and removing them. We find that the links are either critical or ordinary, rather than redundant. This implies that the p53 network is irreducible, but tolerant of small mutations at some ordinary links, and this can be interpreted with evolutionary theory. We further devised practical control schemes for steering the system into the apoptosis attractor in the presence of DNA damage by pinning the state of a single node or perturbing the weight of a single link. Our approach offers insights into understanding and controlling the p53 network, which is of paramount importance for medical treatment and genetic engineering.
Modeling and controlling the two-phase dynamics of the p53 network: a Boolean network approach
Although much empirical evidence has demonstrated that p53 plays a key role in tumor suppression, the dynamics and function of the regulatory network centered on p53 have not yet been fully understood. Here, we develop a Boolean network model to reproduce the two-phase dynamics of the p53 network in response to DNA damage. In particular, we map the fates of cells into two types of Boolean attractors, and we find that the apoptosis attractor does not exist for minor DNA damage, reflecting that the cell is reparable. As the amount of DNA damage increases, the basin of the repair attractor shrinks, accompanied by the rising of the apoptosis attractor and the expansion of its basin, indicating that the cell becomes more irreparable with more DNA damage. For severe DNA damage, the repair attractor vanishes, and the apoptosis attractor dominates the state space, accounting for the exclusive fate of death. Based on the Boolean network model, we explore the significance of links, in terms of the sensitivity of the two-phase dynamics, to perturbing the weights of links and removing them. We find that the links are either critical or ordinary, rather than redundant. This implies that the p53 network is irreducible, but tolerant of small mutations at some ordinary links, and this can be interpreted with evolutionary theory. We further devised practical control schemes for steering the system into the apoptosis attractor in the presence of DNA damage by pinning the state of a single node or perturbing the weight of a single link. Our approach offers insights into understanding and controlling the p53 network, which is of paramount importance for medical treatment and genetic engineering. (paper)
Attractor Neural Network Combined with Likelihood Maximization Algorithm for Boolean Factor Analysis
Frolov, A.; Húsek, Dušan; Polyakov, P.Y.
Vol. 1. Berlin: Springer, 2012 - (Wang, J.; Yen, G.; Polycarpou, M.), s. 1-10. (Lecture Notes in Computer Science. 7367). ISBN 978-3-642-31345-5. ISSN 0302-9743. [ISNN 2012. International Symposium on Neural Networks /9./. Shenyang (CN), 11.07.2012-14.07.2012] R&D Projects: GA ČR GAP202/10/0262 Grant ostatní: GA MŠk(CZ) ED1.1.00/02.0070 Institutional research plan: CEZ:AV0Z10300504 Keywords : Associative Neural Network * Likelihood Maximization * Boolean Factor Analysis * Binary Matrix factorization * Noise XOR Mixing * Plato Problem * Information Gain * Bars problem * Data Mining * Dimension Reduction * Hebbian Learning * Anti-Hebbian Learning Subject RIV: IN - Informatics, Computer Science
Chaudhuri, Arijit
2014-01-01
Combining the two statistical techniques of network sampling and adaptive sampling, this book illustrates the advantages of using them in tandem to effectively capture sparsely located elements in unknown pockets. It shows how network sampling is a reliable guide in capturing inaccessible entities through linked auxiliaries. The text also explores how adaptive sampling is strengthened in information content through subsidiary sampling with devices to mitigate unmanageable expanding sample sizes. Empirical data illustrates the applicability of both methods.
Yih-Lon Lin
2013-01-01
Full Text Available If the given Boolean function is linearly separable, a robust uncoupled cellular neural network can be designed as a maximal margin classifier. On the other hand, if the given Boolean function is linearly separable but has a small geometric margin or it is not linearly separable, a popular approach is to find a sequence of robust uncoupled cellular neural networks implementing the given Boolean function. In the past research works using this approach, the control template parameters and thresholds are restricted to assume only a given finite set of integers, and this is certainly unnecessary for the template design. In this study, we try to remove this restriction. Minterm- and maxterm-based decomposition algorithms utilizing the soft margin and maximal margin support vector classifiers are proposed to design a sequence of robust templates implementing an arbitrary Boolean function. Several illustrative examples are simulated to demonstrate the efficiency of the proposed method by comparing our results with those produced by other decomposition methods with restricted weights.
Adaptive network countermeasures.
McClelland-Bane, Randy; Van Randwyk, Jamie A.; Carathimas, Anthony G.; Thomas, Eric D.
2003-10-01
This report describes the results of a two-year LDRD funded by the Differentiating Technologies investment area. The project investigated the use of countermeasures in protecting computer networks as well as how current countermeasures could be changed in order to adapt with both evolving networks and evolving attackers. The work involved collaboration between Sandia employees and students in the Sandia - California Center for Cyber Defenders (CCD) program. We include an explanation of the need for adaptive countermeasures, a description of the architecture we designed to provide adaptive countermeasures, and evaluations of the system.
Zhu, Zheng; Andresen, Juan Carlos; Moore, M A; Katzgraber, Helmut G
2014-02-01
We study the equilibrium and nonequilibrium properties of Boolean decision problems with competing interactions on scale-free networks in an external bias (magnetic field). Previous studies at zero field have shown a remarkable equilibrium stability of Boolean variables (Ising spins) with competing interactions (spin glasses) on scale-free networks. When the exponent that describes the power-law decay of the connectivity of the network is strictly larger than 3, the system undergoes a spin-glass transition. However, when the exponent is equal to or less than 3, the glass phase is stable for all temperatures. First, we perform finite-temperature Monte Carlo simulations in a field to test the robustness of the spin-glass phase and show that the system has a spin-glass phase in a field, i.e., exhibits a de Almeida-Thouless line. Furthermore, we study avalanche distributions when the system is driven by a field at zero temperature to test if the system displays self-organized criticality. Numerical results suggest that avalanches (damage) can spread across the whole system with nonzero probability when the decay exponent of the interaction degree is less than or equal to 2, i.e., that Boolean decision problems on scale-free networks with competing interactions can be fragile when not in thermal equilibrium. PMID:25353433
Detecting a Singleton Attractor in a Boolean Network Utilizing SAT Algorithms
Tamura, Takeyuki; Akutsu, Tatsuya
The Boolean network (BN) is a mathematical model of genetic networks. It is known that detecting a singleton attractor, which is also called a fixed point, is NP-hard even for AND/OR BNs (i.e., BNs consisting of AND/OR nodes), where singleton attractors correspond to steady states. Though a naive algorithm can detect a singleton attractor for an AND/OR BN in O(n 2n) time, no O((2-ε)n) (ε > 0) time algorithm was known even for an AND/OR BN with non-restricted indegree, where n is the number of nodes in a BN. In this paper, we present an O(1.787n) time algorithm for detecting a singleton attractor of a given AND/OR BN, along with related results. We also show that detection of a singleton attractor in a BN with maximum indegree two is NP-hard and can be polynomially reduced to a satisfiability problem.
Frolov, A. A.; Húsek, Dušan; Muraviev, I. P.; Polyakov, P.Y.
2010-01-01
Roč. 73, č. 7-9 (2010), s. 1394-1404. ISSN 0925-2312 R&D Projects: GA ČR GA205/09/1079; GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * Hopfield neural Network * unsupervised learning * dimension reduction * data mining Subject RIV: IN - Informatics, Computer Science Impact factor: 1.429, year: 2010
Computational complexity of Boolean functions
Boolean functions are among the fundamental objects of discrete mathematics, especially in those of its subdisciplines which fall under mathematical logic and mathematical cybernetics. The language of Boolean functions is convenient for describing the operation of many discrete systems such as contact networks, Boolean circuits, branching programs, and some others. An important parameter of discrete systems of this kind is their complexity. This characteristic has been actively investigated starting from Shannon's works. There is a large body of scientific literature presenting many fundamental results. The purpose of this survey is to give an account of the main results over the last sixty years related to the complexity of computation (realization) of Boolean functions by contact networks, Boolean circuits, and Boolean circuits without branching. Bibliography: 165 titles.
Parameter Learning of Boolean Bayesian Networks%布尔型贝叶斯网络参数学习
吴永广; 周兴旺
2015-01-01
布尔型贝叶斯网络是一类由布尔型变量组成的网络，它能够以线性多变量函数描述，使计算和处理上灵活高效。通过运用连接树算法对络进行分块化处理的方法，可以提高算法的效率，然后以传统的最大似然估计方法对布尔型网络的参数进行学习。服从同一分布律的贝叶斯网络参数学习算法发展比较成熟，这类以狄利克雷或者高斯分布为基础的算法在应用领域中难以发挥其应有的价值。相比之下，基于布尔型贝叶斯网络下的参数学习更贴近于应用，在人工智能和数据挖掘等领域有很好的发展前景。%Boolean Bayesian network is a class of Bayesian networks which are made up of Boolean varia-bles. The method to describe the network with a multi-linear function is flexible and efficient to compute and process variables. By introducing Junction Tree algorithm,the network can be divided into blocks which can make it easy to calculate. Then the traditional maximum likelihood estimation method was used for learning Boolean networks. Parameter learning algorithm following the same distribution is more ma-ture,but this kind of algorithm based on Dirichlet or Gaussian distribution is difficult to play its proper val-ue in practice. In contrast,parameter learning based on Boolean networks gets close to applications. It has good prospects for development in areas such as artificial intelligence and data mining.
Adaptive Dynamic Bayesian Networks
Ng, B M
2007-10-26
A discrete-time Markov process can be compactly modeled as a dynamic Bayesian network (DBN)--a graphical model with nodes representing random variables and directed edges indicating causality between variables. Each node has a probability distribution, conditional on the variables represented by the parent nodes. A DBN's graphical structure encodes fixed conditional dependencies between variables. But in real-world systems, conditional dependencies between variables may be unknown a priori or may vary over time. Model errors can result if the DBN fails to capture all possible interactions between variables. Thus, we explore the representational framework of adaptive DBNs, whose structure and parameters can change from one time step to the next: a distribution's parameters and its set of conditional variables are dynamic. This work builds on recent work in nonparametric Bayesian modeling, such as hierarchical Dirichlet processes, infinite-state hidden Markov networks and structured priors for Bayes net learning. In this paper, we will explain the motivation for our interest in adaptive DBNs, show how popular nonparametric methods are combined to formulate the foundations for adaptive DBNs, and present preliminary results.
Húsek, Dušan; Moravec, P.; Snášel, V.; Frolov, A.; Řezanková, H.; Polyakov, P.Y.
Berlin : Springer, 2007 - (Ghosh, A.; De, R.), s. 235-243 ISBN 978-3-540-77045-9. - (Lecture Notes in Computer Science. 4815). [PReMI 2007. International Conference /2./. Kolkata (IN), 18.12.2007-22.12.2007] R&D Projects: GA MŠk(CZ) 1M0567; GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * neural network * dimension reduction * cluster analysis Subject RIV: BB - Applied Statistics, Operational Research
Boolean implication networks derived from large scale, whole genome microarray datasets
Sahoo, Debashis; Dill, David L.; Gentles, Andrew J.; Tibshirani, Robert; Plevritis, Sylvia K.
2008-01-01
We describe a method for extracting Boolean implications (if-then relationships) in very large amounts of gene expression microarray data. A meta-analysis of data from thousands of microarrays for humans, mice, and fruit flies finds millions of implication relationships between genes that would be missed by other methods. These relationships capture gender differences, tissue differences, development, and differentiation. New relationships are discovered that are preserved across all three sp...
Generalized Adaptive Artificial Neural Networks
Tawel, Raoul
1993-01-01
Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.
Boolean Differential Operators
Catumba, Jorge; Diaz, Rafael
2012-01-01
We consider four combinatorial interpretations for the algebra of Boolean differential operators. We show that each interpretation yields an explicit matrix representation for Boolean differential operators.
Memory-Based Boolean Game and Self-Organized Phenomena on Networks
HUANG Zi-Gang; WU Zhi-Xi; GUAN Jian-Yue; WANG Ying-Hai
2006-01-01
@@ We study a memory-based Boolean game (MBBG) taking place on a regular ring, wherein each agent acts according to its local optimal states of the last M time steps recorded in memory, and the agents in the minority are rewarded. One free parameter p between 0 and 1 is introduced to denote the strength of the agent willing to make a decision according to its memory. It is found that giving proper willing strength p, the MBBG system can spontaneously evolve to a state of performance better than the random game; while for larger p, the herd behaviour emerges to reduce the system profit. By analysing the dependence of dynamics of the system on the memory capacity M, we find that a higher memory capacity favours the emergence of the better performance state, and effectively restrains the herd behaviour, thus increases the system profit. Considering the high cost of long-time memory, the enhancement of memory capacity for restraining the herd behaviour is also discussed,and M = 5 is suggested to be a good choice.
Computation emerges from adaptive synchronization of networking neurons.
Zanin, Massimiliano; Del Pozo, Francisco; Boccaletti, Stefano
2011-01-01
The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress) of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain. PMID:22073167
Computation emerges from adaptive synchronization of networking neurons.
Massimiliano Zanin
Full Text Available The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain.
Combinatorics of Boolean automata circuits dynamics
Demongeot, Jacques; Noual, Mathilde; Sené, Sylvain
2012-01-01
International audience In line with fields of theoretical computer science and biology that study Boolean automata networks to model regulation networks, we present some results concerning the dynamics of networks whose underlying structures are oriented cycles, that is, Boolean automata circuits. In the context of biological regulation, former studies have highlighted the importance of circuits on the asymptotic dynamical behaviour of the biological networks that contain them. Our work fo...
Investigating Boolean Matrix Factorization
Snášel, V.; Platoš, J.; Krömer, P.; Húsek, Dušan; Neruda, Roman; Frolov, A. A.
- : ACM, 2008 - (Ding, C.; Li, T.; Zhu, S.), s. 18-25 ISBN 978-1-60558-307-5. [DMMT'08. Workshop in Conjunction with SIGKDD 2008 /14./. Las Vegas (US), 24.08.2008-24.08.2008] Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * nonnegative matrix factorization * neural networks * information retrieval * data mining * binary data Subject RIV: BB - Applied Statistics, Operational Research http://users.cs.fiu.edu/~taoli/kdd08-workshop/DMMT08-Proceedings.pdf
Decoupled Adapt-then-Combine diffusion networks with adaptive combiners
Fernandez-Bes, Jesus; Arenas-García, Jerónimo; Silva, Magno T. M.; Azpicueta-Ruiz, Luis A.
