Synchronous Update Research Articles

Adaptive switching gravitational search algorithm: an attempt to improve diversity of gravitational search algorithm through its iteration strategy

An adaptive gravitational search algorithm (GSA) that switches between synchronous and asynchronous update is presented in this work. The proposed adaptive switching synchronous–asynchronous GSA (ASw-GSA) improves GSA through manipulation of its iteration strategy. The iteration strategy is switched from synchronous to asynchronous update and vice versa. The switching is conducted so that the population is adaptively switched between convergence and divergence. Synchronous update allows convergence, while switching to asynchronous update causes disruption to the population’s convergence. The ASw-GSA agents switch their iteration strategy when the best found solution is not improved after a period of time. The period is based on a switching threshold. The threshold determines how soon is the switching, and also the frequency of switching in ASw-GSA. ASw-GSA has been comprehensively evaluated based on CEC2014’s benchmark functions. The effect of the switching threshold has been studied and it is found that, in comparison with multiple and early switches, one-time switching towards the end of the search is better and substantially enhances the performance of ASw-GSA. The proposed ASw-GSA is also compared to original GSA, particle swarm optimization (PSO), genetic algorithm (GA), bat-inspired algorithm (BA) and grey wolf optimizer (GWO). The statistical analysis results show that ASw-GSA performs significantly better than GA and BA and as well as PSO, the original GSA and GWO.12

Enhancement of cooperation through conformity-driven reproductive ability

We propose a conformity-driven reproductive ability in which an individual i is more (less) likely to imitate a neighbor j’s strategy if j’s strategy is majority (minority) in i’s neighborhood. The results on the evolutionary spatial prisoner’s dilemma game show that, compared to homogeneous reproductive ability, conformity-driven reproductive ability can greatly enhance cooperation. This finding is robust with respect to different types of network structures (including square lattice and scale-free network) and to different ways of strategy updating (including synchronous and asynchronous strategy updating).

Modelling and Analysing Qualitative Biological Models using Rewriting Logic

Qualitative logical modelling techniques play an important role in biology and are seen as crucial for developing scalable methods for modelling and synthesizing biological systems. While a range of interesting work has been done in this area there still exists challenging issues that need to be ad dressed for the practical application of these modelling techniques. In this paper we present an algebraic framework for exploring these issues by developing techniques for modelling and analysing qualitative biological models using Rewriting Logic (RL). The aim here is to develop a universal formal framework which is able to integrate models expressed in different formalisms (e.g. Boolean networks, Petri Nets and process algebra) and provide a basis for new work in this area (e.g. merging models based on different formalisms; compositional model construction and analysis; and tools for synthetic biology). We take as our starting point Multi-valued networks (MVNs), a simple yet expressive qualitative state based modelling approach widely used in biology. We develop a semantic translation from MVNs to a corresponding RL model and formally show that this translation is correct. We consider both the asynchronous and synchronous update semantics, and investigate the use of rewriting strategies to enable synchronisation to be modelled. We illustrate the RL framework developed and the potential RL analysis possible by presenting two detailed case studies.

Distributed output feedback model predictive control for a team of coupled linear subsystems

A distributed output feedback model predictive control (MPC) algorithm is proposed for a team of linear discrete-time subsystems, for which the states are not measurable. The subsystems are coupled by the cost function. A synchronous update strategy is adopted to formulate a tractable optimisation problem in distributed manner. The terminal controller is specified as the form of output feedback. The weight matrix shared by the terminal region and the terminal cost is designed by solving one optimisation problem. Thereby the terminal region is positively invariant with the terminal controller. Furthermore, the compatibility constraint is tailored for each subsystem to confine the deviation between the real outputs and its assumed ones. The recursive feasibility of the resulting optimisation problem is ensured and the stability of the whole system in closed-loop operation is guaranteed. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm, which can perform faster than the corresponding centralised MPC in spite of a little higher cost.

Synchronism versus asynchronism in monotonic Boolean automata networks

This paper focuses on Boolean automata networks and the updatings of automata states in these networks. More specifically, we study how synchronous updates impact on the global behaviour of a network. On this basis, we define different types of network sensitivity to synchronism, which are effectively satisfied by some networks. We also relate this synchronism-sensitivity to some properties of the structure of networks and to their underlying mechanisms.

Asynchronous updates can promote the evolution of cooperation on multiplex networks

We study the importance to the frequency of cooperation of the choice of updating strategies in a game played asynchronously or synchronously across layers in a multiplex network. Updating asynchronously in the public goods game leads to higher frequencies of cooperation compared to synchronous updates. How large this effect is depends on the sensitivity of the game dynamics to changes in the number of cooperators surrounding a player, with the largest effect observed when players payoffs are small. The discovery of this effect enhances understanding of cooperation on multiplex networks, and demonstrates a new way to maintain cooperation in these systems.

