Non-homogeneous dynamic Bayesian networks for continuous data

Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed. The present paper presents a combination of a Bayesian network with conditional probabilities in the linear Gaussian family, and a Bayesian multiple changepoint process, where the number and location of the changepoints are sampled from the posterior distribution with MCMC. Our work improves four aspects of an earlier conference paper: it contains a comprehensive and self-contained exposition of the methodology; it discusses the problem of spurious feedback loops in network reconstruction; it contains a comprehensive comparative evaluation of the network reconstruction accuracy on a set of synthetic and real-world benchmark problems, based on a novel discrete changepoint process; and it suggests new and improved MCMC schemes for sampling both the network structures and the changepoint configurations from the posterior distribution. The latter study compares RJMCMC, based on changepoint birth and death moves, with two dynamic programming schemes that were originally devised for Bayesian mixture models. We demonstrate the modifications that have to be made to allow for changing network structures, and the critical impact that the prior distribution on changepoint configurations has on the overall computational complexity.     

A dynamic data replication strategy using access-weights in data grids

Data grids deal with a huge amount of data regularly. It is a fundamental challenge to ensure efficient accesses to such widely distributed data sets. Creating replicas to a suitable site by data replication strategy can increase the system performance. It shortens the data access time and reduces bandwidth consumption. In this paper, a dynamic data replication mechanism called Latest Access Largest Weight (LALW) is proposed. LALW selects a popular file for replication and calculates a suitable number of copies and grid sites for replication. By associating a different weight to each historical data access record, the importance of each record is differentiated. A more recent data access record has a larger weight. It indicates that the record is more pertinent to the current situation of data access. A Grid simulator, OptorSim, is used to evaluate the performance of this dynamic replication strategy. The simulation results show that LALW successfully increases the effective network usage. It means that the LALW replication strategy can find out a popular file and replicates it to a suitable site without increasing the network burden too much.

A dynamic framework for integrated management of all types of resources in P2P systems

Traditional Peer-to-Peer (P2P) systems were restricted to sharing of files on the Internet. Although some of the more recent P2P distributed systems have tried to support transparent sharing of other types of resources, like computer processing power, but none allow and support sharing of all types of resources available on the Internet. This is mainly because the resource management part of P2P systems are custom designed in support of specific features of only one type of resource, making simultaneous access to all types of resources impractical. Another shortcoming of existing P2P systems is that they follow a client/server model of resource sharing that makes them structurally constrained and dependent on dedicated servers (resource managers). Clients must get permission from a limited number of servers to share or access resources, and resource management mechanisms run on these servers. Because resource management by servers is not dynamically reconfigurable, such P2P systems are not scalable to the ever growing extent of Internet. We present an integrated framework for sharing of all types of resources in P2P systems by using a dynamic structure for managing four basic types of resources, namely process, file, memory, and I/O, in the same way they are routinely managed by operating systems. The proposed framework allows P2P systems to use dynamically reconfigurable resource management mechanisms where each machine in the P2P system can at the same time serve both as a server and as a client. The pattern of requests for shared resources at a given time identifies which machines are currently servers and which ones are currently clients. The client server pattern changes with changes in the pattern of requests for distributed resources. Scalable P2P systems with dynamically reconfigurable structures can thus be built using our proposed resource management mechanisms. This dynamic structure also allows for the interoperability of different P2P systems.     

A framework for development and evaluation of a dynamic subchannel allocation scheme in an OFDMA system

This paper presents a framework for allocating radio resources to the Access Points (APs) introducing an Access Point Controller (APC). Radio resources can be either time slots or subchannels. The APC assigns subchannels to the APs using a dynamic subchannel allocation scheme. The developed framework evaluates the dynamic subchannel allocation scheme for a downlink multicellular Orthogonal Frequency Division Multiple Access (OFDMA) system. In the considered system, each AP and the associated Mobile Terminals (MTs) are not operating on a frequency channel with fixed bandwidth, rather the channel bandwidth for each AP is dynamically adapted according to the traffic load. The subchannels assignment procedure is based on quality estimations due to the interference measurements and the current traffic load. The traffic load estimation is realized with the measurement of the utilization of the assigned radio resources. The reuse partitioning for the radio resources is done by estimating mutual Signal to Interference Ratio (SIR) of the APs. The developed dynamic subchannel allocation ensures Quality of Service (QoS), better traffic adaptability, and higher spectrum efficiency with less computational complexity.

