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Recommender systems are found in many modern web sites for applications such as recommending products to customers. In this paper we propose a new method for recommender system that employs the users' social network in order to provide better recommendation for media items such as movies or TV shows. As part of this paper we develop a new paradigm for incorporating the feedback of the user's friends. A field study that was conducted on real subjects indicates the strengths and the weaknesses of the proposed method compared to other simple and classic methods. The system is envisioned to function as a service for recommending personalized media (audio, video, print) on mobile phones, online media portals, sling boxes, etc. It is currently under development within Deutsche Telekom Laboratories - Innovations of Integrated Communication projects. [ABSTRACT FROM AUTHOR]

Heterogeneous, distributed multi-databases (data warehouses, grids) are growing day by day as businesses demand more economical and flexible solutions for their data storage and processing needs. One of the main issues obstructing the large scale deployment of distributed multi-databases is latencies involved in data retrieval. Such problems are usually solved with the help of persistent caches. The paper presents a semantic caching architecture which supports faster semantic matching in query processing. This paper enhances the existing architecture of semantic cache and provides the mapping of hierarchical indexing scheme over it with the help of case study in order to represent the working of architecture. This paper also provides experimental results of the query processing over updated architecture and provides the discussion over comparison of the extended architecture with the previously proposed architecture. [ABSTRACT FROM AUTHOR]

In this paper, new natural element approximations are proposed, in order to address issues associated with incompressibility as well as to increase the accuracy in the Natural Element Method (NEM). The NEM exhibits attractive features such as interpolant shape functions or auto-adaptive domain of influence, which alleviates some of the most common difficulties in meshless methods. Nevertheless, the shape functions can only reproduce linear polynomials, and in contrast to moving least squares methods, it is not easy to define interpolations with arbitrary approximation consistency. In order to treat mechanical models involving incompressible media in the framework of mixed formulations, the associated functional approximations must satisfy the well known inf-sup, or LBB condition. The first proposed approach constructs richer NEM approximation schemes by means of bubbles associated with the topological entities of the underlying Delaunay tessellation, allowing to pass the LBB and to remove pressure oscillations in the incompressible limit. Despite of its simplicity, this approach does not construct approximation with higher order consistency. The second part of the paper deals with a discussion on the construction of second-order accurate NEM approximations. For this purpose, two techniques are investigated : (a) the enrichment in the MLS framework of the bubbles with higher-order polynomials and (b) the use of a new Hermite-NEM formulation. [ABSTRACT FROM AUTHOR]

In this paper we present the application of the meshfree method of finite spheres to the solution of thin and thick plates composed of isotropic as well as functionally graded materials. For the solution of such problems it is observed that using Gaussian and adaptive quadrature schemes are computationally inefficient. In this paper a new technique, presented in [26, 21], in which the integration points and weights are generated using genetic algorithms and stored in a lookup table using normalized coordinates as part of an offline preprocessing step, is shown to provide significant reduction of computational time without sacrificing accuracy. [ABSTRACT FROM AUTHOR]

This paper extends the formulation of frictionless impact analysis of planar deformable bodies presented in [6] to frictional impact. In the approach presented in that paper, a linear complementarity problem (LCP) on position level based on the Signorini conditions for the impact problem of continua was formulated. For this purpose, the normal gap distance between possible contact points was represented in terms of normal impact forces. Now, in order to consider the friction, this formulation may be appended to the formulation of tangential contact forces developed for continual contact in [4, 5]. The key issue behind this approach arises from this fact that in the case of deformable bodies, the behavior of impact in the tangential direction is similar to that of the continual contact for a short period of time. However, it is obvious that this assumption is valid only for impact analysis of deformable bodies and in the case of rigid bodies impact analysis, these two events must be distinguished. [ABSTRACT FROM AUTHOR]