2015-01-01
In this paper we analyze a novel diffusion strategy for adaptive networks called Decoupled Adapt-then-Combine, which keeps a fully local estimate of the solution for the adaptation step. Our strategy, which is specially convenient for heterogeneous networks, is compared with the standard Adapt-then-Combine scheme and theoretically analyzed using energy conservation arguments. Such comparison shows the need of implementing adaptive combiners for both schemes to obtain a good performance in cas...
Hu, Mingxiao; Shen, Liangzhong; Zan, Xiangzhen; Shang, Xuequn; Liu, Wenbin
2016-01-01
Boolean networks are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long-term behavior of systems. In this paper, we investigate the less-studied one-bit perturbation, which falls under the category of structural intervention. Previous works focused on finding the optimal one-bit perturbation to maximally alter the steady-state distribution (SSD) of undesirable states through matrix perturbation theory. However, the application of the SSD is limited to Boolean networks with about ten genes. In 2007, Xiao et al. proposed to search the optimal one-bit perturbation by altering the sizes of the basin of attractions (BOAs). However, their algorithm requires close observation of the state-transition diagram. In this paper, we propose an algorithm that efficiently determines the BOA size after a perturbation. Our idea is that, if we construct the basin of states for all states, then the size of the BOA of perturbed networks can be obtained just by updating the paths of the states whose transitions have been affected. Results from both synthetic and real biological networks show that the proposed algorithm performs better than the exhaustive SSD-based algorithm and can be applied to networks with about 25 genes. PMID:27196530
Evolution of regulatory networks towards adaptability and stability in a changing environment.
Lee, Deok-Sun
2014-11-01
Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structure differences depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments. PMID:25493848
Evolution of regulatory networks towards adaptability and stability in a changing environment
Lee, Deok-Sun
2014-11-01
Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structure differences depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments.
Montanaro, Ashley; Osborne, Tobias J.
2008-01-01
In this paper we introduce the study of quantum boolean functions, which are unitary operators f whose square is the identity: f^2 = I. We describe several generalisations of well-known results in the theory of boolean functions, including quantum property testing; a quantum version of the Goldreich-Levin algorithm for finding the large Fourier coefficients of boolean functions; and two quantum versions of a theorem of Friedgut, Kalai and Naor on the Fourier spectra of boolean functions. In o...
Boolean reasoning the logic of boolean equations
Brown, Frank Markham
2012-01-01
A systematic treatment of Boolean reasoning, this concise, newly revised edition combines the works of early logicians with recent investigations, including previously unpublished research results. Brown begins with an overview of elementary mathematical concepts and outlines the theory of Boolean algebras. Two concluding chapters deal with applications. 1990 edition.
Andersen, Henrik Reif; Hulgaard, Henrik
2002-01-01
This paper presents a new data structure called boolean expression diagrams (BEDs) for representing and manipulating Boolean functions. BEDs are a generalization of binary decision diagrams (BDDs) which can represent any Boolean circuit in linear space. Two algorithms are described for transforming...
布尔表达式的化简与并行排序网络验证%Boolean expression simplification and parallel sort network validation
王德才; 徐建国; 吴哲辉; 罗永亮; 王传民
2009-01-01
To design an effective tool that can be used to verify the correctness of a parallel sorting network, a Boolean expression sim-plification algorithm based on the [0,1] theory and Boolean function of the characteristics and the nature is put forward, based on this algorithm a validation tool is designed. The characteristics and the nature of [0,1] theory and Boolean function are discussed and the natures that are helpful to simplify of the operation are pointed out. The tool can be used for the design of parallel sorting networks based on the parameters of the network graphics, and it can automatically generate the Boolean expressions and simplify it. The tool's output will be helpful to analyze the network, and it can also be used to design and optimize the sort network. Finally, the validity of the tool is demonstrated by the application.%为设计出能够验证并行排序网络正确性的有效工具,根据[0,1]原理和布尔函数的特点和性质,提出一种布尔表达式的化简算法,并根据此算法设计出验证工具.对[0,1]原理和布尔函数的特点和性质进行了讨论,指出有利于化简操作的性质.设计出的工具能够根据并行排序网络的参数显示网络图形、自动生成布尔表达式并实现化简验证,工具的输出有利于对排序网络的分析,也可以用于辅助排序网络的设计和优化.实验结果表明了该工具的有效性.
Controllability and observability of Boolean control networks%布尔控制网络的能控性与能观性
李志强; 宋金利
2013-01-01
Using the semi-tensor product,we convert the Boolean control network to its algebraic form.From the structure matrix of Boolean control network,the controllability and observability of the Boolean control network are discussed.A novel necessary and sufficient condition for controllability,which improves the recent results,is given.The new controllability condition eliminates the redundant computation of controllability matrix.The highest power of matrix is reduced from 2m+n to 2 n.Also,a sufficient condition for observability is obtained,which can be computed easily.A numerical example is presented to show the applicability of our controllability and observability condition.%利用矩阵的半张量积,布尔控制网络被转化为离散时间系统.本文从离散时间系统的结构矩阵出发,讨论了逻辑控制系统的能控能观性条件,得到了一个新的能控性条件.新的条件简化了原有能控性矩阵的计算复杂性,矩阵的最高阶数由原来的2m+n降到了2n.另外,还得到了检验布尔控制网络能观性的条件.与原有条件相比,新的条件更容易计算检验.最后,给出一个实例,检验给出的能控能观性判断条件的正确性.
Solomon, Alan D
2012-01-01
REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Boolean Algebra includes set theory, sentential calculus, fundamental ideas of Boolean algebras, lattices, rings and Boolean algebras, the structure of a Boolean algebra, and Boolean
Fluctuating epidemics on adaptive networks
Shaw, Leah B
2008-01-01
A model for epidemics on an adaptive network is considered. Nodes follow an SIRS (susceptible-infective-recovered-susceptible) pattern. Connections are rewired to break links from non-infected nodes to infected nodes and are reformed to connect to other non-infected nodes, as the nodes that are not infected try to avoid the infection. Monte Carlo simulation and numerical solution of a mean field model are employed. The introduction of rewiring affects both the network structure and the epidemic dynamics. Degree distributions are altered, and the average distance from a node to the nearest infective increases. The rewiring leads to regions of bistability where either an endemic or a disease-free steady state can exist. Fluctuations around the endemic state and the lifetime of the endemic state are considered. The fluctuations are found to exhibit power law behavior.
Adaptive Dynamics of Regulatory Networks: Size Matters
Martinetz Thomas
2009-01-01
Full Text Available To accomplish adaptability, all living organisms are constructed of regulatory networks on different levels which are capable to differentially respond to a variety of environmental inputs. Structure of regulatory networks determines their phenotypical plasticity, that is, the degree of detail and appropriateness of regulatory replies to environmental or developmental challenges. This regulatory network structure is encoded within the genotype. Our conceptual simulation study investigates how network structure constrains the evolution of networks and their adaptive abilities. The focus is on the structural parameter network size. We show that small regulatory networks adapt fast, but not as good as larger networks in the longer perspective. Selection leads to an optimal network size dependent on heterogeneity of the environment and time pressure of adaptation. Optimal mutation rates are higher for smaller networks. We put special emphasis on discussing our simulation results on the background of functional observations from experimental and evolutionary biology.
Caglar, Mehmet Umut; Pal, Ranadip
2011-03-01
Central dogma of molecular biology states that ``information cannot be transferred back from protein to either protein or nucleic acid''. However, this assumption is not exactly correct in most of the cases. There are a lot of feedback loops and interactions between different levels of systems. These types of interactions are hard to analyze due to the lack of cell level data and probabilistic - nonlinear nature of interactions. Several models widely used to analyze and simulate these types of nonlinear interactions. Stochastic Master Equation (SME) models give probabilistic nature of the interactions in a detailed manner, with a high calculation cost. On the other hand Probabilistic Boolean Network (PBN) models give a coarse scale picture of the stochastic processes, with a less calculation cost. Differential Equation (DE) models give the time evolution of mean values of processes in a highly cost effective way. The understanding of the relations between the predictions of these models is important to understand the reliability of the simulations of genetic regulatory networks. In this work the success of the mapping between SME, PBN and DE models is analyzed and the accuracy and affectivity of the control policies generated by using PBN and DE models is compared.
Cho Kwang-Hyun; Choi Sun; Kwon Yung-Keun
2007-01-01
Abstract Background A number of studies on biological networks have been carried out to unravel the topological characteristics that can explain the functional importance of network nodes. For instance, connectivity, clustering coefficient, and shortest path length were previously proposed for this purpose. However, there is still a pressing need to investigate another topological measure that can better describe the functional importance of network nodes. In this respect, we considered a fee...
Monotone Boolean functions are an important object in discrete mathematics and mathematical cybernetics. Topics related to these functions have been actively studied for several decades. Many results have been obtained, and many papers published. However, until now there has been no sufficiently complete monograph or survey of results of investigations concerning monotone Boolean functions. The object of this survey is to present the main results on monotone Boolean functions obtained during the last 50 years
Tucker, Jerry H.; Tapia, Moiez A.; Bennett, A. Wayne
1988-01-01
The concept of Boolean integration is developed, and different Boolean integral operators are introduced. Given the changes in a desired function in terms of the changes in its arguments, the ways of 'integrating' (i.e. realizing) such a function, if it exists, are presented. The necessary and sufficient conditions for integrating, in different senses, the expression specifying the changes are obtained. Boolean calculus has applications in the design of logic circuits and in fault analysis.
Andersen, Henrik Reif; Hulgaard, Henrik
This paper presents a new data structure called Boolean Expression Diagrams (BEDs) for representing and manipulating Boolean functions. BEDs are a generalization of Binary Decision Diagrams (BDDs) which can represent any Boolean circuit in linear space and still maintain many of the desirable...... properties of BDDs. Two algorithms are described for transforming a BED into a reduced ordered BDD. One closely mimics the BDD apply-operator while the other can exploit the structural information of the Boolean expression. The efficacy of the BED representation is demonstrated by verifying that the...
Adaptive Networks: the Governance for Sustainable Development
S.G. Nooteboom (Sibout)
2006-01-01
textabstractIn this book, I reconstruct how policy makers, working together in what I term adaptive networks, have enabled a breakthrough in thinking about sustainable mobility in certain policy circles. I define the conduct of leading actors in these adaptive networks as sustainable change manag
Dynamical Adaptation in Terrorist Cells/Networks
Hussain, Dil Muhammad Akbar; Ahmed, Zaki
2010-01-01
followers etc. In this research we analyze and predict the most likely role a particular node can adapt once a member of the network is either killed or caught. The adaptation is based on computing Bayes posteriori probability of each node and the level of the said node in the network structure.......Typical terrorist cells/networks have dynamical structure as they evolve or adapt to changes which may occur due to capturing or killing of a member of the cell/network. Analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long...
Adaptive cluster synchronization in complex dynamical networks
Cluster synchronization is investigated in different complex dynamical networks. In this Letter, a novel adaptive strategy is proposed to make a complex dynamical network achieve cluster synchronization, where the adaptive strategy of one edge is adjusted only according to its local information. A sufficient condition about the global stability arbitrarily grouped of cluster synchronization is derived. Several numerical simulations show the effectiveness of the adaptive strategy.
Recruitment dynamics in adaptive social networks
Shkarayev, Maxim S.; Schwartz, Ira B.; Shaw, Leah B.
2011-01-01
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the...
Analysis and Control of Boolean Networks:A Semi-tensor Product Approach%布尔网络的分析与控制-矩阵半张量积方法
程代展; 齐洪胜; 赵寅
2011-01-01
布尔网络是描述基因调控网络的一个有力工具.由于系统生物学的发展,布尔网络的分析与控制成为生物学与系统控制学科的交叉热点.本文综述作者用其原创的矩阵半张量积方法在布尔网络的分析与控制中得到的一系列结果.内容包括:布尔网络的拓扑结构,布尔控制网络的能控、能观性与实现,布尔网络的稳定性和布尔控制网络的镇定,布尔控制网络的干扰解耦,布尔(控制)网络的辨识,以及布尔网络的最优控制等.%Boolean network is a powerful tool for describing gene regulatory network. With the development of the systems biology, the analysis and control of Boolean networks become a hot topic for multidisciplinary research. This paper surveys some recent results obtained in the analysis and control of Boolean networks using semi-tensor product of matrices. The contents of this paper include the topological structure of Boolean networks, the controllability and observability, realization, stability and stabilization, disturbance decoupling, identification, and optimal control of Boolean (control) networks.
Boolean nested canalizing functions: a comprehensive analysis
Li, Yuan; Murrugarra, David; Aguilar, Boris; Laubenbacher, Reinhard
2012-01-01
Boolean network models of molecular regulatory networks have been used successfully in computational systems biology. The Boolean functions that appear in published models tend to have special properties, in particular the property of being nested canalizing, a property inspired by the concept of canalization in evolutionary biology. It has been shown that networks comprised of nested canalizing functions have dynamic properties that make them suitable for modeling molecular regulatory networks, namely a small number of (large) attractors, as well as relatively short limit cycles. This paper contains a detailed analysis of this class of functions, based on a novel normal form as polynomial functions over the Boolean field. The concept of layer is introduced that stratifies variables into different classes depending on their level of dominance. Using this layer concept a closed form formula is derived for the number of nested canalizing functions with a given number of variables. Additional metrics analyzed in...
Energy-efficient adaptive wireless network design
Havinga, Paul J. M.; Smit, Gerard J.M.; Bos, Martinus
2000-01-01
Energy efficiency is an important issue for mobile computers since they must rely on their batteries. We present an energy-efficient highly adaptive architecture of a network interface and novel data link layer protocol for wireless networks that provides quality of service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations are necessary to achieve energy efficiency and an acceptable quality of service. The paper provides a review of ideas and...
Adaptive Control Based On Neural Network
Wei, Sun; Lujin, Zhang; Jinhai, Zou; Siyi, Miao
2009-01-01
In this paper, the adaptive control based on neural network is studied. Firstly, a neural network based adaptive robust tracking control design is proposed for robotic systems under the existence of uncertainties. In this proposed control strategy, the NN is used to identify the modeling uncertainties, and then the disadvantageous effects caused by neural network approximating error and external disturbances in robotic system are counteracted by robust controller. Especially the proposed cont...
Neural Network Adaptations to Hardware Implementations
Moerland, Perry,; Fiesler,Emile
1997-01-01
In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential.However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling nonuniformities and ...
Neural Network Adaptations to Hardware Implementations
Moerland, Perry,; Fiesler,Emile; Beale, R
1997-01-01
In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential. However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling nonuniformities and...