Implementation of a dynamic adjustment strategy for parallel file transfer in co-allocation data grids

Co-allocation architecture was developed to enable parallel transferring of files from multiple replicas stored in the different servers. Several co-allocation strategies have been coupled and used to exploit the different transfer rates among various client-server links and to address dynamic rate fluctuations by dividing files into multiple blocks of equal sizes. The paper presents a dynamic file transfer scheme, called dynamic adjustment strategy (DAS), for co-allocation architecture in concurrently transferring a file from multiple replicas stored in multiple servers within a data grid. The scheme overcomes the obstacle of transfer performance due to idle waiting time of faster servers in co-allocation based file transfers and, therefore, provides reduced file transfer time. A tool with user friendly interface that can be used to manage replicas and downloading in a data grid environment is also described. Experimental results show that our DAS can obtain high-performance file transfer speed and reduce the time cost of reassembling data blocks.     

Adaptive popularity-driven replica placement in hierarchical data grids

Data grids support access to widely distributed storage for large numbers of users accessing potentially many large files. Efficient access is hindered by the high latency of the Internet. To improve access time, replication at nearby sites may be used. Replication also provides high availability, decreased bandwidth use, enhanced fault tolerance, and improved scalability. Resource availability, network latency, and user requests in a grid environment may vary with time. Any replica placement strategy must be able to adapt to such dynamic behavior. In this paper, we describe a new dynamic replica placement algorithm, Popularity Based Replica Placement (PBRP), for hierarchical data grids which is guided by file “popularity”. Our goal is to place replicas close to clients to reduce data access time while still using network and storage resources efficiently. The effectiveness of PBRP depends on the selection of a threshold value related to file popularity. We also present Adaptive-PBRP (APBRP) that determines this threshold dynamically based on data request arrival rates. We evaluate both algorithms using simulation. Results for a range of data access patterns show that our algorithms can shorten job execution time significantly and reduce bandwidth consumption compared to other dynamic replication methods.     

An Asymptotically Stable Semi-Lagrangian scheme in the Quasi-neutral Limit

This paper deals with the numerical simulations of the Vlasov-Poisson equation using a phase space grid in the quasi-neutral regime. In this limit, explicit numerical schemes suffer from numerical constraints related to the small Debye length and large plasma frequency. Here, we propose a semi-Lagrangian scheme for the Vlasov-Poisson model in the quasi-neutral limit. The main ingredient relies on a reformulation of the Poisson equation derived in (Crispel et al. in C. R. Acad. Sci. Paris, Ser. I 341:341–346, 2005) which enables asymptotically stable simulations. This scheme has a comparable numerical cost per time step to that of an explicit scheme. Moreover, it is not constrained by a restriction on the size of the time and length step when the Debye length and plasma period go to zero. A stability analysis and numerical simulations confirm this statement.     

Adaptive Multiresolution Methods for the Simulation of Waves in Excitable Media

We present fully adaptive multiresolution methods for a class of spatially two-dimensional reaction-diffusion systems which describe excitable media and often give rise to the formation of spiral waves. A novel model ingredient is a strongly degenerate diffusion term that controls the degree of spatial coherence and serves as a mechanism for obtaining sharper wave fronts. The multiresolution method is formulated on the basis of two alternative reference schemes, namely a classical finite volume method, and Barkley’s approach (Barkley in Phys. D 49:61–70, 1991), which consists in separating the computation of the nonlinear reaction terms from that of the piecewise linear diffusion. The proposed methods are enhanced with local time stepping to attain local adaptivity both in space and time. The computational efficiency and the numerical precision of our methods are assessed. Results illustrate that the fully adaptive methods provide stable approximations and substantial savings in memory storage and CPU time while preserving the accuracy of the discretizations on the corresponding finest uniform grid.     

An Adaptive Method for Recovering Image from Mixed Noisy Data

In this paper, we present a new version of the famous Rudin-Osher-Fatemi (ROF) model to restore image. The key point of the model is that it could reconstruct images with blur and non-uniformly distributed noise. We develop this approach by adding several statistical control parameters to the cost functional, and these parameters could be adaptively determined by the given observed image. In this way, we could adaptively balance the performance of the fit-to-data term and the regularization term. The Numerical experiments have demonstrated the significant effectiveness and robustness of our model in restoring blurred images with mixed Gaussian noise or salt-and-pepper noise.     

Adaptive L2 control of nonlinear systems using neural networks

An adaptive neural network controller is developed to achieve output-tracking of a class of nonlinear systems. The global L2 stability of the closed-loop system is established. The proposed control design overcomes the limitation of the conventional adaptive neural control design where the modeling error brought by neural networks is assumed to be bounded over a compact set.Moreover,the generalized matching conditions are also relaxed in the proposed L2 control design as the gains for the external disturbances entering the system are allowed to have unknown upper bounds.     

