Boolean Factor Analysis by Attractor Neural Network

A common problem encountered in disciplines such as statistics, data analysis, signal processing, textual data representation, and neural network research, is finding a suitable representation of the data in the lower dimension space. One of the principles used for this reason is a factor analysis. In this paper, we show that Hebbian learning and a Hopfield-like neural network could be used for a natural procedure for Boolean factor analysis. To ensure efficient Boolean factor analysis, we propose our original modification not only of Hopfield network architecture but also its dynamics as well. In this paper, we describe neural network implementation of the Boolean factor analysis method. We show the advantages of our Hopfield-like network modification step by step on artificially generated data. At the end, we show the efficiency of the method on artificial data containing a known list of factors. Our approach has the advantage of being able to analyze very large data sets while preserving the nature of the data     

An integer programming approach to optimal control problems in context-sensitive probabilistic Boolean networks

A Boolean network is one of the models of biological networks such as gene regulatory networks, and has been extensively studied. In particular, a probabilistic Boolean network (PBN) is well known as an extension of Boolean networks, but in the existing methods to solve the optimal control problem of PBNs, it is necessary to compute the state transition diagram with 2ⁿ nodes for a given PBN with n states. To avoid this computation, an integer programming-based approach is proposed for a context-sensitive PBN (CS-PBN), which is a general form of PBNs. In the proposed method, a CS-PBN is transformed into a linear system with binary variables, and the optimal control problem is reduced to an integer linear programming problem. By a numerical example, the effectiveness of the proposed method is shown.     

Optimal Online Algorithms for an Electronic Commerce Money Distribution System

Abstract. We consider the money distribution problem for a micro-payment scheme using a distributed server system; in particular, for an automatic charging scheme named PayPerClick that allows Internet users to view Web pages for which access charges are levied without tedious payment procedures. A major bottleneck in the scheme is the network traffic caused by the distribution of electronic money to many different servers. We propose a simple online algorithm for distributing electronic money to servers so that the network traffic is minimized. The algorithm achieves the optimal online competitive ratio. We also consider a weighted version, for which we give an asymptotically optimal online algorithm within a constant factor.     

Neural network approach for multicriteria solid transportation problem

In this paper, we propose neural network approach for multicriteria solid transportation problems(STP). First we suggest a neural network architecture to solve single-objective STP according to augmented Lagrange multiplier method. Due to the massive computing unit-neurons and parallel mechanism of neural network approach can solve the large scale problem efficiently and optimal solution can be got. Then we transform the original multicriteria problem into a single objective problem by global criteria method and adopt the neural network approach to solve it. By this way we can get the satisfactory solution of the original problem. The procedure and efficiency of this approach are shown with numerical simulations.     

Efficient Multimedia Distribution in Source Constraint Networks

In recent years, the number of peer-to-peer (P2P) applications has increased significantly. One important problem in many P2P applications is how to efficiently disseminate data from a single source to multiple receivers on the Internet. A successful model used for analyzing this problem is a graph consisting of nodes and edges, with a capacity assigned to each edge. In some situations however, it is inconvenient to use this model. To that end, we propose to study the problem of efficient data dissemination in a source constraint network. A source constraint network is modeled as a graph in which, the capacity is associated with a node, rather than an edge. The contributions of this paper include (a) a quantitative data dissemination in any source constraint network, (b) a set of topologies suitable for data dissemination in P2P networks, and (c) an architecture and implementation of a P2P system based on the proposed optimal topologies. We will present the experimental results of our P2P system deployed on PlanetLab nodes demonstrating that our approach achieves near optimal throughput while providing scalability, low delay and bandwidth fairness among peers.     

A Unifying Theoretical Background for Some Bdd-based Data Structures

In the paper, we propose a general concept (denoted by TBDD) for Boolean functions manipulation that is based on cube transformations. The basic idea is to manipulate a Boolean function by converting it by means of a cube transformation into a function that can be efficiently represented and manipulated in terms of ordered binary decision diagrams (OBDDs). We show that the new concept unifies several BDD–based data structures considerably, and simplifies their manipulation to work with the simple and well–understood data struture of OBDDs. This is especially important for practical applications.Further, to give an example of how TBDDs open new ways in the search for efficient data structures for Boolean functions, we discuss the data structure of typed kFBDDs.     

Metaheuristics for the feedforward artificial neural network (ANN) architecture optimization problem

This article deals with evolutionary artificial neural network (ANN) and aims to propose a systematic and automated way to find out a proper network architecture. To this, we adapt four metaheuristics to resolve the problem posed by the pursuit of optimum feedforward ANN architecture and introduced a new criteria to measure the ANN performance based on combination of training and generalization error. Also, it is proposed a new method for estimating the computational complexity of the ANN architecture based on the number of neurons and epochs needed to train the network. We implemented this approach in software and tested it for the problem of identification and estimation of pollution sources and for three separate benchmark data sets from UCI repository. The results show the proposed computational approach gives better performance than a human specialist, while offering many advantages over similar approaches found in the literature.     