On the number of attractors of Boolean automata circuits
Demongeot, Jacques; Noual, Mathilde; Sené, Sylvain
2009-01-01
In line with fields of theoretical computer science and biology that study Boolean automata networks often seen as models of regulation networks, we present some results concerning the dynamics of networks whose underlying interaction graphs are circuits, that is Boolean automata circuits. In the context of biological regulation, former studies have highlighted the importance of circuits on the asymptotic dynamical behaviour of the biological networks that contain them. Our work focuses on th...
Recruitment dynamics in adaptive social networks
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean-field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime). (paper)
Recruitment dynamics in adaptive social networks.
Shkarayev, Maxim S; Schwartz, Ira B; Shaw, Leah B
2013-01-01
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime). PMID:25395989
New Measure of Boolean Factor Analysis Quality
Frolov, A. A.; Húsek, Dušan; Polyakov, P.Y.
Vol. 1. Heidelberg: Springer, 2011 - (Dobnikar, A.; Lotrič, U.; Šter, B.), s. 100-109. (Lecture Notes in Computer Science. 6593). ISBN 978-3-642-20281-0. ISSN 0302-9743. [ICANNGA'2011. International Conference /10./. Ljubljana (SI), 14.04.2011-16.04.2011] R&D Projects: GA ČR GAP202/10/0262; GA ČR GA205/09/1079 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * information gain * expectation-maximization * associative memory * neural network application * Boolean matrix factorization * bars problem * Hopfield neural network Subject RIV: IN - Informatics, Computer Science
Boolean filters of distributive lattices
M. Sambasiva Rao
2013-07-01
Full Text Available In this paper we introduce the notion of Boolean filters in a pseudo-complemented distributive lattice and characterize the class of all Boolean filters. Further a set of equivalent conditions are derived for a proper filter to become a prime Boolean filter. Also a set of equivalent conditions is derived for a pseudo-complemented distributive lattice to become a Boolean algebra. Finally, a Boolean filter is characterized in terms of congruences.
Network measures for characterising team adaptation processes
Barth, S.K.; Schraagen, J.M.C.; Schmettow, M.
2015-01-01
The aim of this study was to advance the conceptualisation of team adaptation by applying social network analysis (SNA) measures in a field study of a paediatric cardiac surgical team adapting to changes in task complexity and ongoing dynamic complexity. Forty surgical procedures were observed by tr
Neural network with dynamically adaptable neurons
Tawel, Raoul (Inventor)
1994-01-01
This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.
Stratification and enumeration of Boolean functions by canalizing depth
He, Qijun
2015-01-01
Boolean network models have gained popularity in computational systems biology over the last dozen years. Many of these networks use canalizing Boolean functions, which has led to increased interest in the study of these functions. The canalizing depth of a function describes how many canalizing variables can be recursively picked off, until a non-canalizing function remains. In this paper, we show how every Boolean function has a unique algebraic form involving extended monomial layers and a well-defined core polynomial. This generalizes recent work on the algebraic structure of nested canalizing functions, and it yields a stratification of all Boolean functions by their canalizing depth. As a result, we obtain closed formulas for the number of n-variable Boolean functions with depth k, which simultaneously generalizes enumeration formulas for canalizing, and nested canalizing functions.
Stratification and enumeration of Boolean functions by canalizing depth
He, Qijun; Macauley, Matthew
2016-01-01
Boolean network models have gained popularity in computational systems biology over the last dozen years. Many of these networks use canalizing Boolean functions, which has led to increased interest in the study of these functions. The canalizing depth of a function describes how many canalizing variables can be recursively "picked off", until a non-canalizing function remains. In this paper, we show how every Boolean function has a unique algebraic form involving extended monomial layers and a well-defined core polynomial. This generalizes recent work on the algebraic structure of nested canalizing functions, and it yields a stratification of all Boolean functions by their canalizing depth. As a result, we obtain closed formulas for the number of n-variable Boolean functions with depth k, which simultaneously generalizes enumeration formulas for canalizing, and nested canalizing functions.
Adaptive Networks Theory, Models and Applications
Gross, Thilo
2009-01-01
With adaptive, complex networks, the evolution of the network topology and the dynamical processes on the network are equally important and often fundamentally entangled. Recent research has shown that such networks can exhibit a plethora of new phenomena which are ultimately required to describe many real-world networks. Some of those phenomena include robust self-organization towards dynamical criticality, formation of complex global topologies based on simple, local rules, and the spontaneous division of "labor" in which an initially homogenous population of network nodes self-organizes into functionally distinct classes. These are just a few. This book is a state-of-the-art survey of those unique networks. In it, leading researchers set out to define the future scope and direction of some of the most advanced developments in the vast field of complex network science and its applications.
In-Network Adaptation of Video Streams Using Network Processors
Mohammad Shorfuzzaman
2009-01-01
problem can be addressed, near the network edge, by applying dynamic, in-network adaptation (e.g., transcoding of video streams to meet available connection bandwidth, machine characteristics, and client preferences. In this paper, we extrapolate from earlier work of Shorfuzzaman et al. 2006 in which we implemented and assessed an MPEG-1 transcoding system on the Intel IXP1200 network processor to consider the feasibility of in-network transcoding for other video formats and network processor architectures. The use of “on-the-fly” video adaptation near the edge of the network offers the promise of simpler support for a wide range of end devices with different display, and so forth, characteristics that can be used in different types of environments.
Lahoz-Beltra, R; Hameroff, S R; Dayhoff, J E
1993-01-01
Adaptive behaviors and dynamic activities within living cells are organized by the cytoskeleton: intracellular networks of interconnected protein polymers which include microtubules (MTs), actin, intermediate filaments, microtubule associated proteins (MAPs) and other protein structures. Cooperative interactions among cytoskeletal protein subunit conformational states have been used to model signal transmission and information processing. In the present work we present a theoretical model for molecular computing in which Boolean logic is implemented in parallel networks of individual MTs interconnected by MAPs. Conformational signals propagate on MTs as in data buses and in the model MAPs are considered as Boolean operators, either as bit-lines (like MTs) where a signal can be transported unchanged between MTs ('BUS-MAP'), or as bit-lines where a Boolean operation is performed in one of the two MAP-MT attachments ('LOGIC-MAP'). Three logic MAPs have been defined ('NOT-MAP, 'AND-MAP', 'XOR-MAP') and used to demonstrate addition, subtraction and other arithmetic operations. Although our choice of Boolean logic is arbitrary, the simulations demonstrate symbolic manipulation in a connectionist system and suggest that MT-MAP networks can perform computation in living cells and are candidates for future molecular computing devices. PMID:8318677
Boolean gates on actin filaments
Siccardi, Stefano; Tuszynski, Jack A.; Adamatzky, Andrew
2016-01-01
Actin is a globular protein which forms long polar filaments in the eukaryotic cytoskeleton. Actin networks play a key role in cell mechanics and cell motility. They have also been implicated in information transmission and processing, memory and learning in neuronal cells. The actin filaments have been shown to support propagation of voltage pulses. Here we apply a coupled nonlinear transmission line model of actin filaments to study interactions between voltage pulses. To represent digital information we assign a logical TRUTH value to the presence of a voltage pulse in a given location of the actin filament, and FALSE to the pulse's absence, so that information flows along the filament with pulse transmission. When two pulses, representing Boolean values of input variables, interact, then they can facilitate or inhibit further propagation of each other. We explore this phenomenon to construct Boolean logical gates and a one-bit half-adder with interacting voltage pulses. We discuss implications of these findings on cellular process and technological applications.
Boolean Delay Equations: A Simple Way of Looking at Interactions and Extreme Events
Ghil, Michael
2013-04-01
Boolean Delay Equations (BDEs) are semi-discrete dynamical models with Boolean-valued variables that evolve in continuous time. Systems of BDEs can be classified into conservative or dissipative, in a manner that parallels the classification of ordinary or partial differential equations. Solutions to certain conservative BDEs exhibit growth of complexity in time; such BDEs can be seen therefore as metaphors for biological evolution or human history. Dissipative BDEs are structurally stable and exhibit multiple equilibria and limit cycles, as well as more complex, fractal solution sets, such as Devil's staircases and ``fractal sunbursts.'' BDE systems have been used as highly idealized models of climate change on several time scales, as well as in earthquake modeling and prediction, and in genetics. BDEs with an infinite number of variables on a regular spatial grid have been called "partial BDEs" and we discuss connections with other types of discrete dynamical systems, including cellular automata and Boolean networks. We present recent BDE work on damage propagation in networks, with an emphasis on production-chain models. This formalism turns out to be well adapted to investigating propagation of an initial damage due to a climatic or other natural disaster. It thus serves to study economic impacts of extreme events, as well as extreme disruption of a network of interactions.
Adaptation by Plasticity of Genetic Regulatory Networks
Brenner, Naama
2007-03-01
Genetic regulatory networks have an essential role in adaptation and evolution of cell populations. This role is strongly related to their dynamic properties over intermediate-to-long time scales. We have used the budding yeast as a model Eukaryote to study the long-term dynamics of the genetic regulatory system and its significance in evolution. A continuous cell growth technique (chemostat) allows us to monitor these systems over long times under controlled condition, enabling a quantitative characterization of dynamics: steady states and their stability, transients and relaxation. First, we have demonstrated adaptive dynamics in the GAL system, a classic model for a Eukaryotic genetic switch, induced and repressed by different carbon sources in the environment. We found that both induction and repression are only transient responses; over several generations, the system converges to a single robust steady state, independent of external conditions. Second, we explored the functional significance of such plasticity of the genetic regulatory network in evolution. We used genetic engineering to mimic the natural process of gene recruitment, placing the gene HIS3 under the regulation of the GAL system. Such genetic rewiring events are important in the evolution of gene regulation, but little is known about the physiological processes supporting them and the dynamics of their assimilation in a cell population. We have shown that cells carrying the rewired genome adapted to a demanding change of environment and stabilized a population, maintaining the adaptive state for hundreds of generations. Using genome-wide expression arrays we showed that underlying the observed adaptation is a global transcriptional programming that allowed tuning expression of the recruited gene to demands. Our results suggest that non-specific properties reflecting the natural plasticity of the regulatory network support adaptation of cells to novel challenges and enhance their evolvability.
Bayesian Network Models for Adaptive Testing
Plajner, Martin; Vomlel, Jiří
Achen: Sun SITE Central Europe, 2016 - (Agosta, J.; Carvalho, R.), s. 24-33. (CEUR Workshop Proceedings. Vol 1565). ISSN 1613-0073. [The Twelfth UAI Bayesian Modeling Applications Workshop (BMAW 2015). Amsterdam (NL), 16.07.2015] R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : Bayesian networks * Computerized adaptive testing Subject RIV: JD - Computer Applications, Robotics http://library.utia.cas.cz/separaty/2016/MTR/plajner-0458062.pdf
Adaptive scheduling in cellular access, wireless mesh and IP networks
Nieminen, Johanna
2011-01-01
Networking scenarios in the future will be complex and will include fixed networks and hybrid Fourth Generation (4G) networks, consisting of both infrastructure-based and infrastructureless, wireless parts. In such scenarios, adaptive provisioning and management of network resources becomes of critical importance. Adaptive mechanisms are desirable since they enable a self-configurable network that is able to adjust itself to varying traffic and channel conditions. The operation of adaptive me...
A Neural Network for Generating Adaptive Lessons
Hassina Seridi-Bouchelaghem
2005-01-01
Full Text Available Traditional sequencing technology developed in the field of intelligent tutoring systems have not find an immediate place in large-scale Web-based education. This study investigates the use of computational intelligence for adaptive lesson generation in a distance learning environment over the Web. An approach for adaptive pedagogical hypermedia document generation is proposed and implemented in a prototype called KnowledgeClass. This approach is based on a specialized artificial neural network model. The system allows automatic generation of individualised courses according to the learners goal and previous knowledge and can dynamically adapt the course according to the learners success in acquiring knowledge. Several experiments showed the effectiveness of the proposed method.
Inadmissible Class of Boolean Functions under Stuck-at Faults
Das, Debesh K.; Chowdhury, Debabani; Bhattacharya, Bhargab B; Sasao, Tsutomu
2013-01-01
Many underlying structural and functional factors that determine the fault behavior of a combinational network, are not yet fully understood. In this paper, we show that there exists a large class of Boolean functions, called root functions, which can never appear as faulty response in irredundant two-level circuits even when any arbitrary multiple stuck-at faults are injected. Conversely, we show that any other Boolean function can appear as a faulty response from an irredundant realization ...
Fault Tolerant Boolean Satisfiability
Roy, A
2011-01-01
A delta-model is a satisfying assignment of a Boolean formula for which any small alteration, such as a single bit flip, can be repaired by flips to some small number of other bits, yielding a new satisfying assignment. These satisfying assignments represent robust solutions to optimization problems (e.g., scheduling) where it is possible to recover from unforeseen events (e.g., a resource becoming unavailable). The concept of delta-models was introduced by Ginsberg, Parkes and Roy (AAAI 1998), where it was proved that finding delta-models for general Boolean formulas is NP-complete. In this paper, we extend that result by studying the complexity of finding delta-models for classes of Boolean formulas which are known to have polynomial time satisfiability solvers. In particular, we examine 2-SAT, Horn-SAT, Affine-SAT, dual-Horn-SAT, 0-valid and 1-valid SAT. We see a wide variation in the complexity of finding delta-models, e.g., while 2-SAT and Affine-SAT have polynomial time tests for delta-models, testing w...
Algorithms for Weighted Boolean Optimization
Manquinho, Vasco; Marques-Silva, Joao; Planes Cid, Jordi
2009-01-01
The Pseudo-Boolean Optimization (PBO) and Maximum Satisfiability (MaxSAT) problems are natural optimization extensions of Boolean Satisfiability (SAT). In the recent past, different algorithms have been proposed for PBO and for MaxSAT, despite the existence of straightforward mappings from PBO to MaxSAT and viceversa. This papers proposes Weighted Boolean Optimization (WBO), a new uni- fied framework that aggregates and extends PBO and MaxSAT. In addition, the paper proposes...
Geometric Operators on Boolean Functions
Frisvad, Jeppe Revall; Falster, Peter
2007-01-01
In truth-functional propositional logic, any propositional formula represents a Boolean function (according to some valuation of the formula). We describe operators based on Decartes' concept of constructing coordinate systems, for translation of a propositional formula to the image of a Boolean function. With this image of a Boolean function corresponding to a propositional formula, we prove that the orthogonal projection operator leads to a theorem describing all rules of inference in propo...