An improved SVM method P‐SVM for classification of remotely sensed data

A support vector machine (SVM) is a mathematical tool which is based on the structural risk minimization principle. It tries to find a hyperplane in high dimensional feature space to solve some linearly inseparable problems. SVM has been applied within the remote sensing community to multispectral and hyperspectral imagery analysis. However, the standard SVM faces some technical disadvantages. For instance, the solution of an SVM learning problem is scale sensitive, and the process is time‐consuming. A novel Potential SVM (P‐SVM) algorithm is proposed to overcome the shortcomings of standard SVM and it has shown some improvements. In this letter, the P‐SVM algorithm is introduced into multispectral and high‐spatial resolution remotely sensed data classification, and it is applied to ASTER imagery and ADS40 imagery respectively. Experimental results indicate that the P‐SVM is competitive with the standard SVM algorithm in terms of accuracy of classification of remotely sensed data, and the time needed is less.     

Erratum to: New efficient method for dynamic modeling and simulation of flexible multibody systems moving in plane

Efficient, precise dynamic analysis for general flexible multibody systems has become a research focus in the field of flexible multibody dynamics. In this paper, the finite element method and component mode synthesis are introduced to describe the deformations of the flexible components, and the dynamic equations of flexible bodies moving in plane are deduced. By combining the discrete time transfer matrix method of multibody system with these dynamic equations of flexible component, the transfer equations and transfer matrices of flexible bodies moving in plane are developed. Finally, a high-efficient dynamic modeling method and its algorithm are presented for high-speed computation of general flexible multibody dynamics. Compared with the ordinary dynamics methods, the proposed method combines the strengths of the transfer matrix method and finite element method. It does not need the global dynamic equations of system and has the low order of system matrix and high computational efficiency. This method can be applied to solve the dynamics problems of flexible multibody systems containing irregularly shaped flexible components. It has advantages for dynamic design of complex flexible multibody systems. Formulations as well as a numerical example of a multi-rigid-flexible-body system containing irregularly shaped flexible components are given to validate the method.     

Parameterized energy–latency trade-offs for data propagation in sensor networks

We study the problem of greedy, single path data propagation in wireless sensor networks, aiming mainly to minimize the energy dissipation. In particular, we first mathematically analyze and experimentally evaluate the energy efficiency and latency of three characteristic protocols, each one selecting the next hop node with respect to a different criterion (minimum projection, minimum angle and minimum distance to the destination). Our analytic and simulation findings suggest that any single criterion does not simultaneously satisfy both energy efficiency and low latency. Towards parameterized energy–latency trade-offs we provide as well hybrid combinations of the two criteria (direction and proximity to the sink). Our hybrid protocols achieve significant performance gains and allow fine-tuning of desired performance. Also, they have nice energy balance properties, and can prolong the network lifetime.     

New efficient method for dynamic modeling and simulation of flexible multibody systems moving in plane

Efficient, precise dynamic analysis for general flexible multibody systems has become a research focus in the field of flexible multibody dynamics. In this paper, the finite element method and component mode synthesis are introduced to describe the deformations of the flexible components, and the dynamic equations of flexible bodies moving in plane are deduced. By combining the discrete time transfer matrix method of multibody system with these dynamic equations of flexible component, the transfer equations and transfer matrices of flexible bodies moving in plane are developed. Finally, a high-efficient dynamic modeling method and its algorithm are presented for high-speed computation of general flexible multibody dynamics. Compared with the ordinary dynamics methods, the proposed method combines the strengths of the transfer matrix method and finite element method. It does not need the global dynamic equations of system and has the low order of system matrix and high computational efficiency. This method can be applied to solve the dynamics problems of flexible multibody systems containing irregularly shaped flexible components. It has advantages for dynamic design of complex flexible multibody systems. Formulations as well as a numerical example of a multi-rigid-flexible-body system containing irregularly shaped flexible components are given to validate the method.     

Evolution of cooperation in reputation system by group-based scheme

Reputation systems are very useful in large online communities in which users may frequently have the opportunity to interact with users with whom they have no prior experience. Recently, how to enhance the cooperative behaviors in the reputation system has become to one of the key open issues. Emerging schemes focused on developing efficient reward and punishment mechanisms or capturing the social or economic properties of participants. However, whether this kind of method can work widely or not has been hard to prove until now. Research in evolutionary game theory shows that group selection (or multilevel selection) can favor the cooperative behavior in the finite population. Furthermore, some recent works give fundamental conditions for the evolution of cooperation by group selection. In the paper, we extend the original group selection theory and propose a group-based scheme to enhance cooperation for online reputation systems. Related concepts are defined to capture the social structure and ties among participants in reputation system, e.g., group, assortativity, etc. Also, we use a Fermi distribution function to reflect the bounded rationality of participants and the existence of stochastic factors in evolutionary process. Extended simulations show that our scheme can enhance cooperation and improve the average performance of participants (e.g. payoff) in reputation system.     