The 2-facility centdian network problem

The p-facility centdian network problem consists of finding the p points that minimize a convex combination of the p-center and p-median objective functions. The vertices and local centers constitute a dominating set for the 1-facility centdian; i.e., it contains an optimal solution for all instances of the problem. Hooker et al. (1991) give a theoretical result to extend the dominating sets for the 1-facility problems to the corresponding p-facility problems. They claim that the set of vertices and local centers is also a dominating set for the p-facility centdian problem. We give a counterexample and an alternative finite dominating set for p=2. We propose a solution procedure for a network that improves the complexity of the exhaustive search in the dominating set. We also provide a very efficient algorithm that solves the 2-centdian on a tree network with complexity O(n²).     

Improving BDD-based attractor detection for synchronous Boolean networks

Boolean networks are an important formalism for modelling biological systems and have attracted much attention in recent years. An important challenge in Boolean networks is to exhaustively find attractors, which represent steady states of a biological network. In this paper, we propose a new approach to improve the efficiency of BDD-based attractor detection. Our approach includes a monolithic algorithm for small networks, an enumerative strategy to deal with large networks, a method to accelerate attractor detection based on an analysis of the network structure, and two heuristics on ordering BDD variables. We demonstrate the performance of our approach on a number of examples and on a realistic model of apoptosis in hepatocytes. We compare it with one existing technique in the literature.     

A comprehensive reconfigurable computing approach to memory wall problem of large graph computation

Graph computation problems that exhibit irregular memory access patterns are known to show poor performance on multiprocessor architectures. Although recent studies use FPGA technology to tackle the memory wall problem of graph computation by adopting a massively multi-threaded architecture, the performance is still far less than optimal memory performance due to the long memory access latency. In this paper, we propose a comprehensive reconfigurable computing approach to address the memory wall problem. First, we present an extended edge-streaming model with massive partitions to provide better load balance while taking advantage of the streaming bandwidth of external memory in processing large graphs. Second, we propose a two-level shuffle network architecture to significantly reduce the on-chip memory requirement while provide high processing throughput that matches the bandwidth of the external memory. Third, we introduce a compact storage design based on graph compression schemes and propose the corresponding encoding and decoding hardware to reduce the data volume transferred between the processing engines and external memory. We validate the effectiveness of the proposed architecture by implementing three frequently-used graph algorithms on ML605 board, showing an up to 3.85 × improvement in terms of performance to bandwidth ratio over previously published FPGA-based implementations.     

Location and Configuration Management in Mobile-Switch ATM Networks

The features of two important application scenarios, supporting mobile switches with fixed end users and mobile switches with mobile users, are dramatically different from those of the traditional wired network structure. To exploit mobile switches, the location and configuration management of mobile switches is essential to handle the mobility and topology change of the wireless/mobile ATM network. In this paper we address the location management and configuration problems of mobile switches in an ATM network. We investigate several aspects of the location management problem including architecture to support switch mobility, mobile switch tracking, and mobile switch locating. We propose an approach that is based on the Private Network-Network Interface (PNNI) protocol. We extend the PNNI protocol to enable it to handle mobile switches. Moreover, we develop analytical models to determine the tracking and locating costs for mobile switches under the proposed scheme. The models illustrate the relation between total cost (tracking cost + locating cost) and peer group size. The models can be used to derive the optimal configuration for an ATM network with mobile switches.     

Generating probabilistic Boolean networks from a prescribed stationary distribution

Modeling gene regulation is an important problem in genomic research. Boolean networks (BN) and its generalization probabilistic Boolean networks (PBNs) have been proposed to model genetic regulatory interactions. BN is a deterministic model while PBN is a stochastic model. In a PBN, on one hand, its stationary distribution gives important information about the long-run behavior of the network. On the other hand, one may be interested in system synthesis which requires the construction of networks from the observed stationary distribution. This results in an inverse problem which is ill-posed and challenging. Because there may be many networks or no network having the given properties and the size of the inverse problem is huge. In this paper, we consider the problem of constructing PBNs from a given stationary distribution and a set of given Boolean Networks (BNs). We first formulate the inverse problem as a constrained least squares problem. We then propose a heuristic method based on Conjugate Gradient (CG) algorithm, an iterative method, to solve the resulting least squares problem. We also introduce an estimation method for the parameters of the PBNs. Numerical examples are then given to demonstrate the effectiveness of the proposed methods.     

Information Security: A Defensive Battle

Abstract

Medium to large networks usually comprise different types of security components. To establish an efficient security architecture, an engineer must fully understand the major role of each security component, its comparative advantage, and natural position. Although this article does not mention every type of the existing and emerging security product, it is possible to apply the battlefield analogies to virtually every type of security solution to simplify the complicated architecture of today's data networks. The basic security platform comprises a layered approach and an efficient way to synchronize security products at different layers of the network.     