On Capability-Related Adaptation in Networked Service Systems
Finn Arve Aagesen; Patcharee Thongtra
2012-01-01
Adaptability is a property related to engineering as well as to the execution of networked service systems. This publication considers issues of adaptability both within a general and a scoped view. The generalview considers issues of adaptation at two levels: 1) System of entities, functions and adaptability types, and 2) Architectures supporting adaptability. Adaptability types defined are capability-related, functionality-related and context-related adaptation. The scoped view of the publi...
Cooperative Media Streaming Using Adaptive Network Compression
Møller, Janus Heide; Sørensen, Jesper Hemming; Krigslund, Rasmus;
2008-01-01
an adaptive hybrid between LC and MDC. In order to facilitate the use of MDC-CC, a new overlay network approach is proposed, using tree of meshes. A control system for managing description distribution and compression in a small mesh is implemented in the discrete event simulator NS-2. The two...... media distribution using traditional approaches. In particular, the asymmetric relationship between the uplink and the downlink bandwidth makes the cooperative distribution difﬁcult. A promising concept, termed MDC with Conditional Compression (MDC-CC), has been proposed [11], which essentially acts as...
Adaptive-network models of swarm dynamics
Huepe, Cristian [614 N Paulina Street, Chicago, IL 60622-6062 (United States); Zschaler, Gerd; Do, Anne-Ly; Gross, Thilo, E-mail: cristian@northwestern.edu [Max-Planck-Institut fuer Physik komplexer Systeme, Noethnitzer Strasse 38, 01187 Dresden (Germany)
2011-07-15
We propose a simple adaptive-network model describing recent swarming experiments. Exploiting an analogy with human decision making, we capture the dynamics of the model using a low-dimensional system of equations permitting analytical investigation. We find that the model reproduces several characteristic features of swarms, including spontaneous symmetry breaking, noise- and density-driven order-disorder transitions that can be of first or second order, and intermittency. Reproducing these experimental observations using a non-spatial model suggests that spatial geometry may have less of an impact on collective motion than previously thought.
Adaptive-network models of swarm dynamics
We propose a simple adaptive-network model describing recent swarming experiments. Exploiting an analogy with human decision making, we capture the dynamics of the model using a low-dimensional system of equations permitting analytical investigation. We find that the model reproduces several characteristic features of swarms, including spontaneous symmetry breaking, noise- and density-driven order-disorder transitions that can be of first or second order, and intermittency. Reproducing these experimental observations using a non-spatial model suggests that spatial geometry may have less of an impact on collective motion than previously thought.
Quantitative Adaptive RED in Differentiated Service Networks
LONG KePing(隆克平); WANG Qian(王茜); CHENG ShiDuan(程时端); CHEN JunLiang(陈俊亮)
2003-01-01
This paper derives a quantitative model between RED (Random Early Detection)maxp and committed traffic rate for token-based marking schemes in DiffServ IP networks. Then,a DiffServ Quantitative RED (DQRED) is presented, which can adapt its dropping probabilityto marking probability of the edge router to reflect not only the sharing bandwidth but also therequirement of performance of these services. Hence, DQRED can cooperate with marking schemesto guarantee fairness between different DiffServ AF class services. A new marking probabilitymetering algorithm is also proposed to cooperate with DQRED. Simulation results verify thatDQRED mechanism can not only control congestion of DiffServ network very well, but also satisfydifferent quality requirements of AF class service. The performance of DQRED is better than thatof WRED.
Adaptive bipartite consensus on coopetition networks
Hu, Jiangping; Zhu, Hong
2015-07-01
In this paper, a bipartite consensus tracking problem is considered for a group of autonomous agents on a coopetition network, on which the agents interact cooperatively and competitively simultaneously. The coopetition network involves positive and negative edges and is conveniently modeled by a signed graph. Additionally, the dynamics of all the agents are subjected to unknown disturbances, which are represented by linearly parameterized models. An adaptive estimation scheme is designed for each agent by virtue of the relative position measurements and the relative velocity measurements from its neighbors. Then a consensus tracking law is proposed for a new distributed system, which uses the relative measurements as the new state variables. The convergence of the consensus tracking error and the parameter estimation are analyzed even when the coopetition network is time-varying and no more global information about the bounds of the unknown disturbances is available to all the agents. Finally, some simulation results are provided to demonstrate the formation of the bipartite consensus on the coopetition network.
Fault Tolerant Boolean Satisfiability
Roy, A
2011-01-01
A delta-model is a satisfying assignment of a Boolean formula for which any small alteration, such as a single bit flip, can be repaired by flips to some small number of other bits, yielding a new satisfying assignment. These satisfying assignments represent robust solutions to optimization problems (e.g., scheduling) where it is possible to recover from unforeseen events (e.g., a resource becoming unavailable). The concept of delta-models was introduced by Ginsberg, Parkes and Roy (AAAI 1998...
Symmetry in Boolean Satisfiability
Fadi A. Aloul
2010-06-01
Full Text Available This paper reviews recent approaches on how to accelerate Boolean Satisfiability (SAT search by exploiting symmetries in the problem space. SAT search algorithms traverse an exponentially large search space looking for an assignment that satisfies a set of constraints. The presence of symmetries in the search space induces equivalence classes on the set of truth assignments. The goal is to use symmetries to avoid traversing all assignments by constraining the search to visit a few representative assignments in each equivalence class. This can lead to a significant reduction in search runtime without affecting the completeness of the search.
Boolean Orthogonalizing Combination Methods
Yavuz Can
2015-05-01
Full Text Available In this paper a new logical operation method called “ presented. It is used to calculate the difference, but also the complement of a function as well as the EXOR and EXNOR of two minterms respectively two ternary respectively two ternary-vector logical operation method called “orthogonal OR advantages of both methods are their results, which are already available form that has an essential advantage for continuing calculations. Since it applies, an orthogonal disjunctive normal form is equal to orthogonal antivalence normal form, subsequent Boolean differential calculus will be simplified.
Approximate Reasoning with Fuzzy Booleans
Broek, van den P.M.; Noppen, J.A.R.
2004-01-01
This paper introduces, in analogy to the concept of fuzzy numbers, the concept of fuzzy booleans, and examines approximate reasoning with the compositional rule of inference using fuzzy booleans. It is shown that each set of fuzzy rules is equivalent to a set of fuzzy rules with singleton crisp ante
Geometric Operators on Boolean Functions
Frisvad, Jeppe Revall; Falster, Peter
of a few geometric operators working on the images of Boolean functions. The operators we describe, arise from the niche area of array-based logic and have previously been tightly bound to an array-based representation of Boolean functions. We redefine the operators in an abstract form to make them...
Cryptographic Boolean functions and applications
Cusick, Thomas W
2009-01-01
Boolean functions are the building blocks of symmetric cryptographic systems. Symmetrical cryptographic algorithms are fundamental tools in the design of all types of digital security systems (i.e. communications, financial and e-commerce).Cryptographic Boolean Functions and Applications is a concise reference that shows how Boolean functions are used in cryptography. Currently, practitioners who need to apply Boolean functions in the design of cryptographic algorithms and protocols need to patch together needed information from a variety of resources (books, journal articles and other sources). This book compiles the key essential information in one easy to use, step-by-step reference. Beginning with the basics of the necessary theory the book goes on to examine more technical topics, some of which are at the frontier of current research.-Serves as a complete resource for the successful design or implementation of cryptographic algorithms or protocols using Boolean functions -Provides engineers and scient...
Cardinal invariants on Boolean algebras
Monk, J Donald
2014-01-01
This book is concerned with cardinal number valued functions defined for any Boolean algebra. Examples of such functions are independence, which assigns to each Boolean algebra the supremum of the cardinalities of its free subalgebras, and cellularity, which gives the supremum of cardinalities of sets of pairwise disjoint elements. Twenty-one such functions are studied in detail, and many more in passing. The questions considered are the behaviour of these functions under algebraic operations such as products, free products, ultraproducts, and their relationships to one another. Assuming familiarity with only the basics of Boolean algebras and set theory, through simple infinite combinatorics and forcing, the book reviews current knowledge about these functions, giving complete proofs for most facts. A special feature of the book is the attention given to open problems, of which 185 are formulated. Based on Cardinal Functions on Boolean Algebras (1990) and Cardinal Invariants on Boolean Algebras (1996) by the...
Probabilistic Adaptive Anonymous Authentication in Vehicular Networks
Yong Xi; Ke-Wei Sha; Wei-Song Shi; Loren Schwiebert; Tao Zhang
2008-01-01
Vehicular networks have attracted extensive attention in recent years for their promises in improving safety and enabling other value-added services. Most previous work focuses on designing the media access and physical layer protocols.Privacy issues in vehicular systems have not been well addressed. We argue that privacy is a user-specific concept, and a good privacy protection mechanism should allow users to select the levels of privacy they wish to have. To address this requirement, we propose an adaptive anonymous authentication mechanism that can trade off the anonymity level with computational and communication overheads (resource usage). This mechanism, to our knowledge, is the first effort on adaptive anonymous authentication. The resources used by our protocol are few. A high traffic volume of 2000 vehicles per hour consumes about 60kbps bandwidth, which is less than one percent of the bandwidth of DSRC (Dedicated Short Range Communications). By using adaptive anonymity, the protocol response time can further be improved 2～4 times with lessthan 20% bandwidth overheads.
Integrated Adaptive Analysis and Visualization of Satellite Network Data Project
National Aeronautics and Space Administration — We propose to develop a system that enables integrated and adaptive analysis and visualization of satellite network management data. Integrated analysis and...
Opportunistic Adaptive Relaying in Cognitive Radio Networks
Jaafar, Wael; Haccoun, David
2012-01-01
Combining cognitive radio technology with user cooperation could be advantageous to both primary and secondary transmissions. In this paper, we propose a first relaying scheme for cognitive radio networks (called "Adaptive relaying scheme 1"), where one relay node can assist the primary or the secondary transmission with the objective of improving the outage probability of the secondary transmission with respect to a primary outage probability threshold. Upper bound expressions of the secondary outage probability using the proposed scheme are derived over Rayleigh fading channels. Numerical and simulation results show that the secondary outage probability using the proposed scheme is lower than that of other relaying schemes. Then, we extend the proposed scheme to the case where the relay node has the ability to decode both the primary and secondary signals and also can assist simultaneously both transmissions. Simulations show the performance improvement that can be obtained due to this extension in terms of...
Link-based formalism for time evolution of adaptive networks
Zhou, Jie; Chen, Guanrong
2013-01-01
Network topology and nodal dynamics are two fundamental stones of adaptive networks. Detailed and accurate knowledge of these two ingredients is crucial for understanding the evolution and mechanism of adaptive networks. In this paper, by adopting the framework of the adaptive SIS model proposed by Gross et al. [Phys. Rev. Lett. 96, 208701 (2006)] and carefully utilizing the information of degree correlation of the network, we propose a link-based formalism for describing the system dynamics with high accuracy and subtle details. Several specific degree correlation measures are introduced to reveal the coevolution of network topology and system dynamics.
Adaptive training of feedforward neural networks by Kalman filtering
Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.)
On the Number of Attractors of Positive and Negative Boolean Automata Circuits.
Demongeot, Jacques; Noual, Mathilde; Sené, Sylvain
2010-01-01
International audience In line with fields of theoretical computer science and biology that study Boolean automata networks often seen as models of regulation networks, we present some results concerning the dynamics of networks whose underlying interaction graphs are circuits, that is, Boolean automata circuits. In the context of biological regulation, former studies have highlighted the importance of circuits on the asymptotic dynamical behaviour of the biological networks that contain t...
On Kolmogorov's superpositions and Boolean functions
Beiu, V.
1998-12-31
The paper overviews results dealing with the approximation capabilities of neural networks, as well as bounds on the size of threshold gate circuits. Based on an explicit numerical (i.e., constructive) algorithm for Kolmogorov's superpositions they will show that for obtaining minimum size neutral networks for implementing any Boolean function, the activation function of the neurons is the identity function. Because classical AND-OR implementations, as well as threshold gate implementations require exponential size (in the worst case), it will follow that size-optimal solutions for implementing arbitrary Boolean functions require analog circuitry. Conclusions and several comments on the required precision are ending the paper.
Speed Adaptation in Urban Road Network Management
Raiyn Jamal
2016-06-01
Full Text Available Various forecasting schemes have been proposed to manage traffic data, which is collected by videos cameras, sensors, and mobile phone services. However, these are not sufficient for collecting data because of their limited coverage and high costs for installation and maintenance. To overcome the limitations of these tools, we introduce a hybrid scheme based on intelligent transportation system (ITS and global navigation satellite system (GNSS. Applying the GNSS to calculate travel time has proven efficient in terms of accuracy. In this case, GNSS data is managed to reduce traffic congestion and road accidents. This paper introduces a short-time forecasting model based on real-time travel time for urban heterogeneous road networks. Travel time forecasting has been achieved by predicting travel speeds using an optimized exponential moving Average (EMA model. Furthermore for speed adaptation in heterogeneous road networks, it is necessary to introduce asuitable control strategy for longitude, based on the GNSS. GNSS products provide worldwide and real-time services using precise timing information and, positioning technologies.
Opinion dynamics on an adaptive random network
Benczik, I. J.; Benczik, S. Z.; Schmittmann, B.; Zia, R. K. P.
2009-04-01
We revisit the classical model for voter dynamics in a two-party system with two basic modifications. In contrast to the original voter model studied in regular lattices, we implement the opinion formation process in a random network of agents in which interactions are no longer restricted by geographical distance. In addition, we incorporate the rapidly changing nature of the interpersonal relations in the model. At each time step, agents can update their relationships. This update is determined by their own opinion, and by their preference to make connections with individuals sharing the same opinion, or rather with opponents. In this way, the network is built in an adaptive manner, in the sense that its structure is correlated and evolves with the dynamics of the agents. The simplicity of the model allows us to examine several issues analytically. We establish criteria to determine whether consensus or polarization will be the outcome of the dynamics and on what time scales these states will be reached. In finite systems consensus is typical, while in infinite systems a disordered metastable state can emerge and persist for infinitely long time before consensus is reached.
Brain network adaptability across task states.
Elizabeth N Davison
2015-01-01
Full Text Available Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific and across (task-general brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.