The paper presents results of eccentric flows of amaranth and flaxseed in a flat-bottomed Plexiglas model of silo. The images of the flowing grains were registered by a camera of high resolution. The Digital Particle Image Velocimetry technique was used to analyse the seed flow. For this purpose long sequences of digital images of the flowing grains were processed to obtain full velocity vector fields as well as selected velocity profiles for the analysed discharge processes. Deformations occurred in the flowing material are also evaluated. The experiments were made in the model of three different roughness of the walls, the smooth and lined with sand paper of two different roughness. To investigate effects of eccentricity, both during filling and discharge, the model was filled centrally at the symmetry axis, close to the left edge of the silo, and near its right edge. The filling pipe was also located centrally or eccentrically above the model. The differences between different modes of flow are described and analysed in detail. [ABSTRACT FROM AUTHOR]

Pulp for making paper is produced in the groundwood (GW) process from logs. In batch-type GW-machines the control of the log loading is a demanding task because of the complicated log group dynamics where also the heterogeneous wood material has a great influence on the batch behaviour. It is essential to achieve better understanding of the regularities occurring in the process in order to improve the loading control and thus the quality and efficiency of pulp production. For this purpose a numerical model of the complete process has been built up and the dynamic system behaviour has been studied by time domain simulation. In this paper the focus is in the description of the log batch behaviour. Three separate approaches have been adopted to model the batch. [ABSTRACT FROM AUTHOR]

In this paper we describe the concepts of Time Series, Neural Networks, Modular Neural Networks, and Parallelism. Modular Neural Networks and Parallel Processing for Time Series Forecasting of Tomato Prices in Mexico are described in this paper. A particular modular neural network architecture implemented in parallel was used. Simulation results with the modular neural network approach for this application are very good. [ABSTRACT FROM AUTHOR]

In a previous paper we presented the implementation of the Fuzzy Sugeno Integral formulas developed with Matlab 6.5™. The programs are now included in a System called "Herramientas Multired" ("hmr"). In this paper we will review an example of modular neural network for image recognition, using images divided in four parts. The Fuzzy Sugeno Integral was used to make a final decision for pattern recognition. [ABSTRACT FROM AUTHOR]

Intelligent Agents have originated a lot of discussion about what they are, and how they are different from general programs. We describe in this paper a new paradigm for intelligent agents. This paradigm helped us deal with failures in an independent and efficient way. We proposed three types of agents to treat the system in a hierarchic way. A new way to visualize fault tolerant systems (FTS) is proposed, in this paper with the incorporation of intelligent agents, which as they grow and specialized create the Multi-Agent System (MAS). The MAS contains a diversified range of agents, which depending on the perspective will be specialized or evolutionary (from our initially proposal) they will be specialized for the detection and possible solution of errors that appear in an FTS). The initial structure of the agent is proposed in [1] and it is called a reflected agent with an internal state and in the Method MeCSMA [2]. [ABSTRACT FROM AUTHOR]

Short message service (SMS) is a mechanism of delivery of short messages over the mobile networks. Because of its popularity, SMS is now a ubiquitous way of communicating. In order to improve its efficiency and intelligence, this paper examines methods for analyzing message entry techniques by using a query against a knowledge base which has been acquired background information. People can also use SMS to perform reasoning or discover implicit knowledge. This study proposes application and architecture of intelligent knowledge query answering system based on SMS. Short message processing module and knowledge query module are two main parts of this system. The first module includes correlative AT command & PDU data format analysis and processing flow of short message. The second module analyses query sentence by using natural language understanding technology which tries to understand query sentence by using a conceptual network model to organize knowledge. Conceptual network is adopted to systematically organize relationships among concepts and represent different features of each concept so as to substantially improve people's ability to get specific information in response to specific needs. Furthermore, the paper discusses PDU coding format and question analysis in detail. [ABSTRACT FROM AUTHOR]