MODL: A Bayes optimal discretization method for continuous attributes

While real data often comes in mixed format, discrete and continuous, many supervised induction algorithms require discrete data. Efficient discretization of continuous attributes is an important problem that has effects on speed, accuracy and understandability of the induction models. In this paper, we propose a new discretization method MODL¹, founded on a Bayesian approach. We introduce a space of discretization models and a prior distribution defined on this model space. This results in the definition of a Bayes optimal evaluation criterion of discretizations. We then propose a new super-linear optimization algorithm that manages to find near-optimal discretizations. Extensive comparative experiments both on real and synthetic data demonstrate the high inductive performances obtained by the new discretization method. Editor: Tom Fawcett ¹French patent No. 04 00179.     

STRATEGIC KNOWLEDGE IN AN EXPERT SYSTEM FOR AGRICULTURE

Abstract

Most of the expert systems applied in vegetal pathology treat the problem of selecting treatment in a conventional manner, by means of production rules that associate to each pathology the most adequate chemical product. This makes it difficult to generate useful explanations. In order to generate satisfactory explanations the knowledge of the system must be based on the strategies used by human experts. This article introduces our approach for the identification and representation of strategic knowledge in an expert system for plague control in greenhouses. We present an introduction to the application domain and make an analysis of the strategic knowledge implied. We distinguish between the underlying strategy and the practical strategy used by the expert for the solution of the problem. From this we propose a preliminary architecture based on strategic reasoning agents.     

多媒体会议系统分层通信结构及共算法研究

目前,多媒体会议系统的通信结构主要分为两种：集中式通信结构和全分布式通信结构。在远程多媒体会议系统中,这两种通信结构都很难满足多媒体会议系统的实时性要求。针对这两种通信结构的缺点与不足,该文提出了一种新的通信结构：分层通信结构。这种通信结构可有效减少远程多媒体会议系统的时间延迟。对于不同的媒体混合技术,我们分别给出了构造其最佳分层通信结构的方法及相应算法。实验数据表明,：相对于集中式通信结构和全分布式通信结构而言,分层通信结构可以有效减少远程多媒体会议系统的时间延尺,从而更好的满足远程多媒体会议系统的实时性要求。     

Applying big data and stream processing to the real estate domain

ABSTRACT

In this paper, we propose an architecture that combines Big Data and Stream Processing which can be applied to the Real Estate Domain. Our approach consists of a specialisation of Lambda architecture and it is inspired by some aspects of Kappa architecture. As a proof of this solution, we show a prototype developed following it and a comparison of the three architecture quality models. Finally, we highlight the differences between the proposed architecture and similar ones and draw some future lines following the present approach.     

A task decomposition approach to using neural networks for the interpretation of bioprocess data

During the course of most bioproccss development programs a large amount of process data is generated and stored. However, while these data records contain important information about the process, little or no use is made of this asset. The work described here uses a neural network approach to “learn” to recognize patterns in fermentation data. Neural networks, trained using fermentation data generated from previous runs, are then used to interpret data from a new fermentation. We propose a task decomposition approach to the problem. The approach involves decomposing the problem of bioprocess data interpretation into specific tasks. Separate neural networks are trained to perform each of these tasks which include fault diagnosis, growth phase determination and metabolic condition evaluation. These trained networks are combined into a multiple neural network hierarchy for the diagnosis of bioprocess data. The methodology is evaluated using experimental data from fed-batch, Saccharomyces cerevisiae fermentations. We argue that the task decomposition approach taken here allows for each network to develop a task specific representation and that this in turn, can lead to network activations and connection weights that are more clearly interpretable. These expert networks can now be pruned to remove nodes that do not contribute significant additional information.     

Optimal server allocations for streaming multimedia applications on the Internet

In this paper, we address the server selection problem for streaming applications on the Internet. The architecture we consider is similar to the content distribution networks consisting of geographically dispersed servers and user populations over an interconnected set of metropolitan areas. Server selection issues for Web-based applications in such an environment have been widely addressed; the selection is mostly based on proximity measured using packet delay. Such a greedy or heuristic approach to server selection will not address the capacity planning problem evident in multimedia applications. For such applications, admission control becomes an essential part of their design to maintain Quality of Service (QoS). Our objective in providing a solution to the server selection problem is threefold: first, to direct clients to the nearest server; second, to provide multiple sources to diffuse network load; third, to match server capacity to user demand so that optimal blocking performance can be expected. We accomplish all three objectives by using a special type of Linear Programming (LP) formulation called the Transportation Problem (TP). The objective function in the TP is to minimize the cost of serving a video request from user population x using server y as measured by network distance. The optimal allocation between servers and user populations from TP results in server clusters, the aggregated capacity of each cluster designed to meet the demands of its designated user population. Within a server cluster, we propose streaming protocols for request handling that will result in a balanced load. We implement threshold-based admission control in individual servers within a cluster to enforce the fair share of the server resource to its designated user population. The blocking performance is used as a trigger to find new optimal allocations when blocking rates become unacceptable due to change in user demands. We substantiate the analytical model with an extensive simulation for analyzing the performance of the proposed clustered architecture and the protocols. The simulation results show significant difference in overall blocking performance between optimal and suboptimal allocations in as much as 15% at moderate to high workloads. We also show that the proposed cluster protocols result in lower packet loss and latencies by forcing path diversity from multiple sources for request delivery.