Techniques for solving Boolean equation systems
Keinänen, Misa
2006-01-01
Boolean equation systems are ordered sequences of Boolean equations decorated with least and greatest fixpoint operators. Boolean equation systems provide a useful framework for formal verification because various specification and verification problems, for instance, μ-calculus model checking can be represented as the problem of solving Boolean equation systems. The general problem of solving a Boolean equation system is a computationally hard task, and no polynomial time solution technique ...
Adaptive-impulsive synchronization of uncertain complex dynamical networks
This Letter studies adaptive-impulsive synchronization of uncertain complex dynamical networks. Based on the stability analysis of impulsive system, several network synchronization criteria for local and global adaptive-impulsive synchronization are established. Numerical example is also given to illustrate the results
A more robust Boolean model describing inhibitor binding
Zhaoqian Steven XIE; Chao TANG
2008-01-01
From the first application of the Boolean model to the cell cycle regulation network of budding yeast, new regulative pathways have been discovered, par-ticularly in the G1/S transition circuit. This discovery called for finer modeling to study the essential biology, and the resulting outcomes are first introduced in the ar-ticle. A traditional Boolean network model set up for the new G1/S transition circuit shows that it cannot correctly simulate real biology unless the model parameters are fine tuned. The deficiency is caused by an overly coarse-grained description of the inhibitor binding process, which shall be overcome by a two-vector model proposed whose robustness is surveyed using random perturba-tions. Simulations show that the proposed two-vector model is much more robust in describing inhibitor binding processes within the Boolean framework.
A Note on the Inversion Complexity of Boolean Functions in Boolean Formulas
Morizumi, Hiroki
2008-01-01
In this note, we consider the minimum number of NOT operators in a Boolean formula representing a Boolean function. In circuit complexity theory, the minimum number of NOT gates in a Boolean circuit computing a Boolean function $f$ is called the inversion complexity of $f$. In 1958, Markov determined the inversion complexity of every Boolean function and particularly proved that $\\lceil \\log_2(n+1) \\rceil$ NOT gates are sufficient to compute any Boolean function on $n$ variables. As far as we...
Synchronization of general complex networks via adaptive control schemes
Ping He; Chun-Guo Jing; Chang-Zhong Chen; Tao Fan; Hassan Saberi Nik
2014-03-01
In this paper, the synchronization problem of general complex networks is investigated by using adaptive control schemes. Time-delay coupling, derivative coupling, nonlinear coupling etc. exist universally in real-world complex networks. The adaptive synchronization scheme is designed for the complex network with multiple class of coupling terms. A criterion guaranteeing synchronization of such complex networks is established by employing the Lyapunov stability theorem and adaptive control schemes. Finally, an illustrative example with numerical simulation is given to show the feasibility and efficiency of theoretical results.
Adaptive Synchronization of Complex Dynamical Networks with State Predictor
Yuntao Shi; Bo Liu; Xiao Han
2013-01-01
This paper addresses the adaptive synchronization of complex dynamical networks with nonlinear dynamics. Based on the Lyapunov method, it is shown that the network can synchronize to the synchronous state by introducing local adaptive strategy to the coupling strengths. Moreover, it is also proved that the convergence speed of complex dynamical networks can be increased via designing a state predictor. Finally, some numerical simulations are worked out to illustrate the analytical results.
Electrooptical adaptive switching network for the hypercube computer
Chow, E.; Peterson, J.
1988-01-01
An all-optical network design for the hyperswitch network using regular free-space interconnects between electronic processor nodes is presented. The adaptive routing model used is described, and an adaptive routing control example is presented. The design demonstrates that existing electrooptical techniques are sufficient for implementing efficient parallel architectures without the need for more complex means of implementing arbitrary interconnection schemes. The electrooptical hyperswitch network significantly improves the communication performance of the hypercube computer.
Adaptive Mobile Positioning in WCDMA Networks
Dong B.
2005-01-01
Full Text Available We propose a new technique for mobile tracking in wideband code-division multiple-access (WCDMA systems employing multiple receive antennas. To achieve a high estimation accuracy, the algorithm utilizes the time difference of arrival (TDOA measurements in the forward link pilot channel, the angle of arrival (AOA measurements in the reverse-link pilot channel, as well as the received signal strength. The mobility dynamic is modelled by a first-order autoregressive (AR vector process with an additional discrete state variable as the motion offset, which evolves according to a discrete-time Markov chain. It is assumed that the parameters in this model are unknown and must be jointly estimated by the tracking algorithm. By viewing a nonlinear dynamic system such as a jump-Markov model, we develop an efficient auxiliary particle filtering algorithm to track both the discrete and continuous state variables of this system as well as the associated system parameters. Simulation results are provided to demonstrate the excellent performance of the proposed adaptive mobile positioning algorithm in WCDMA networks.
LTE Adaptation for Mobile Broadband Satellite Networks
Bastia Francesco
2009-01-01
Full Text Available One of the key factors for the successful deployment of mobile satellite systems in 4G networks is the maximization of the technology commonalities with the terrestrial systems. An effective way of achieving this objective consists in considering the terrestrial radio interface as the baseline for the satellite radio interface. Since the 3GPP Long Term Evolution (LTE standard will be one of the main players in the 4G scenario, along with other emerging technologies, such as mobile WiMAX; this paper analyzes the possible applicability of the 3GPP LTE interface to satellite transmission, presenting several enabling techniques for this adaptation. In particular, we propose the introduction of an inter-TTI interleaving technique that exploits the existing H-ARQ facilities provided by the LTE physical layer, the use of PAPR reduction techniques to increase the resilience of the OFDM waveform to non linear distortion, and the design of the sequences for Random Access, taking into account the requirements deriving from the large round trip times. The outcomes of this analysis show that, with the required proposed enablers, it is possible to reuse the existing terrestrial air interface to transmit over the satellite link.
Temporal percolation of a susceptible adaptive network
Valdez, L D; Braunstein, L A
2013-01-01
In the last decades, due to the appearance of many diseases such as SARS and the H1N1 flu strain, many authors studied the impact of the disease spreading in the evolution of the infected individuals using the susceptible-infected-recovered model. However, few authors focused on the temporal behavior of the susceptible individuals. Recently it was found that in an epidemic spreading, the dynamic of the size of the biggest susceptible cluster can be explained by a temporal node void percolation [Valdez et al PLoS ONE 7, e44188 (2012)]. It was shown that the size of the biggest susceptible cluster is the order parameter of this temporal percolation where the control parameter can be related to the number of links between susceptible individuals at a given time. As a consequence, there is a critical time at which the biggest susceptible cluster is destroyed. In this paper, we study the susceptible-infected-recovered model in an adaptive network where an intermittent social distancing strategy is applied. In this...
Fuzzy Optimized Metric for Adaptive Network Routing
Ahmad Khader Haboush
2012-04-01
Full Text Available Network routing algorithms used today calculate least cost (shortest paths between nodes. The cost of a path is the sum of the cost of all links on that path. The use of a single metric for adaptive routing is insufficient to reflect the actual state of the link. In general, there is a limitation on the accuracy of the link state information obtained by the routing protocol. Hence it becomes useful if two or more metrics can be associated to produce a single metric that can describe the state of the link more accurately. In this paper, a fuzzy inference rule base is implemented to generate the fuzzy cost of each candidate path to be used in routing the incoming calls. This fuzzy cost is based on the crisp values of the different metrics; a fuzzy membership function is defined. The parameters of these membership functions reflect dynamically the requirement of the incoming traffic service as well as the current state of the links in the path. And this paper investigates how three metrics, the mean link bandwidth, queue utilization and the mean link delay, can be related using a simple fuzzy logic algorithm to produce a optimized cost of the link for a certain interval that is more „precise‟ than either of the single metric, to solve routing problem .
Dynamic multimedia stream adaptation and rate control for heterogeneous networks
SZWABE Andrzej; SCHORR Andreas; HAUCK Franz J.; KASSLER Andreas J.
2006-01-01
Dynamic adaptation of multimedia content is seen as an important feature of next generation networks and pervasive systems enabling terminals and applications to adapt to changes in e.g. context, access network, and available Quality-of-Service(QoS) due to mobility of users, devices or sessions. We present the architecture of a multimedia stream adaptation service which enables communication between terminals having heterogeneous hardware and software capabilities and served by heterogeneous networks. The service runs on special content adaptation nodes which can be placed at any location within the network. The flexible structure of our architecture allows using a variety of different adaptation engines. A generic transcoding engine is used to change the codec of streams. An MPEG-21 Digital Item Adaptation (DIA) based transformation engine allows adjusting the data rate of scalable media streams. An intelligent decision-taking engine implements adaptive flow control which takes into account current network QoS parameters and congestion information. Measurements demonstrate the quality gains achieved through adaptive congestion control mechanisms under conditions typical for a heterogeneous network.
Adapting Bayes Network Structures to Non-stationary Domains
Nielsen, Søren Holbech; Nielsen, Thomas Dyhre
2008-01-01
When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN is gradu......When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN...
Information Theoretic Adaptive Tracking of Epidemics in Complex Networks
Harrington, Patrick L
2013-01-01
Adaptively monitoring the states of nodes in a large complex network is of interest in domains such as national security, public health, and energy grid management. Here, we present an information theoretic adaptive tracking and sampling framework that recursively selects measurements using the feedback from performing inference on a dynamic Bayesian Network. We also present conditions for the existence of a network specific, observation dependent, phase transition in the updated posterior of hidden node states resulting from actively monitoring the network. Since traditional epidemic thresholds are derived using observation independent Markov chains, the threshold of the posterior should more accurately model the true phase transition of a network. The adaptive tracking framework and epidemic threshold should provide insight into modeling the dynamic response of the updated posterior to active intervention and control policies while monitoring modern complex networks.
Adaptive cluster synchronization of directed complex networks with time delays.
Heng Liu
Full Text Available This paper studied the cluster synchronization of directed complex networks with time delays. It is different from undirected networks, the coupling configuration matrix of directed networks cannot be assumed as symmetric or irreducible. In order to achieve cluster synchronization, this paper uses an adaptive controller on each node and an adaptive feedback strategy on the nodes which in-degree is zero. Numerical example is provided to show the effectiveness of main theory. This method is also effective when the number of clusters is unknown. Thus, it can be used in the community recognizing of directed complex networks.
Robust adaptive neural network control with supervisory controller
张天平; 梅建东
2004-01-01
The problem of direct adaptive neural network control for a class of uncertain nonlinear systems with unknown constant control gain is studied in this paper. Based on the supervisory control strategy and the approximation capability of multilayer neural networks (MNNs), a novel design scheme of direct adaptive neural network controller is proposed.The adaptive law of the adjustable parameter vector and the matrix of weights in the neural networks and the gain of sliding mode control term to adaptively compensate for the residual and the approximation error of MNNs is determined by using a Lyapunov method. The approach does not require the optimal approximation error to be square-integrable or the supremum of the optimal approximation error to be known. By theoretical analysis, the closed-loop control system is proven to be globally stable in the sense that all signals involved are bounded, with tracking error converging to zero.Simulation results demonstrate the effectiveness of the approach.
Generalized join-hemimorphisms on Boolean algebras
Sergio Celani
2003-01-01
We introduce the notions of generalized join-hemimorphism and generalized Boolean relation as an extension of the notions of join-hemimorphism and Boolean relation, respectively. We prove a duality between these two notions. We will also define a generalization of the notion of Boolean algebra with operators by considering a finite family of Boolean algebras endowed with a generalized join-hemimorphism. Finally, we define suitable notions of subalgebra, congruences, Boole...
On the Performance of Adaptive Modulation in Cognitive Radio Networks
Foukalas, F.; Karetsos, G. T.
2013-01-01
We study the performance of cognitive radio networks (CRNs) when incorporating adaptive modulation at the physical layer. Three types of CRNs are considered, namely opportunistic spectrum access (OSA), spectrum sharing (SS) and sensing-based SS. We obtain closed-form expressions for the average spectral efficiency achieved at the secondary network and the optimal power allocation for both continuous and discrete rate types of adaptive modulation assuming perfect channel state information. The...
Boolean Operations on Conic Polygons
Yong-Xi Gong; Yu Liu; Lun Wu; Yu-Bo Xie
2009-01-01
An algorithm for Boolean operations on conic polygons is proposed. Conic polygons are polygons consisting of conic segments or bounded conics with directions. Preliminaries of Boolean operations on general polygons are presented. In our algorithm, the intersection points and the topological relationships between two conic polygons are computed. Boundaries are obtained by tracking path and selecting uncrossed boundaries following rule tables to build resulting conic polygons.We define a set of rules for the intersection, union, and subtraction operations on conic polygons. The algorithm considers degeneration cases such as homology, complement, interior, and exterior. The algorithm is also evaluated and implemented.
Adaptive nonlinear control using input normalized neural networks
An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small
Traffic flow on realistic road networks with adaptive traffic lights
de Gier, Jan; Rojas, Omar
2010-01-01
We present a model of traffic flow on generic urban road networks based on cellular automata. We apply this model to an existing road network in the Australian city of Melbourne, using empirical data as input. For comparison, we also apply this model to a square-grid network using hypothetical input data. On both networks we compare the effects of non-adative vs adaptive traffic lights, in which instantaneous traffic state information feeds back into the traffic signal schedule. We observe that not only do adaptive traffic lights result in better averages of network observables, they also lead to significantly smaller fluctuations in these observables. We furthermore compare two different systems of adaptive traffic signals, one which is informed by the traffic state on both upstream and downstream links, and one which is informed by upstream links only. We find that, in general, the total travel time is smallest when using the joint upstream-downstream control strategy.
Stochastic analysis of epidemics on adaptive time varying networks
Kotnis, Bhushan; Kuri, Joy
2013-06-01
Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an “adaptive threshold,” i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.
Linking Individual and Collective Behavior in Adaptive Social Networks
Pinheiro, Flávio L.; Santos, Francisco C.; Pacheco, Jorge M.
2016-03-01
Adaptive social structures are known to promote the evolution of cooperation. However, up to now the characterization of the collective, population-wide dynamics resulting from the self-organization of individual strategies on a coevolving, adaptive network has remained unfeasible. Here we establish a (reversible) link between individual (micro)behavior and collective (macro)behavior for coevolutionary processes. We demonstrate that an adaptive network transforms a two-person social dilemma locally faced by individuals into a collective dynamics that resembles that associated with an N -person coordination game, whose characterization depends sensitively on the relative time scales between the entangled behavioral and network evolutions. In particular, we show that the faster the relative rate of adaptation of the network, the smaller the critical fraction of cooperators required for cooperation to prevail, thus establishing a direct link between network adaptation and the evolution of cooperation. The framework developed here is general and may be readily applied to other dynamical processes occurring on adaptive networks, notably, the spreading of contagious diseases or the diffusion of innovations.