The hybrid dynamic parallel scheduling algorithm for load balancing on Chained-Cubic Tree interconnection networks

The Chained-Cubic Tree (CCT) interconnection network topology was recently proposed as a continuation for the extended efforts in the area of interconnection networks’ performance improvement. This topology, which promises to exhibit the best properties of the hypercube and tree topologies, needs to be deeply investigated in order to evaluate its performance among other interconnection networks’ topologies. This work comes as a complementary effort, in which the load balancing technique is investigated as one of the most important aspects of performance improvement. This paper proposes a new load balancing algorithm on CCT interconnection networks. The proposed algorithm, which is called Hybrid Dynamic Parallel Scheduling Algorithm (HD-PSA), is a combination of two common load balancing strategies; dynamic load balancing and parallel scheduling. The performance of the proposed algorithm is evaluated both, analytically and experimentally, in terms of various performance metrics; including, execution time, load balancing accuracy, communication cost, number of tasks hops, and tasks locality.     

An Adaptive P 1 Finite Element Method for Two-Dimensional Maxwell’s Equations

Recently a new numerical approach for two-dimensional Maxwell’s equations based on the Hodge decomposition for divergence-free vector fields was introduced by Brenner et al. In this paper we present an adaptive P ₁ finite element method for two-dimensional Maxwell’s equations that is based on this new approach. The reliability and efficiency of a posteriori error estimators based on the residual and the dual weighted-residual are verified numerically. The performance of the new approach is shown to be competitive with the lowest order edge element of Nédélec’s first family.     

Dynamic-CoMPI: dynamic optimization techniques for MPI parallel applications

This work presents an optimization of MPI communications, called Dynamic-CoMPI, which uses two techniques in order to reduce the impact of communications and non-contiguous I/O requests in parallel applications. These techniques are independent of the application and complementaries to each other. The first technique is an optimization of the Two-Phase collective I/O technique from ROMIO, called Locality aware strategy for Two-Phase I/O (LA-Two-Phase I/O). In order to increase the locality of the file accesses, LA-Two-Phase I/O employs the Linear Assignment Problem (LAP) for finding an optimal I/O data communication schedule. The main purpose of this technique is the reduction of the number of communications involved in the I/O collective operation. The second technique, called Adaptive-CoMPI, is based on run-time compression of MPI messages exchanged by applications. Both techniques can be applied on every application, because both of them are transparent for the users. Dynamic-CoMPI has been validated by using several MPI benchmarks and real HPC applications. The results show that, for many of the considered scenarios, important reductions in the execution time are achieved by reducing the size and the number of the messages. Additional benefits of our approach are the reduction of the total communication time and the network contention, thus enhancing, not only performance, but also scalability.     

Analytical modeling of interconnection networks in heterogeneous multi-cluster systems

The study of interconnection networks is important because the overall performance of a distributed system is often critically hinged on the effectiveness of its interconnection network. This paper addresses the problem of interconnection networks performance modeling of large-scale distributed systems with emphases on heterogeneous multi-cluster computing systems. We present an analytical model to predict message latency in multi-cluster systems in the presence of node, network and system organization heterogeneity. The model is validated through comprehensive simulation, which demonstrates that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.

Exploiting slack time for just-in-time scheduling in wireless sensor networks

We consider the problem of real-time data collection in wireless sensor networks, in which data need to be delivered to one or more sinks within end-to-end deadlines. To enhance performance with respect to end-to-end deadline miss ratio, existing approaches schedule packets by prioritizing them based on per-packet deadlines and other factors such as the distance to the sink. However, important factors affecting the end-to-end performance such as queuing delays and buffer overruns have largely been ignored in the existing real-time schemes. Packet prioritization by itself cannot assist with these issues, and may in fact, exacerbate them for real-time data collection, since many high priority packets may simultaneously contend for the constrained network resources. In sensor networks, where the channel bandwidth and buffer space are often quite limited, these issues can dramatically impact real-time performance. Based on this observation, we propose Just-in-Time Scheduling (JiTS) strategies where packets are judiciously delayed within their slack time to reduce contention and load balance the use of the network buffers. We explore several policies for delaying data packets at different intermediate nodes considering potential contention. In addition, we also show that the routing protocol has a significant impact on real-time performance. In particular, shortest path routing leads to considerably better performance than geographic forwarding, which is often used for real-time data transmission in wireless sensor networks. Using an extensive simulation study, we demonstrate that JiTS can significantly improve the deadline miss ratio and packet drop ratio compared to two state-of-the-art approaches for real-time packet delivery for sensor networks (RAP and SPEED) under various scenarios. Notably, JiTS requires neither lower layer (e.g., MAC layer) support nor synchronization among the sensor nodes.