An Information Criterion for Inferring Coupling of Distributed Dynamical Systems

The behaviour of many real-world phenomena can be modelled by nonlinear dynamical systems whereby a latent system state is observed through a filter. We are interested in interacting subsystems of this form, which we model by a set of coupled maps as a synchronous update graph dynamical systems. Specifically, we study the structure learning problem for spatially distributed dynamical systems coupled via a directed acyclic graph. Unlike established structure learning procedures that find locally maximum posterior probabilities of a network structure containing latent variables, our work exploits the properties of dynamical systems to compute globally optimal approximations of these distributions. We arrive at this result by the use of time delay embedding theorems. Taking an information-theoretic perspective, we show that the log-likelihood has an intuitive interpretation in terms of information transfer.

Estimating Propensity Parameters Using Google PageRank and Genetic Algorithms.

Stochastic Boolean networks, or more generally, stochastic discrete networks, are an important class of computational models for molecular interaction networks. The stochasticity stems from the updating schedule. Standard updating schedules include the synchronous update, where all the nodes are updated at the same time, and the asynchronous update where a random node is updated at each time step. The former produces a deterministic dynamics while the latter a stochastic dynamics. A more general stochastic setting considers propensity parameters for updating each node. Stochastic Discrete Dynamical Systems (SDDS) are a modeling framework that considers two propensity parameters for updating each node and uses one when the update has a positive impact on the variable, that is, when the update causes the variable to increase its value, and uses the other when the update has a negative impact, that is, when the update causes it to decrease its value. This framework offers additional features for simulations but also adds a complexity in parameter estimation of the propensities. This paper presents a method for estimating the propensity parameters for SDDS. The method is based on adding noise to the system using the Google PageRank approach to make the system ergodic and thus guaranteeing the existence of a stationary distribution. Then with the use of a genetic algorithm, the propensity parameters are estimated. Approximation techniques that make the search algorithms efficient are also presented and Matlab/Octave code to test the algorithms are available at http://www.ms.uky.edu/~dmu228/GeneticAlg/Code.html.

Networks of conforming or nonconforming individuals tend to reach satisfactory decisions

Binary decisions of agents coupled in networks can often be classified into two types: "coordination," where an agent takes an action if enough neighbors are using that action, as in the spread of social norms, innovations, and viral epidemics, and "anticoordination," where too many neighbors taking a particular action causes an agent to take the opposite action, as in traffic congestion, crowd dispersion, and division of labor. Both of these cases can be modeled using linear-threshold-based dynamics, and a fundamental question is whether the individuals in such networks are likely to reach decisions with which they are satisfied. We show that, in the coordination case, and perhaps more surprisingly, also in the anticoordination case, the agents will indeed always tend to reach satisfactory decisions, that is, the network will almost surely reach an equilibrium state. This holds for every network topology and every distribution of thresholds, for both asynchronous and partially synchronous decision-making updates. These results reveal that irregular network topology, population heterogeneity, and partial synchrony are not sufficient to cause cycles or nonconvergence in linear-threshold dynamics; rather, other factors such as imitation or the coexistence of coordinating and anticoordinating agents must play a role.

Synchronous incremental update of materialized views for PostgreSQL

Materialized views are logically excess stored query results in SQL-oriented databases. This technology can significantly improve the performance of database systems. Although the idea of materialized views came up in the 1980s, only three database management systems, i.e. DB2, Oracle, SQL Server, have been successfully developed completely enough with materialized views so far. The barrier lies in building a module that can incrementally update the materialized views automatically, which corresponds to data changes in the base tables. This paper presents the algorithm to incrementally update the materialized views with inner join, focusing on one with aggregate functions, and building of a program that automatically generates codes inPL/pgSQL for triggers, which can undertake synchronous incremental updates of the materialized views in PostgreSQL.

Adaptive Label Propagation Algorithm to Detect Overlapping Community in Complex Networks

According to the defects that community detection algorithm in unknown complex networks has a pre-parameter. We propose Adaptive Label Propagation Algorithm (ALPA) to detect community structures in complex networks. The ALPA algorithm find out all disjoint Maximal Clique (MC) and let each MC share the identical weight and unique label so as to reduce the redundant labels and uncontrollable factors. The stability of ALPA algorithm is enhanced by synchronous update during iterations. Meanwhile it will converge easily due to the termination condition that all of the vertexes have the label. During iterations we use the adaptive threshold method to overcome the pre-parameter limitation. Compared with other community detection algorithms in synthetic networks and real networks, our experiments show that ALPA algorithm not only improves the tolerance of mixing parameter, but also enhances its robustness.