Implementation of an Adaptive Learning System Using a Bayesian Network
Yasuda, Keiji; Kawashima, Hiroyuki; Hata, Yoko; Kimura, Hiroaki
2015-01-01
An adaptive learning system is proposed that incorporates a Bayesian network to efficiently gauge learners' understanding at the course-unit level. Also, learners receive content that is adapted to their measured level of understanding. The system works on an iPad via the Edmodo platform. A field experiment using the system in an elementary school…
Boolean modeling in systems biology: an overview of methodology and applications
Mathematical modeling of biological processes provides deep insights into complex cellular systems. While quantitative and continuous models such as differential equations have been widely used, their use is obstructed in systems wherein the knowledge of mechanistic details and kinetic parameters is scarce. On the other hand, a wealth of molecular level qualitative data on individual components and interactions can be obtained from the experimental literature and high-throughput technologies, making qualitative approaches such as Boolean network modeling extremely useful. In this paper, we build on our research to provide a methodology overview of Boolean modeling in systems biology, including Boolean dynamic modeling of cellular networks, attractor analysis of Boolean dynamic models, as well as inferring biological regulatory mechanisms from high-throughput data using Boolean models. We finally demonstrate how Boolean models can be applied to perform the structural analysis of cellular networks. This overview aims to acquaint life science researchers with the basic steps of Boolean modeling and its applications in several areas of systems biology. (paper)
Adaptation to synchronization in phase-oscillator networks
Arizmendi, Fernando; Zanette, Damian H.
2008-01-01
We introduce an adaptation algorithm by which an ensemble of coupled oscillators with attractive and repulsive interactions is induced to adopt a prescribed synchronized state. While the performance of adaptation is controlled by measuring a macroscopic quantity, which characterizes the achieved degree of synchronization, adaptive changes are introduced at the microscopic level of the interaction network, by modifying the configuration of repulsive interactions. This scheme emulates the disti...
Collaborative Trust Networks in Engineering Design Adaptation
Atkinson, Simon Reay; Maier, Anja; Caldwell, Nicholas;
2011-01-01
); applying the Change Prediction Method (CPM) tool. It posits the idea of the ‘Networks-in-Being’ with varying individual and collective characteristics. [Social] networks are considered to facilitate information exchange between actors. At the same time, networks failing to provide trusted-information can...... hinder effective communication and collaboration. Different combinations of trust may therefore improve or impair the likelihood of information flow, transfer and subsequent action (cause and effect). This paper investigates how analysing different types of network-structures-in-being can support......Within organisations, decision makers have to rely on collaboration with other actors from different disciplines working within highly dynamic and distributed associated networks of varying size and scales. This paper develops control and influence networks within Design Structure Matrices (DSM...
Adaptive optimization and control using neural networks
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
This paper presents a new robust adaptive synchronization method for a class of uncertain dynamical complex networks with network failures and coupling time-varying delays. Adaptive schemes are proposed to adjust controller parameters for the faulty network compensations, as well as to estimate the upper and lower bounds of delayed state errors and perturbations to compensate the effects of delay and perturbation on-line without assuming symmetry or irreducibility of networks. It is shown that, through Lyapunov stability theory, distributed adaptive controllers constructed by the adaptive schemes are successful in ensuring the achievement of asymptotic synchronization of networks in the present of faulty and delayed networks, and perturbation inputs. A Chua's circuit network example is finally given to show the effectiveness of the proposed synchronization criteria. (general)
Adaptive swarm-based routing in communication networks
吕勇; 赵光宙; 苏凡军; 历小润
2004-01-01
Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features, including adaptation, robustness and distributed, decentralized nature, which are well suited for routing in modern communication networks. This paper describes an adaptive swarm-based routing algorithm that increases convergence speed, reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum.Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.
Adaptive swarm-based routing in communication networks
吕勇; 赵光宙; 苏凡军; 历小润
2004-01-01
Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features,including adaptation,robustness and distributed,decentralized nature,which are well suited for routing in modern communication networks.This paper describes an adaptive swarm-based routing algorithm that increases convergence speed,reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum.Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.
Model for cascading failures with adaptive defense in complex networks
This paper investigates cascading failures in networks by considering interplay between the flow dynamic and the network topology, where the fluxes exchanged between a pair of nodes can be adaptively adjusted depending on the changes of the shortest path lengths between them. The simulations on both an artificially created scale-free network and the real network structure of the power grid reveal that the adaptive adjustment of the fluxes can drastically enhance the robustness of complex networks against cascading failures. Particularly, there exists an optimal region where the propagation of the cascade is significantly suppressed and the fluxes supported by the network are maximal. With this understanding, a costless strategy of defense for preventing cascade breakdown is proposed. It is shown to be more effective for suppressing the propagation of the cascade than the recent proposed strategy of defense based on the intentional removal of nodes. (general)
Adaptive Neurons For Artificial Neural Networks
Tawel, Raoul
1990-01-01
Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.
Network-topology-adaptive quantum conference protocols
Zhang Sheng; Wang Jian; Tang Chao-Jing; Zhang Quan
2011-01-01
As an important application of the quantum network communication,quantum multiparty conference has made multiparty secret communication possible.Previous quantum multiparty conference schemes based on quantum data encryption are insensitive to network topology.However,the topology of the quantum network significantly affects the communication efficiency,e.g.,parallel transmission in a channel with limited bandwidth.We have proposed two distinctive protocols,which work in two basic network topologies with efficiency higher than the existing ones.We first present a protocol which works in the reticulate network using Greeberger-Horne-Zeilinger states and entanglement swapping.Another protocol,based on quantum multicasting with quantum data compression,which can improve the efficiency of the network,works in the star-like network.The security of our protocols is guaranteed by quantum key distribution and one-time-pad encryption.In general,the two protocols can be applied to any quantum network where the topology can be equivalently transformed to one of the two structures we propose in our protocols.
A Neural Network for Generating Adaptive Lessons
Hassina Seridi-Bouchelaghem; Toufik Sari; Mokhtar Sellami
2005-01-01
Traditional sequencing technology developed in the field of intelligent tutoring systems have not find an immediate place in large-scale Web-based education. This study investigates the use of computational intelligence for adaptive lesson generation in a distance learning environment over the Web. An approach for adaptive pedagogical hypermedia document generation is proposed and implemented in a prototype called KnowledgeClass. This approach is based on a specialized art...
Boolean-Valued Belief Functions
Kramosil, Ivan
2002-01-01
Roč. 31, č. 2 (2002), s. 153-181. ISSN 0308-1079 R&D Projects: GA AV ČR IAA1030803 Institutional research plan: AV0Z1030915 Keywords : Dempster-Schafer theory * Boolean algebra Subject RIV: BA - General Mathematics Impact factor: 0.241, year: 2002
Cardinal invariants on Boolean algebras
Monk, J Donald
2009-01-01
Deals with cardinal number valued functions defined for any Boolean algebra. This title considers the behavior of these functions under algebraic operations such as products, free products, ultraproducts, and their relationships to one another. It covers topics such as ultraproducts and Fedorchukis theorem
Evolutionary Design of Boolean Functions
WANG Zhang-yi; ZHANG Huan-guo; QIN Zhong-ping; MENG Qing-shu
2005-01-01
We use evolutionary computing to synthesize Boolean functions randomly. By using specific crossover and mutation operator in evolving process and modifying search space and fitness function, we get some high non-linearity functions which have other good cryptography characteristics such as autocorrelation etc. Comparing to other heuristic search techniques, evolutionary computing approach is more effective because of global search strategy and implicit parallelism.
TCP-Adaptive in High Speed Long Distance Networks
Quan Liu
2014-02-01
Full Text Available With the development of high performance computing and increasing of network bandwidth, more and more applications require fast data transfer over high-speed long-distance networks. Research shows that the standard TCP Reno cannot fulfill the requirement of fast transfer of massive data due to its conservative congestion control mechanism. Some works have been proposed to improve the TCP throughput performance using more aggressive window increasing tactics and obtain substantial achievements. However, they cannot be strictly proved to be comprehensively suitable for high-speed complex network environments. In this paper, we propose TCP-Adaptive, an adaptive congestion control algorithm adjusting the increasing congestion window dynamically. The algorithm improves logarithmic detection procedure for available bandwidth in the flow path by distinguishing the first detection in congestion avoidance and retransmission timeout. On the other hand, an adaptive control algorithm is proposed to achieve better performance in high-speed long-distance networks. The algorithm uses round trip time (RTT variations to predict the congestion trends to update the increments of congestion window. Simulations verify the property of TCP-Adaptive and show satisfying performance in throughput, RTT fairness aspects over high-speed long-distance networks. Especially in sporadic loss environment, TCP-Adaptive shows a significant adaptability with the variations of link quality
Temporal and structural heterogeneities emerging in adaptive temporal networks
Aoki, Takaaki; Gross, Thilo
2015-01-01
We introduce a model of adaptive temporal networks whose evolution is regulated by an interplay between node activity and dynamic exchange of information through links. We study the model by using a master equation approach. Starting from a homogeneous initial configuration, we show that temporal and structural heterogeneities, characteristic of real-world networks, spontaneously emerge. This theoretically tractable model thus contributes to the understanding of the dynamics of human activity and interaction networks.
Temporal and structural heterogeneities emerging in adaptive temporal networks
Aoki, Takaaki; Rocha, Luis E. C.; Gross, Thilo
2016-04-01
We introduce a model of adaptive temporal networks whose evolution is regulated by an interplay between node activity and dynamic exchange of information through links. We study the model by using a master equation approach. Starting from a homogeneous initial configuration, we show that temporal and structural heterogeneities, characteristic of real-world networks, spontaneously emerge. This theoretically tractable model thus contributes to the understanding of the dynamics of human activity and interaction networks.
Adaptive Energy-Aware Gathering Strategy for Wireless Sensor Networks
E M Saad; Awadalla, M. H.; R. R. Darwish
2009-01-01
Energy hole problem is considered one of the most severe threats in wireless sensor networks. In this paper the idea of exploiting sink mobility for the purpose of culling the energy hole problem in hierarchical large-scale wireless sensor networks based on bees algorithm is presented. In the proposed scheme, a mobile sink equipped with a powerful transceiver and battery, traverses the entire field, and periodically gathers data from network cluster heads. The mobile sink follows an adaptive ...
Time scales in evolutionary game on adaptive networks
Most previous studies concerning spatial games have assumed strategy updating occurs with a fixed ratio relative to interactions. We here set up a coevolutionary model to investigate how different ratio affects the evolution of cooperation on adaptive networks. Simulation results demonstrate that cooperation can be significantly enhanced under our rewiring mechanism, especially with slower natural selection. Meanwhile, slower selection induces larger network heterogeneity. Strong selection contracts the parameter area where cooperation thrives. Therefore, cooperation prevails whenever individuals are offered enough chances to adapt to the environment. Robustness of the results has been checked under rewiring cost or varied networks.
Time scales in evolutionary game on adaptive networks
Cong, Rui, E-mail: congrui0000@126.com [School of Mechano-Electronic Engineering, Xidian University, Xi' an (China); Wu, Te; Qiu, Yuan-Ying [School of Mechano-Electronic Engineering, Xidian University, Xi' an (China); Wang, Long [School of Mechano-Electronic Engineering, Xidian University, Xi' an (China); Center for Systems and Control, State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing (China)
2014-02-01
Most previous studies concerning spatial games have assumed strategy updating occurs with a fixed ratio relative to interactions. We here set up a coevolutionary model to investigate how different ratio affects the evolution of cooperation on adaptive networks. Simulation results demonstrate that cooperation can be significantly enhanced under our rewiring mechanism, especially with slower natural selection. Meanwhile, slower selection induces larger network heterogeneity. Strong selection contracts the parameter area where cooperation thrives. Therefore, cooperation prevails whenever individuals are offered enough chances to adapt to the environment. Robustness of the results has been checked under rewiring cost or varied networks.
Adaptive Network Dynamics and Evolution of Leadership in Collective Migration
Pais, Darren
2013-01-01
The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcations with respect to investment cost explains the observed hysteretic effect associated with recovery of migration in fragmented environments. Further, we show a minimum connectivity threshold above which there is evolutionary branching into leader and follower populations. For small populations, we show how the topology of the underlying social interaction network influences the emergence and location of leaders in the adaptive system. Our model and analysis can describe other adaptive network dynamics involving collective...
Time-adaptive and history-adaptive multicriterion routing in stochastic, time-dependent networks
Pretolani, Daniele; Nielsen, Lars Relund; Andersen, Kim Allan;
2009-01-01
We compare two different models for multicriterion routing in stochastic time-dependent networks: the classic "time-adaptive'' model and the more flexible "history-adaptive'' one. We point out several properties of the sets of efficient solutions found under the two models. We also devise a method...
Adaptive intelligent power systems: Active distribution networks
Electricity networks are extensive and well established. They form a key part of the infrastructure that supports industrialised society. These networks are moving from a period of stability to a time of potentially major transition, driven by a need for old equipment to be replaced, by government policy commitments to cleaner and renewable sources of electricity generation, and by change in the power industry. This paper looks at moves towards active distribution networks. The novel transmission and distribution systems of the future will challenge today's system designs. They will cope with variable voltages and frequencies, and will offer more flexible, sustainable options. Intelligent power networks will need innovation in several key areas of information technology. Active control of flexible, large-scale electrical power systems is required. Protection and control systems will have to react to faults and unusual transient behaviour and ensure recovery after such events. Real-time network simulation and performance analysis will be needed to provide decision support for system operators, and the inputs to energy and distribution management systems. Advanced sensors and measurement will be used to achieve higher degrees of network automation and better system control, while pervasive communications will allow networks to be reconfigured by intelligent systems
Implementing Boolean Matrix Factorization
Neruda, Roman; Snášel, V.; Platoš, J.; Krömer, P.; Húsek, Dušan; Frolov, A. A.