Designing the approximate reasoning matching schemes and the corresponding algorithms with similarity relation ℚ instead of equivalence relation ℚ can reflect the nature of approximate reasoning and meet more application expectations. This paper bases on similarity relation ℚ and introduces the type V matching scheme and the corresponding approximate reasoning type V ℚ-algorithm with the given input A* and knowledge A → B. We also present type V completeness and type V perfection of knowledge base K in ℚ-logic ℂℚ in this paper. [ABSTRACT FROM AUTHOR]

This paper considers a class of fuzzy linear programming (FLP) problems where the coefficients of the objective function are characterized by LR-fuzzy intervals or LR-fuzzy numbers with the same shapes. The existing methods mainly focus on the problems whose fuzzy coefficients are linear shape. In this paper, we are interested in the solution of FLP problems whose fuzzy coefficients are nonlinear shapes. Specifically, we show that the FLP problem can be transformed into a multi-objective linear programming problem with four objectives if LR-fuzzy numbers have the same shape, and therefore the optimal solutions can be found by the relationships between FLP problems and the resulting parameter linear programming problems. An example is also presented to demonstrate that the method is invalid if fuzzy coefficients have different shapes. Then, we discuss the relationships among FLP problems, possibility and necessity maximization problems, and obtain a conclusion that all Pareto optimal solutions of the possibility-necessity approaches are the subset of the weak dominated solutions of the FLP problem. Finally, one numerical example is given to illustrate the solution procedure. [ABSTRACT FROM AUTHOR]

A novel method is proposed in the paper, called A Region-Based Image Retrieval Method with Fuzzy Features (RBIRFF), which partitions images into regions firstly, then computes each region fuzzy features for color, texture and shape with fuzzy set theory, finally computes similarity between images and gets the retrieval results according to the similarity sequences. The design and realization of RBIRFF system is also introduced in the paper, and its retrieval effectiveness is evaluated by experiments. [ABSTRACT FROM AUTHOR]

As the matter fact that there are many joints in the hexapod walking Bio-robot and the calculation of its walking trajectory, as well as the problem of the acceleration leap caused by adopting elementary function as the robot's feet trajectory. This paper puts forward using cubic spline curves as the robots feet trajectory and designing the robot's walking gait. Then the authors conducted kinematical simulation and analyses on the model of the bionic hexapod walking robot which have done with the use of Solidworks and MSC.ADAMS. Through the simulation experiments, we validated the applicability of designed triangle gait and the feasibility of using cubic spline curve as the feet trajectory. This paper shows principle, method and course on the trajectory simulation of hexapod walking Bio-robot in detail. The way to find a converse solution under the condition of ADAMS is found, which simplifies the theory calculation and improves design efficiency. [ABSTRACT FROM AUTHOR]

The update of rules is the key to success for rule-based network intrusion detection system because of the endless appearance of new attacks. To efficiently extract classification rules from the vast network traffic data, this paper gives a new approach based on Particle Swarm Optimization (PSO) and introduces a new coding scheme called "indexical coding" in accord with the feature of the network traffic data. PSO is a novel optimization technique and has been shown high performance in numeric problems, but few researches have been reported in rule learning for IDS that requires a high level representation of the individual, this paper makes a study and demonstrates the performance on the 1999 KDD cup data. The results show the feasibility and effectiveness of it. [ABSTRACT FROM AUTHOR]

This paper presents a method to predict pressure distribution and residual oil distribution for water flooding oilfield. To meet the demands of the integral design of oilfield development plan and the high-efficiency exploitation of oilfield, this paper proposes an optimization method for single stimulation measure and integral adjustment of a tract based on analysis of oilfield performance, economic evaluation and professional experience. This integral optimization method is helpful to decision making and optimize the adjustment project. Enforcement of the plan will help not only complete the mission of production, but can bring the maximum incomes under present conditions. So, this study is significant to improve the efficiency and effectiveness of field work. [ABSTRACT FROM AUTHOR]