Robustness, Stability, and Gains of Utility Maximization Algorithms for Mobile Ad Hoc Networks

Cross-layer designs (CLDs) have the potential to better utilize network resources than a traditional layered protocol architecture, providing better communication services to end-users. This is particularly relevant in MANETs, which are characterized by a scarcity of network resources, in particular bandwidth. While many papers have demonstrated the potential performance benefit that CLDs can provide, their evaluation is often done under idealized scenarios, such as instantaneous message transmission times, complete topology knowledge, and/or zero message loss rates. Nodes in real MANETs do typically experience message losses, message delays, and have at best a partial and most likely inaccurate knowledge of the dynamic network topology. Our work focuses on realistic evaluations of a specific CLD approach. We propose a robust and stable version of a network utility maximization algorithm that optimizes the medium access probabilities at the MAC layer jointly with the end-to-end sessions rates at the transport layer. We report on stability tests with different link failure scenarios in case of synchronous and asynchronous message updates, convergence speed, and link utilizations. The results show that the algorithm converges even with a very high rate of link transmission failures and can tolerate asynchronous parameters updates. The CLD fully utilizes the link capacities and provides a network capacity gain of up to 250 %. The proposed modified CLD approach is therefore both beneficial and practical for real MANETs.

Scaling Synchronization in Multicore Programs

Designing software for modern multicore processors poses a dilemma. Traditional software designs, in which threads manipulate shared data, have limited scalability because synchronization of updates to shared data serializes threads and limits parallelism. Alternative distributed software designs, in which threads do not share mutable data, eliminate synchronization and offer better scalability. But distributed designs make it challenging to implement features that shared data structures naturally provide, such as dynamic load balancing and strong consistency guarantees, and are simply not a good fit for every program. Often, however, the performance of shared mutable data structures is limited by the synchronization methods in use today, whether lock-based or lock-free. To help readers make informed design decisions, this article describes advanced (and practical) synchronization methods that can push the performance of designs using shared mutable data to levels that are acceptable to many applications.

Recovery rate affects the effective epidemic threshold with synchronous updating.

Accurate identification of effective epidemic threshold is essential for understanding epidemic dynamics on complex networks. In this paper, we systematically study how the recovery rate affects the susceptible-infected-removed spreading dynamics on complex networks, where synchronous and asynchronous updating processes are taken into account. We derive the theoretical effective epidemic threshold and final outbreak size based on the edge-based compartmental theory. To validate the proposed theoretical predictions, extensive numerical experiments are implemented by using asynchronous and synchronous updating methods. When asynchronous updating method is used in simulations, recovery rate does not affect the final state of spreading dynamics. But with synchronous updating, we find that the effective epidemic threshold decreases with recovery rate, and final outbreak size increases with recovery rate. A good agreement between the theoretical predictions and the numerical results are observed on both synthetic and real-world networks. Our results extend the existing theoretical studies and help us to understand the phase transition with arbitrary recovery rate.

FlexFlux: combining metabolic flux and regulatory network analyses.

BackgroundExpression of cell phenotypes highly depends on metabolism that supplies matter and energy. To achieve proper utilisation of the different metabolic pathways, metabolism is tightly regulated by a complex regulatory network composed of diverse biological entities (genes, transcripts, proteins, signalling molecules…). The integrated analysis of both regulatory and metabolic networks appears very insightful but is not straightforward because of the distinct characteristics of both networks. The classical method used for metabolic flux analysis is Flux Balance Analysis (FBA), which is constraint-based and relies on the assumption of steady-state metabolite concentrations throughout the network. Regarding regulatory networks, a broad spectrum of methods are dedicated to their analysis although logical modelling remains the major method to take charge of large-scale networks.ResultsWe present FlexFlux, an application implementing a new way to combine the analysis of both metabolic and regulatory networks, based on simulations that do not require kinetic parameters and can be applied to genome-scale networks. FlexFlux is based on seeking regulatory network steady-states by performing synchronous updates of multi-state qualitative initial values. FlexFlux is then able to use the calculated steady-state values as constraints for metabolic flux analyses using FBA. As input, FlexFlux uses the standards Systems Biology Markup Language (SBML) and SBML Qualitative Models Package (“qual”) extension (SBML-qual) file formats and provides a set of FBA based functions.ConclusionsFlexFlux is an open-source java software with executables and full documentation available online at http://lipm-bioinfo.toulouse.inra.fr/flexflux/. It can be defined as a research tool that enables a better understanding of both regulatory and metabolic networks based on steady-state simulations. FlexFlux integrates well in the flux analysis ecosystem thanks to the support of standard file formats and can thus be used as a complementary tool to existing software featuring other types of analyses.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0238-z) contains supplementary material, which is available to authorized users.