Vol. Part I. Berlin : Springer, 2008 - (Kůrková, V.; Neruda, R.; Koutník, J.), s. 543-552 ISBN 978-3-540-87535-2. - (Lecture Notes in Computer Science. 5163). [ICANN 2008. International Conference on Artificial Neural Network s /18./. Prague (CZ), 03.09.2008-06.09.2008] Institutional research plan: CEZ:AV0Z10300504 Keywords : factor analysis * genetic algorithm * neural network s Subject RIV: IN - Informatics, Computer Science
Concurrent enhancement of percolation and synchronization in adaptive networks
Eom, Young-Ho; Boccaletti, Stefano; Caldarelli, Guido
2016-06-01
Co-evolutionary adaptive mechanisms are not only ubiquitous in nature, but also beneficial for the functioning of a variety of systems. We here consider an adaptive network of oscillators with a stochastic, fitness-based, rule of connectivity, and show that it self-organizes from fragmented and incoherent states to connected and synchronized ones. The synchronization and percolation are associated to abrupt transitions, and they are concurrently (and significantly) enhanced as compared to the non-adaptive case. Finally we provide evidence that only partial adaptation is sufficient to determine these enhancements. Our study, therefore, indicates that inclusion of simple adaptive mechanisms can efficiently describe some emergent features of networked systems’ collective behaviors, and suggests also self-organized ways to control synchronization and percolation in natural and social systems.
Controling contagious processes on temporal networks via adaptive rewiring
Belik, Vitaly; Hövel, Philipp
2015-01-01
We consider recurrent contagious processes on a time-varying network. As a control procedure to mitigate the epidemic, we propose an adaptive rewiring mechanism for temporary isolation of infected nodes upon their detection. As a case study, we investigate the network of pig trade in Germany. Based on extensive numerical simulations for a wide range of parameters, we demonstrate that the adaptation mechanism leads to a significant extension of the parameter range, for which most of the index nodes (origins of the epidemic) lead to vanishing epidemics. We find that diseases with detection times around a week and infectious periods up to 3 months can be effectively controlled. Furthermore the performance of adaptation is very heterogeneous with respect to the index node. We identify index nodes that are most responsive to the adaptation strategy and quantify the success of the proposed adaptation scheme in dependence on the infectious period and detection times.
Network Reliability Algorithm Based on Pathset Matrix and Boolean Operation%基于路集矩阵与布尔运算的网络可靠度算法
高会生; 展敬宇; 王博颖; 李潇睿
2012-01-01
This paper analyzes the network reliability algorithm based on pathset matrix, and there exists a serious combination explosion problem in this algorithm. Aiming at this problem, it proposes a network reliability algorithm based on pathset matrix and boolean operation. The concept of bit vector is introduced. In addition, the pre-process of special pathsets and count of all-one bit vectors are also implied. Experimental results show that it not only increases the memory utilization, reduce the redundancy but also relieve the combination explosion problem in some degree.%分析基于路集矩阵与布尔运算的网络可靠度算法,指出其存在组合爆炸问题.为此,提出一种改进算法,引入位矢量以减少内存需求,对特殊路集进行预处理并统计全1位矢量.实验结果表明,改进算法可提高内存利用率、减少冗余运算,能在一定程度上缓解组合爆炸问题.
Adaptive computational resource allocation for sensor networks
WANG Dian-hong; FEI E; YAN Yu-jie
2008-01-01
To efficiently utilize the limited computational resource in real-time sensor networks, this paper focu-ses on the challenge of computational resource allocation in sensor networks and provides a solution with the method of economies. It designs a mieroeconomic system in which the applications distribute their computational resource consumption across sensor networks by virtue of mobile agent. Further, it proposes the market-based computational resource allocation policy named MCRA which satisfies the uniform consumption of computational energy in network and the optimal division of the single computational capacity for multiple tasks. The simula-tion in the scenario of target tracing demonstrates that MCRA realizes an efficient allocation of computational re-sources according to the priority of tasks, achieves the superior allocation performance and equilibrium perform-ance compared to traditional allocation policies, and ultimately prolongs the system lifetime.
Adaptive Capacity Management in Bluetooth Networks
Son, L.T.
With the Internet and mobile wireless development, accelerated by high-speed and low cost VLSI device evolution, short range wireless communications have become more and more popular, especially Bluetooth. Bluetooth is a new short range radio technology that promises to be very convenient, low...... power, and low cost mobile ad hoc solution for the global interconnection of all mobile devices. To implement Bluetooth network as a true mobile ad hoc wireless network operating in short radio range, highly dynamic network environment, low power, and scarce resources, many new research challenges occur......, such as limited wireless bandwidth operation, routing, scheduling, network control, etc. Currently Bluetooth specification particularly does not describe in details about how to implement Quality of Service and Resource Management in Bluetooth protocol stacks. These issues become significant, when the number...
Bifurcation Analysis of Equilibria in Competitive Logistic Networks with Adaptation
Raimondi, A.; Tebaldi, C.
2008-04-01
A general n-node network is considered for which, in absence of interactions, each node is governed by a logistic equation. Interactions among the nodes take place in the form of competition, which also includes adaptive abilities through a (short term) memory effect. As a consequence the dynamics of the network is governed by a system of n2 nonlinear ordinary differential equations. As a first step, equilibria and their stability are investigated analytically for the general network in dependence of the relevant parameters, namely the strength of competition, the adaptation rate and the network size. The existence of classes of invariant subspaces, related to symmetries, allows the introduction of a reduced model, four dimensional, where n appears as a parameter, which give full account of existence and stability for the equilibria in the network.
Scalable Lunar Surface Networks and Adaptive Orbit Access
Wang, Xudong
2015-01-01
Teranovi Technologies, Inc., has developed innovative network architecture, protocols, and algorithms for both lunar surface and orbit access networks. A key component of the overall architecture is a medium access control (MAC) protocol that includes a novel mechanism of overlaying time division multiple access (TDMA) and carrier sense multiple access with collision avoidance (CSMA/CA), ensuring scalable throughput and quality of service. The new MAC protocol is compatible with legacy Institute of Electrical and Electronics Engineers (IEEE) 802.11 networks. Advanced features include efficiency power management, adaptive channel width adjustment, and error control capability. A hybrid routing protocol combines the advantages of ad hoc on-demand distance vector (AODV) routing and disruption/delay-tolerant network (DTN) routing. Performance is significantly better than AODV or DTN and will be particularly effective for wireless networks with intermittent links, such as lunar and planetary surface networks and orbit access networks.
Explosive Synchronization and Emergence of Assortativity on Adaptive Networks
JIANG Hui-Jun; WU Hao; HOU Zhong-Huai
2011-01-01
@@ We report an explosive transition from incoherence to synchronization of coupled phase oscillators on adaptive networks,following an Achlioptas process based on dynamic clustering information.During each adaptive step of the network topology,a portion of the links is randomly removed and the same amount of new links is generated following the so-called product rules(PRs) applied to the dynamic clusters.Particularly,two types of PRs are considered,namely,the min-PR and max-PR.We demonstrate that the synchronization transition becomes explosive in both cases.Interestingly,we find that the min-PR rule can lead to disassortativity of the network topology,while the max-PR rule leads to assortativity.%We report an explosive transition from incoherence to synchronization of coupled phase oscillators on adaptive networks, following an Achlioptas process based on dynamic clustering information. During each adaptive step of the network topology, a portion of the links is randomly removed and the same amount of new links is generated following the so-called product rules (PRs) applied to the dynamic clusters. Particularly, two types of PRs are considered, namely, the min-PR and max-PR. We demonstrate that the synchronization transition becomes explosive in both cases. Interestingly, we find that the min-PR rule can lead to disassortativity of the network topology, while the max-PR rule leads to assortativity.
Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism
Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu;
2012-01-01
known transcription regulation network. Interactions across multiple levels of regulation were involved in adaptive changes that could also be achieved by controlling single genes. Our analysis suggests that global trade-offs and evolutionary constraints provide incentives to favor complex control......Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and...... model-based data analyses of dynamic transcript, protein, and metabolite abundances and promoter activities. Adaptation to malate was rapid and primarily controlled posttranscriptionally compared with the slow, mainly transcriptionally controlled adaptation to glucose that entailed nearly half of the...
Analysis of adaptive algorithms for an integrated communication network
Reed, Daniel A.; Barr, Matthew; Chong-Kwon, Kim
1985-01-01
Techniques were examined that trade communication bandwidth for decreased transmission delays. When the network is lightly used, these schemes attempt to use additional network resources to decrease communication delays. As the network utilization rises, the schemes degrade gracefully, still providing service but with minimal use of the network. Because the schemes use a combination of circuit and packet switching, they should respond to variations in the types and amounts of network traffic. Also, a combination of circuit and packet switching to support the widely varying traffic demands imposed on an integrated network was investigated. The packet switched component is best suited to bursty traffic where some delays in delivery are acceptable. The circuit switched component is reserved for traffic that must meet real time constraints. Selected packet routing algorithms that might be used in an integrated network were simulated. An integrated traffic places widely varying workload demands on a network. Adaptive algorithms were identified, ones that respond to both the transient and evolutionary changes that arise in integrated networks. A new algorithm was developed, hybrid weighted routing, that adapts to workload changes.
A candidate multimodal functional genetic network for thermal adaptation
Katharina C. Wollenberg Valero
2014-09-01
Full Text Available Vertebrate ectotherms such as reptiles provide ideal organisms for the study of adaptation to environmental thermal change. Comparative genomic and exomic studies can recover markers that diverge between warm and cold adapted lineages, but the genes that are functionally related to thermal adaptation may be difficult to identify. We here used a bioinformatics genome-mining approach to predict and identify functions for suitable candidate markers for thermal adaptation in the chicken. We first established a framework of candidate functions for such markers, and then compiled the literature on genes known to adapt to the thermal environment in different lineages of vertebrates. We then identified them in the genomes of human, chicken, and the lizard Anolis carolinensis, and established a functional genetic interaction network in the chicken. Surprisingly, markers initially identified from diverse lineages of vertebrates such as human and fish were all in close functional relationship with each other and more associated than expected by chance. This indicates that the general genetic functional network for thermoregulation and/or thermal adaptation to the environment might be regulated via similar evolutionarily conserved pathways in different vertebrate lineages. We were able to identify seven functions that were statistically overrepresented in this network, corresponding to four of our originally predicted functions plus three unpredicted functions. We describe this network as multimodal: central regulator genes with the function of relaying thermal signal (1, affect genes with different cellular functions, namely (2 lipoprotein metabolism, (3 membrane channels, (4 stress response, (5 response to oxidative stress, (6 muscle contraction and relaxation, and (7 vasodilation, vasoconstriction and regulation of blood pressure. This network constitutes a novel resource for the study of thermal adaptation in the closely related nonavian reptiles and
Adaptive Media Access Control for Energy Harvesting - Wireless Sensor Networks
Fafoutis, Xenofon; Dragoni, Nicola
2012-01-01
ODMAC (On-Demand Media Access Control) is a recently proposed MAC protocol designed to support individual duty cycles for Energy Harvesting — Wireless Sensor Networks (EH-WSNs). Individual duty cycles are vital for EH-WSNs, because they allow nodes to adapt their energy consumption to the ever...... three key properties of EH-WSNs: adaptability of energy consumption, distributed energy-aware load balancing and support for different application-specific requirements....
Scalable Harmonization of Complex Networks With Local Adaptive Controllers
Kárný, Miroslav; Herzallah, R.
-, - (2016). ISSN 2168-2216 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Adaptive control * Adaptive estimation * Bayes methods * Complex networks * Decentralized control * Feedback * Feedforward systems * Recursive estimation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.699, year: 2014 http://library.utia.cas.cz/separaty/2016/AS/karny-0457337.pdf
Adaptive Network Dynamics and Evolution of Leadership in Collective Migration
Pais, Darren; Leonard, Naomi Ehrich
2013-01-01
The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcatio...
Cost-Optimal Execution of Trees of Boolean Operators with Shared Streams
Casanova, Henri; Lim, Lipyeow; Robert, Yves; Vivien, Frédéric; Zaidouni, Dounia
2013-01-01
The processing of queries expressed as trees of boolean operators applied to predicates on sensor data streams has several applications in mobile computing. Sensor data must be retrieved from the sensors to a query processing device, such as a smartphone, over one or more network interfaces. Retrieving a data item incurs a cost, e.g., an energy expense that depletes the smartphone's battery. Since the query tree contains boolean operators, part of the tree can be shortcircuited depending on t...
Radio propagation and adaptive antennas for wireless communication networks
Blaunstein, Nathan
2014-01-01
Explores novel wireless networks beyond 3G, and advanced 4G technologies, such as MIMO, via propagation phenomena and the fundamentals of adapted antenna usage.Explains how adaptive antennas can improve GoS and QoS for any wireless channel, with specific examples and applications in land, aircraft and satellite communications.Introduces new stochastic approach based on several multi-parametric models describing various terrestrial scenarios, which have been experimentally verified in different environmental conditionsNew chapters on fundamentals of wireless networks, cellular and non-cellular,
Boolean Models of Biological Processes Explain Cascade-Like Behavior.
Chen, Hao; Wang, Guanyu; Simha, Rahul; Du, Chenghang; Zeng, Chen
2016-01-01
Biological networks play a key role in determining biological function and therefore, an understanding of their structure and dynamics is of central interest in systems biology. In Boolean models of such networks, the status of each molecule is either "on" or "off" and along with the molecules interact with each other, their individual status changes from "on" to "off" or vice-versa and the system of molecules in the network collectively go through a sequence of changes in state. This sequence of changes is termed a biological process. In this paper, we examine the common perception that events in biomolecular networks occur sequentially, in a cascade-like manner, and ask whether this is likely to be an inherent property. In further investigations of the budding and fission yeast cell-cycle, we identify two generic dynamical rules. A Boolean system that complies with these rules will automatically have a certain robustness. By considering the biological requirements in robustness and designability, we show that those Boolean dynamical systems, compared to an arbitrary dynamical system, statistically present the characteristics of cascadeness and sequentiality, as observed in the budding and fission yeast cell- cycle. These results suggest that cascade-like behavior might be an intrinsic property of biological processes. PMID:26821940
QoS-Aware Error Recovery in Wireless Body Sensor Networks Using Adaptive Network Coding
Mohammad Abdur Razzaque
2014-12-01
Full Text Available Wireless body sensor networks (WBSNs for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS, in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network’s QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts.
Adaptive Data Transmission in Multimedia Networks
Manimegalai Parry
2005-01-01
Full Text Available This study suggests a method where the packet size of each source is adjusted according to the network bandwidth. A controller is used to trace the data transmission rate at the router. An algorithm is developed and coded in Tool Command Language. Simulation is performed on NS-2 using 4 different test cases and the results show that the proposed algorithm avoids router queue overflow.
Study on Adaptive Control with Neural Network Compensation
单剑锋; 黄忠华; 崔占忠
2004-01-01
A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify pareters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed.
Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks
Jorgensen, Charles C.
1997-01-01
A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.