Horizontal Collaboration Fuzzy C-Means (HC-FCM) is such a clustering method that does clustering in a set of patterns described in some feature space by considering some external sources of clustering information which are about the same set of patterns but described in different feature spaces. Because of potential security and privacy restrictions, the external information is provided only by some partition matrices obtained by Fuzzy C-Means (FCM). HC-FCM quantifies the influence of these partition matrices by constructing a new objective function which combines these partition matrices with suitable weights. These weights are crucial in HC-FCM, but how to determine them still remains a problem. This paper first puts forward a concept of similarity measure of partition matrices, then based on this similarity measure, proposes some simple and practical methods for automatically determining the weights, and finally gives a new version of HC-FCM, named auto-weighted HC-FCM. With the work of this paper, HC-FCM becomes more practical. Some experiments are carried to show the performance and reveal the validity of our method. [ABSTRACT FROM AUTHOR]

This papers analyses the choice, combination and optimization of maintenance measures in the maintenance of oil and gas pipelines, initially proposes 4 factors system that influence effect of maintenance on pipelines, which includes person factor, fieldwork difficult degree, fieldwork management and environment condition, establishes hierarchy model of maintenance measures, introduces fuzzy analytical hierarchy process based on triangular fuzzy number, and compares the effects of different maintenance measures in order to determine the weight under single factor condition and composite matter-element of maintenance measures. On the basis of the above analysis, the paper proposes 4 methods of maintenance projects fund distribution based on different weight of efficiency factor of maintenance measures under the condition of limited charge. Using the fuzzy-gray-element theory combining fuzzy mathematics, gray theory and matter-element analysis, the paper analyses maintenance measure fund distribution based on different weight of efficiency factor of maintenance measures with fuzzygray-element correlation decision-making analysis, and conclusively determines optimization of maintenance project according to criteria of maximized correlative degree. [ABSTRACT FROM AUTHOR]

Attribute reduction is one of the key problems in the theoretical research of Rough Set, and many algorithms have been proposed and studied about it. These methods may be divided into two strategies (addition strategy and deletion strategy), which are based on adopting different heuristics or fitness functions for attribute selection. In this paper, we propose a new algorithm based on frequency of attribute appearing in the discernibility matrix. It takes the core as foundation, and joins the most high frequency attribute in the discernibility matrix, until the discernibility matrix is empty. In order to find optimum Pawlak reduction of decision table, this paper adds the converse eliminate action until it cannot delete. The time complexity of the algorithm in this paper is O(mn2), and the testing indicates that the performance of the method proposed in this paper is faster than that of those algorithms. [ABSTRACT FROM AUTHOR]

Theories of minimization of fuzzy automata have been developed by several authors, and most of which applied methods of algebraic theory, more specifically, the equivalence relation, the quotient space and equivalence class, congruence and homomorphism, in studying fuzzy automata. In this paper, we also apply the algebraic theory in the study of lattice automata, and obtain some results similar to the ones of fuzzy automata. In this paper, concepts of refining equivalence and refining congruence are defined, the quotient lattice automaton with respect to refining congruence is formulated, the concept of lattice automaton is reviewed, the equivalence of a lattice automaton and its quotient automaton is proved, the minimal property of quotient automaton is shown, and the minimization algorithm of lattice automata is proposed. The main idea of this paper is that by putting forward the concepts of refining equivalence and refining congruence, we can derive the quotient lattice automaton with respect to refining congruence, and via showing that the quotient lattice automaton is not only equivalent to the lattice automaton but also a minimal automaton, we obtain the minimization of a lattice automaton, and thus get the minimization algorithm of lattice automata. [ABSTRACT FROM AUTHOR]

This paper introduces and investigate the ℚ-knowledge mass, ℚ-knowledge universe, ℚ-knowledge base and ℚ-automatic reasoning system bases on the similarity logic ℂℚ. We also present and investigate the ℚ-true level k knowledge circle, extended ℚ- knowledge base, extended Qℚ-automatic reasoning system and the level (k,j) perfection of extended Qℚ- knowledge base $$ \mathbb{K}^V $$ in this paper. [ABSTRACT FROM AUTHOR]