Towards scalable real-time analytics

We present an overview of our work on the SAP HANA Scale-out Extension, a novel distributed database architecture designed to support large scale analytics over real-time data. This platform permits high performance OLAP with massive scale-out capabilities, while concurrently allowing OLTP workloads. This dual capability enables analytics over real-time changing data and allows fine grained user-specified service level agreements (SLAs) on data freshness. We advocate the decoupling of core database components such as query processing, concurrency control, and persistence, a design choice made possible by advances in high-throughput low-latency networks and storage devices. We provide full ACID guarantees and build on a logical timestamp mechanism to provide MVCC-based snapshot isolation, while not requiring synchronous updates of replicas. Instead, we use asynchronous update propagation guaranteeing consistency with timestamp validation. We provide a view into the design and development of a large scale data management platform for real-time analytics, driven by the needs of modern enterprise customers.

A model of the regulatory network involved in the control of the cell cycle and cell differentiation in the Caenorhabditis elegans vulva

BackgroundThere are recent experimental reports on the cross-regulation between molecules involved in the control of the cell cycle and the differentiation of the vulval precursor cells (VPCs) of Caenorhabditis elegans. Such discoveries provide novel clues on how the molecular mechanisms involved in the cell cycle and cell differentiation processes are coordinated during vulval development. Dynamic computational models are helpful to understand the integrated regulatory mechanisms affecting these cellular processes.ResultsHere we propose a simplified model of the regulatory network that includes sufficient molecules involved in the control of both the cell cycle and cell differentiation in the C. elegans vulva to recover their dynamic behavior. We first infer both the topology and the update rules of the cell cycle module from an expected time series. Next, we use a symbolic algorithmic approach to find which interactions must be included in the regulatory network. Finally, we use a continuous-time version of the update rules for the cell cycle module to validate the cyclic behavior of the network, as well as to rule out the presence of potential artifacts due to the synchronous updating of the discrete model. We analyze the dynamical behavior of the model for the wild type and several mutants, finding that most of the results are consistent with published experimental results.ConclusionsOur model shows that the regulation of Notch signaling by the cell cycle preserves the potential of the VPCs and the three vulval fates to differentiate and de-differentiate, allowing them to remain completely responsive to the concentration of LIN-3 and lateral signal in the extracellular microenvironment.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0498-z) contains supplementary material, which is available to authorized users.

Structure of attractors in randomly connected networks.

The deterministic dynamics of randomly connected neural networks are studied, where a state of binary neurons evolves according to a discrete-time synchronous update rule. We give theoretical support that the overlap of systems' states between the current and a previous time develops in time according to a Markovian stochastic process in large networks. This Markovian process predicts how often a network revisits one of the previously visited states, depending on the system size. The state concentration probability, i.e., the probability that two distinct states coevolve to the same state, is utilized to analytically derive various characteristics that quantify attractors' structure. The analytical predictions about the total number of attractors, the typical cycle length, and the number of states belonging to all attractive cycles match well with numerical simulations for relatively large system sizes.

Evolutionary games on graphs and discrete dynamical systems

Evolutionary games on graphs play an important role in the study of evolution of cooperation in applied biology. Using rigorous mathematical concepts from a dynamical systems and graph theoretical point of view, we formalize the notions of attractor, update rules and update orders. We prove results on attractors for different utility functions and update orders. For complete graphs we characterize attractors for synchronous and sequential update rules. In other cases (for -regular graphs or for different update orders) we provide sufficient conditions for attractivity of full cooperation and full defection. We construct examples to show that these conditions are not necessary. Finally, by formulating a list of open questions we emphasize the advantages of our rigorous approach.

A Performance Study on Synchronous and Asynchronous Update Rules for A Plug-In Direct Particle Swarm Repetitive Controller

Abstract In this paper two different update schemes for the recently developed plug-in direct particle swarm repetitive controller (PDPSRC) are investigated and compared. The proposed approach employs the particle swarm optimizer (PSO) to solve in on-line mode a dynamic optimization problem (DOP) related to the control task in the constant-amplitude constant-frequency voltage-source inverter (CACF VSI) with an LC output filter. The effectiveness of synchronous and asynchronous update rules, both commonly used in static optimization problems (SOPs), is assessed and compared in the case of PDPSRC. The performance of the controller, when synthesized using each of the update schemes, is studied numerically.