An Adaptive Complex Network Model for Brain Functional Networks
Gomez Portillo, Ignacio J.; Gleiser, Pablo M.
2009-01-01
Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show diffe...
Maximizing Quality and Performance of Network Through Adaptive Traffic Engineering
Sameera Pallavi; Ch.Sandeep; P.Pramod Kumar
2013-01-01
Network management systems are to handle traffic dynamics in order to ensure congestion free network with highest throughput. IP environments are able to provide simple facilities for forwarding and routing packets. However, in presence of dynamic traffic conditions efficient management of resources is yet to be achieved. Recently Ning Wang et al. proposed a traffic engineering system which can ynamically adapt to traffic conditions with the help of virtual routing topologies. It has two majo...
Adaptive projective synchronization with different scaling factors in networks
Guo Liu-Xiao; Xu Zhen-Yuan; Hu Man-Feng
2008-01-01
We study projective synchronization with different scaling factors (PSDF) in N coupled chaotic systems networks.By using the adaptive linear control,some sufficient criteria for the PSDF in symmetrical and asymmetrical coupled networks are separately given based on the Lyapunov function method and the left eigenvalue theory.Numerical simulations for a generalized chaotic unified system are illustrated to verify the theoretical results.
Optimization of an adaptive neural network to predict breathing
Murphy, Martin J; Pokhrel, Damodar
2008-01-01
Purpose: To determine the optimal configuration and performance of an adaptive feed forward neural network filter to predict breathing in respiratory motion compensation systems for external beam radiation therapy. Method and Materials: A two-layer feed forward neural network was trained to predict future breathing amplitudes for 27 recorded breathing histories. The prediction intervals ranged from 100 to 500 ms. The optimal sampling frequency, number of input samples, training rate, and numb...
Quantum algorithms for testing Boolean functions
Erika Andersson
2010-06-01
Full Text Available We discuss quantum algorithms, based on the Bernstein-Vazirani algorithm, for finding which variables a Boolean function depends on. There are 2^n possible linear Boolean functions of n variables; given a linear Boolean function, the Bernstein-Vazirani quantum algorithm can deterministically identify which one of these Boolean functions we are given using just one single function query. The same quantum algorithm can also be used to learn which input variables other types of Boolean functions depend on, with a success probability that depends on the form of the Boolean function that is tested, but does not depend on the total number of input variables. We also outline a procedure to futher amplify the success probability, based on another quantum algorithm, the Grover search.
Social Networking Adapted for Distributed Scientific Collaboration
Karimabadi, Homa
2012-01-01
Share is a social networking site with novel, specially designed feature sets to enable simultaneous remote collaboration and sharing of large data sets among scientists. The site will include not only the standard features found on popular consumer-oriented social networking sites such as Facebook and Myspace, but also a number of powerful tools to extend its functionality to a science collaboration site. A Virtual Observatory is a promising technology for making data accessible from various missions and instruments through a Web browser. Sci-Share augments services provided by Virtual Observatories by enabling distributed collaboration and sharing of downloaded and/or processed data among scientists. This will, in turn, increase science returns from NASA missions. Sci-Share also enables better utilization of NASA s high-performance computing resources by providing an easy and central mechanism to access and share large files on users space or those saved on mass storage. The most common means of remote scientific collaboration today remains the trio of e-mail for electronic communication, FTP for file sharing, and personalized Web sites for dissemination of papers and research results. Each of these tools has well-known limitations. Sci-Share transforms the social networking paradigm into a scientific collaboration environment by offering powerful tools for cooperative discourse and digital content sharing. Sci-Share differentiates itself by serving as an online repository for users digital content with the following unique features: a) Sharing of any file type, any size, from anywhere; b) Creation of projects and groups for controlled sharing; c) Module for sharing files on HPC (High Performance Computing) sites; d) Universal accessibility of staged files as embedded links on other sites (e.g. Facebook) and tools (e.g. e-mail); e) Drag-and-drop transfer of large files, replacing awkward e-mail attachments (and file size limitations); f) Enterprise-level data and
Boolean Reasoning with Graphs of Partitions
Goossens, Daniel
2010-01-01
version longue du papier court "A Dynamic Boolean Knowledge Base" accepté à ICTAI 2010. This paper presents an implemented architecture for easy learning, reorganizing and navigation into a Boolean knowledge base. As the base grows with new definitions and constraints, it is normalized by the closure of a completion operator. This normalization allows arbitrary formats for Boolean expressions. It ensures basic reasoning abilities and spontaneously organizes intermingled taxonomies of conce...
Model Checking of Boolean Process Models
Schneider, Christoph; Wehler, Joachim
2011-01-01
In the field of Business Process Management formal models for the control flow of business processes have been designed since more than 15 years. Which methods are best suited to verify the bulk of these models? The first step is to select a formal language which fixes the semantics of the models. We adopt the language of Boolean systems as reference language for Boolean process models. Boolean systems form a simple subclass of coloured Petri nets. Their characteristics are low tokens to mode...
Estimation of Boolean Factor Analysis Performance by Informational Gain
Frolov, A.; Húsek, Dušan; Polyakov, P.Y.
Berlin : Springer, 2010 - (Snášel, V.; Szczepaniak, P.; Abraham, A.; Kacprzyk, J.), s. 83-94 ISBN 978-3-642-10686-6. - (Advances in Intelligent and Soft Computing. 67). [AWIC 2009. Atlantic Web Intelligence Conference /6./. Prague (CZ), 09.09.2009-11.09.2009] Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * informational gain * Hopfield-like network Subject RIV: IN - Informatics, Computer Science
Progress in Applications of Boolean Functions
Sasao, Tsutomu
2010-01-01
This book brings together five topics on the application of Boolean functions. They are 1. Equivalence classes of Boolean functions: The number of n-variable functions is large, even for values as small as n = 6, and there has been much research on classifying functions. There are many classifications, each with their own distinct merit. 2. Boolean functions for cryptography: The process of encrypting/decrypting plain text messages often depends on Boolean functions with specific properties. For example, highly nonlinear functions are valued because they are less susceptible to linear attacks.
An Adaptive Neural Network Model for Nonlinear Programming Problems
Xiang-sun Zhang; Xin-jian Zhuo; Zhu-jun Jing
2002-01-01
In this paper a canonical neural network with adaptively changing synaptic weights and activation function parameters is presented to solve general nonlinear programming problems. The basic part of the model is a sub-network used to find a solution of quadratic programming problems with simple upper and lower bounds. By sequentially activating the sub-network under the control of an external computer or a special analog or digital processor that adjusts the weights and parameters, one then solves general nonlinear programming problems. Convergence proof and numerical results are given.
Adaptive synchronization in an array of asymmetric coupled neural networks
Gao Ming; Cui Bao-Tong
2009-01-01
This paper investigates the global synchronization in an array of linearly coupled neural networks with constant and delayed coupling. By a simple combination of adaptive control and linear feedback with the updated laws, some sufficient conditions are derived for global synchronization of the coupled neural networks. The coupling configuration matrix is assumed to be asymmetric, which is more coincident with the realistic network. It is shown that the approaches developed here extend and improve the earlier works. Finally, numerical simulations are presented to demonstrate the effectiveness of the theoretical results.
High dynamic adaptive mobility network model and performance analysis
LIU Hui; ZHANG Jun
2008-01-01
Since mobile networks are not currently deployed on a large scale, research in this area is mostly by simulation. Among other simulation parameters, the mobility model plays a very important role in determining the protocol performance in MANET. Based on random direction mobility model, a high dynamic adaptive mo-bility network model is proposed in the paper. The algorithms and modeling are mainly studied and explained in detail. The technique keystone is that normal dis-tribution is combined with uniform distribution and inertial feedback control is combined with kinematics, through the adaptive control on nodes speed and pre-diction tracking on nodes routes, an adaptive model is designed, which can be used in simulations to produce realistic and dynamic network scenarios. It is the adaptability that nodes mobile parameters can be adjusted randomly in three-dimensional space. As a whole, colony mobility can show some rules. Such ran-dom movement processes as varied speed and dwells are simulated realistically. Such problems as sharp turns and urgent stops are smoothed well. The model can be adapted to not only common dynamic scenarios, but also high dynamic sce-narios. Finally, the mobility model performance is analyzed and validated based on random dynamic scenarios simulations.
In-network adaptation of SHVC video in software-defined networks
Awobuluyi, Olatunde; Nightingale, James; Wang, Qi; Alcaraz Calero, Jose Maria; Grecos, Christos
2016-04-01
Software Defined Networks (SDN), when combined with Network Function Virtualization (NFV) represents a paradigm shift in how future networks will behave and be managed. SDN's are expected to provide the underpinning technologies for future innovations such as 5G mobile networks and the Internet of Everything. The SDN architecture offers features that facilitate an abstracted and centralized global network view in which packet forwarding or dropping decisions are based on application flows. Software Defined Networks facilitate a wide range of network management tasks, including the adaptation of real-time video streams as they traverse the network. SHVC, the scalable extension to the recent H.265 standard is a new video encoding standard that supports ultra-high definition video streams with spatial resolutions of up to 7680×4320 and frame rates of 60fps or more. The massive increase in bandwidth required to deliver these U-HD video streams dwarfs the bandwidth requirements of current high definition (HD) video. Such large bandwidth increases pose very significant challenges for network operators. In this paper we go substantially beyond the limited number of existing implementations and proposals for video streaming in SDN's all of which have primarily focused on traffic engineering solutions such as load balancing. By implementing and empirically evaluating an SDN enabled Media Adaptation Network Entity (MANE) we provide a valuable empirical insight into the benefits and limitations of SDN enabled video adaptation for real time video applications. The SDN-MANE is the video adaptation component of our Video Quality Assurance Manager (VQAM) SDN control plane application, which also includes an SDN monitoring component to acquire network metrics and a decision making engine using algorithms to determine the optimum adaptation strategy for any real time video application flow given the current network conditions. Our proposed VQAM application has been implemented and
Genetic adaptation of the antibacterial human innate immunity network
Lazarus Ross
2011-07-01
Full Text Available Abstract Background Pathogens have represented an important selective force during the adaptation of modern human populations to changing social and other environmental conditions. The evolution of the immune system has therefore been influenced by these pressures. Genomic scans have revealed that immune system is one of the functions enriched with genes under adaptive selection. Results Here, we describe how the innate immune system has responded to these challenges, through the analysis of resequencing data for 132 innate immunity genes in two human populations. Results are interpreted in the context of the functional and interaction networks defined by these genes. Nucleotide diversity is lower in the adaptors and modulators functional classes, and is negatively correlated with the centrality of the proteins within the interaction network. We also produced a list of candidate genes under positive or balancing selection in each population detected by neutrality tests and showed that some functional classes are preferential targets for selection. Conclusions We found evidence that the role of each gene in the network conditions the capacity to evolve or their evolvability: genes at the core of the network are more constrained, while adaptation mostly occurred at particular positions at the network edges. Interestingly, the functional classes containing most of the genes with signatures of balancing selection are involved in autoinflammatory and autoimmune diseases, suggesting a counterbalance between the beneficial and deleterious effects of the immune response.
Adaptive Control of Flexible Redundant Manipulators Using Neural Networks
SONG Yimin; LI Jianxin; WANG Shiyu; LIU Jianping
2006-01-01
An investigation on the neural networks based active vibration control of flexible redundant manipulators was conducted.The smart links of the manipulator were synthesized with the flexible links to which were attached piezoceramic actuators and strain gauge sensors.A nonlinear adaptive control strategy named neural networks based indirect adaptive control (NNIAC) was employed to improve the dynamic performance of the manipulator.The mathematical model of the 4-layered dynamic recurrent neural networks (DRNN) was introduced.The neuro-identifier and the neurocontroller featuring the DRNN topology were designed off line so as to enhance the initial robustness of the NNIAC.By adjusting the neuro-identifier and the neuro-controller alternatively,the manipulator was controlled on line for achieving the desired dynamic performance.Finally,a planar 3R redundant manipulator with one smart link was utilized as an illustrative example.The simulation results proved the validity of the control strategy.
Adaptive Multipath Key Reinforcement for Energy Harvesting Wireless Sensor Networks
Di Mauro, Alessio; Dragoni, Nicola
2015-01-01
Energy Harvesting - Wireless Sensor Networks (EH-WSNs) constitute systems of networked sensing nodes that are capable of extracting energy from the environment and that use the harvested energy to operate in a sustainable state. Sustainability, seen as design goal, has a significant impact...... on the design of the security protocols for such networks, as the nodes have to adapt and optimize their behaviour according to the available energy. Traditional key management schemes do not take energy into account, making them not suitable for EH-WSNs. In this paper we propose a new multipath key...... reinforcement scheme specifically designed for EH-WSNs. The proposed scheme allows each node to take into consideration and adapt to the amount of energy available in the system. In particular, we present two approaches, one static and one fully dynamic, and we discuss some experimental results....
Network Experiences Lead to the Adaption of a Firm’s Network Competence
Bianka Kühne
2011-12-01
Full Text Available Networks become increasingly important as external sources of innovation for firms. Through networks firms get incontact with different actors with whom they can exchange information and collaborate. A firm’s ability to be asuccessful network actor depends on its network competence. This term can be defined as having the necessaryknowledge, skills and qualifications for networking as well as using them effectively. In this paper we investigate thelink between a firm’s network competence and the benefits resulting from it in a two‐way direction. First, thenetwork competence of the firm facilitates the adoption of information from other network actors which may leadto innovation success. Second the perceived network benefits shall in their turn influence the network competenceof the firm. Consequently, firms will adapt their network strategy corresponding their experiences. The objective ofthis paper is to investigate the dynamics of networking and its influence on the firm’s network competence. For thisexploratory research 3 Belgian networks are examined. In‐depth interviews are used in combination with semistructuredinterview guides to conduct the research. Our results indicate that some firms perceive benefits fromtheir network efforts, for others it is more a burden. Furthermore, in some of our cases we found that positiveexperiences with clear benefits motivate the firm to enhance its network competence. This is illustrated by the factthat collaborations are more frequently initiated, trust is more easily build, firms are more open to communicateinformation and the confidentiality threshold is overcome.
Boolean Search: Current State and Perspectives.
Frants, Valery I.; Shapiro, Jacob; Taksa, Isak; Voiskunskii, Vladimir G.
1999-01-01
Discusses the use of Boolean logic in information-retrieval systems and analyzes existing criticisms of operational systems. Considers users' ability to use and understand Boolean operators, ranking, the quality of query formulations, and negative effects of criticism; and concludes that criticism is directed at the methodology employed in…