This paper presents an assignment matrix based heuristic attribute reduction method, states the significance of attributes in the incomplete decision table by introducing assignment matrices and their distances from one another, which is then used as heuristic information for selecting attributes. This algorithm has a polynomial time complexity. Seeking the minimal relative reduction in a decision table is typically a NP-hard problem, and its complexity can be reduced by using the algorithm provided in this paper. An example shows this algorithm can achieve the minimal relative reduction of incomplete decision table. [ABSTRACT FROM AUTHOR]

The paper proposes the original node of the spreading path of the virus must be the source of the virus. However most network virus have more than one spreading channels and the hints of the virus' spreading are also unclear. The paper establishes a fuzzy source tracing equations for the network virus. Working out the equations repeatedly, the paper gets the spreading path of the virus; thus the start node of the path should be the source of the virus in the network. Finally, the paper carries out the simulation test. The results of the test verify our tracing model and methods of working out the equations. Thus the paper opens a theoretic fuzzy way to tracing the source of net viruses. [ABSTRACT FROM AUTHOR]

In this paper we give an introduction to modelling the natural gas value chain including production, transportation, processing, contracts, and markets. The presentation gives insight in the complexity of planning in the natural gas supply chain and how optimization can help decision makers in a natural gas company coordinate the different activities. We present an integrated view from the perspective of an upstream company. The paper starts with decribing how to model natural gas transportation and storage, and at the end we present a stochastic portfolio optimization model for the natural gas value chain in a liberalized market. [ABSTRACT FROM AUTHOR]

The paper has two main themes: The first theme is to give the reader an introduction to modern methods for systems of conservation laws. To this end, we start by introducing two classical schemes, the Lax-Friedrichs scheme and the Lax-Wendroff scheme. Using a simple example, we show how these two schemes fail to give accurate approximations to solutions containing discontinuities. We then introduce a general class of semi-discrete finite-volume schemes that are designed to produce accurate resolution of both smooth and nonsmooth parts of the solution. Using this special class we wish to introduce the reader to the basic principles used to design modern high-resolution schemes. As examples of systems of conservation laws, we consider the shallow-water equations for water waves and the Euler equations for the dynamics of an ideal gas. The second theme in the paper is how programmable graphics processor units (GPUs or graphics cards) can be used to efficiently compute numerical solutions of these systems. In contrast to instruction driven micro-processors (CPUs), GPUs subscribe to the data-stream-based computing paradigm and have been optimised for high throughput of large data streams. Most modern numerical methods for hyperbolic conservation laws are explicit schemes defined over a grid, in which the unknowns at each grid point or in each grid cell can be updated independently of the others. Therefore such methods are particularly attractive for implementation using data-stream-based processing. [ABSTRACT FROM AUTHOR]

Using graphics hardware for general-purpose computations (GPGPU) has for selected applications shown a performance increase of more than one order of magnitude compared to traditional CPU implementations. The intent of this paper is to give an introduction to the use of graphics hardware as a computational resource. Understanding the architecture of graphics hardware is essential to comprehend GPGPU-programming. This paper first addresses the fixed functionality graphics pipeline, and then explains the architecture and programming model of programmable graphics hardware. As the CPU is instruction driven, while a graphics processing unit (GPU) is data stream driven, a good CPU algorithm is not necessarily well suited for GPU implementation. We will illustrate this with some commonly used GPU algorithms. The paper winds up with examples of GPGPU-research at SINTEF within simulation, visualization, image processing, and geometry processing. [ABSTRACT FROM AUTHOR]

This paper addresses a new, unified thermo-mechanical constitutive model for unsaturated soils through a coupled study. In the context of elasto-plasticity and the critical state theory, the model uses the concepts of multi-mechanism and bounding surface theory. This advanced constitutive approach involves thermo-plasticity of saturated and unsaturated soils. Bishop's effective stress framework is adopted to represent the stress state in the soil. This stress is linked to the water retention curve, which is represented by an elasto-plastic model. Attention is focused particularly on the coupling relations inferred from this unified thermo-hydro-mechanical (THM) study. Finally, the theoretical aspects of the paper are supported by comparisons between numerical simulations and experimental results. [ABSTRACT FROM AUTHOR]

The CORBA based middlewares are appropriate for Telematics system which is an important and practical infrastructure for ubiquitous convergence. And fault tolerance is a first priority for real time characteristics of transport information provided by Telematics systems. There are many methods to enhance the fault tolerance of CORBA based systems. Among these methods, this paper proposes the one of enabling seamless information services where systems based on the CORBA suffer from object faults which occur processing real time transport information. Namely, this paper observes a method to deal efficiently with object faults occurring in 3 tier architecture environments. It is possible to replicate objects as a method to enhance fault tolerance in preparation against object faults. This paper presents a method of enhancing Telematics fault tolerance and maintaining service continuity by continuing to operate systems until faults of the Fault Tolerant (FT)-CORBA protocol itself is recovered. The proposed agent enhances Telematics performance by adjusting the minimum number of object replicas for real time transport information services during system operation. The evaluation is performed by comparing costs for system fault tolerance which consist of communication traffic and S/W complexities. [ABSTRACT FROM AUTHOR]

Intelligent Agents have originated a lot discussion about what they are, and how they are a different from general programs. We describe in this paper a new paradigm for intelligent agents, This paradigm helped us deal with failures in an independent and efficient way. We proposed these types of agents to treat the system in a hierarchical way. The Agent Node is also described. A new method to visualize fault tolerant system (FTS) is proposed, in this paper witch the incorporation of intelligent agents, which as they grow and specialized create the Agent System (MAS).One is the communications diagrams of the each of the agents, described in diagrams of transaction of states. [ABSTRACT FROM AUTHOR]

Single input Fuzzy TPE systems, as proposed by Sudkamp and Hammel, allow a more efficient computational inference of a single input fuzzy rule base. This paper shows that it is possible to generalize single input Fuzzy TPE systems to 2-input systems, therefore extending its range of possible applications. The paper presents the details and proof of the extension validity, and shows benchmark results comparing the 2-input Fuzzy TPE with classical inference systems. [ABSTRACT FROM AUTHOR]

The Particle Swarm Optimization (PSO) and the Genetic Algorithms (GA) have been used successfully in solving problems of optimization with continuous and combinatorial search spaces. In this paper the results of the application of PSO and GAs for the optimization of mathematical functions is presented. These two methodologies have been implemented with the goal of making a comparison of their performance in solving complex optimization problems. This paper describes a comparison between a GA and PSO for the optimization of a complex mathematical function. [ABSTRACT FROM AUTHOR]

Process Planning activities are significantly based on experience and technical skill. In spite of the great efforts made for planning automation, this activity continues being made in manual form. Process Planning activities are significantly based on experience and technical skills. The advent of the CAM systems (Computer Aided Manufacturing) has partially close the gap left between the Automated Design and Manufacture. Meanwhile, a great dose of manual work still exists and investigation in this area is still necessary. This paper presents the application of a multi objective genetic algorithm for the definition of the optimal cutting parameters. The objective functions consider the production rate and production cost in turning operations. The obtained Pareto front is compared to high efficiency cutting range. This paper also describes one application of the developed mechanism using an example. [ABSTRACT FROM AUTHOR]

We propose a new method for handwriting recognition that utilizes geometric features of letters. The paper deals with recognition of isolated handwritten characters using an artificial neural network. The characters are written on a regular sheet of paper using a pen, and then they are captured optically by a scanner and processed to a binary image which is analyzed by a computer. In this paper we present a new method for off-line handwriting recognition and also describe our research and tests performed on the neural network. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for fuzzy rule extraction from trained support vector machines (SVMs) for multi-class problems. SVMs have been applied to a wide variety of application. However, SVMs are considered "black box models", where no interpretation about the input-output mapping is provided. Some methods to reduce this limitation have already been proposed, however, they are restricted to binary classification problems and to the extraction of symbolic rules with intervals or functions in their antecedents. Hence, to improve the interpretability of the generated rules, this paper presents a new model for extracting fuzzy rules from a trained SVM. Moreover, the proposed model was developed for classification in multi-class problems. The generated fuzzy rules are presented in the format "IF x1 is C1 AND x2 is C2 AND ... AND xn is Cn, THEN x = (x1, x2,... xn) is of class A", where C1, C2..., Cn are fuzzy sets. The proposed method was evaluated in four benchmark databases (Bupa Liver Disorders, Wisconsin Breast Cancer, Iris and Wine). The results obtained demonstrate the capacity of the proposed method to generate a set of interpretable rules that explains the database and the influence of the input variables in the determination of the final class. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent home environment with an adaptive type-2 fuzzy logic (AT2FL) method is developed. The considered system is mainly for elderly and disabled persons and catches the users' intention through recognizing their hand gestures instead of remote controller. However, during the recognition procedure, to distinguish between the command and ordinary gestures needs a very rigorous procedure, and to cope with that, the AT2FL approach is proposed in this paper. The presented algorithm is applied to control home appliances, and some experimental results will be shown to verify the feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

We describe in this paper a new method for response integration in modular neural networks using type-2 fuzzy logic. The modular neural networks were used in human person recognition. Biometric authentication is used to achieve person recognition. Three biometric characteristics of the person are used: face, fingerprint, and voice. A modular neural network of three modules is used. Each module is a local expert on person recognition based on each of the biometric measures. The response integration method of the modular neural network has the goal of combining the responses of the modules to improve the recognition rate of the individual modules. We show in this paper results of a type-2 fuzzy approach for response integration that improves performance over type-1 fuzzy logic approaches. [ABSTRACT FROM AUTHOR]

In this paper we present an approximation to the mutual information between a single variable and a set of variables. The main aim of our approach is to reduce the amount of sufficient statistics (i.e. frequency counts) required to calculate the mutual information. To do so, we use the chain rule and assume different independence statements between variables. We will use our approximation to calculate the MDL of a given Bayesian network. We will show that our approximated approach to the MDL measure is score equivalent and we will use it in order to learn Bayesian networks from data. We will experimentally see that learning algorithms that use our approach obtain high quality Bayesian networks. We also note that our approach can be used in any information based measures. [ABSTRACT FROM AUTHOR]

Bayesian Networks (BN) are often sought as useful descriptive and predictive models for the available data. Learning algorithms trying to ascertain automatically the best BN model (graph structure) for some input data are of the greatest interest for practical reasons. In this paper we examine a number of evolutionary programming algorithms for this network induction problem. Our algorithms build on recent advances in the field and are based on selection and various kinds of mutation operators (working at both the directed acyclic and essential graph level). A review of related evolutionary work is also provided. We analyze and discuss the merit and computational toll of these EP algorithms in a couple of benchmark tasks. Some general conclusions about the most efficient algorithms, and the most appropriate search landscapes are presented. [ABSTRACT FROM AUTHOR]

The development of Bayesian classifiers is frequently accomplished by means of algorithms that are highly data-driven. However, for many domains dataavailability is scarce such that the resulting classifiers show poor performance. Even if performance is acceptable, Bayesian classifier structures are highly restricted and may therefore be unintelligable to the user. In this paper we address both issues. In the first part, we explore the trade-offs between classifiers constructed from clinical background knowledge and classifiers learned from a small clinical dataset. It is shown that the construction of classifiers from (partial) background knowledge is a feasible approach. In the second part, we introduce a construction algorithm that allows for a less restricted classifier structure, allowing easier clinical interpretation. [ABSTRACT FROM AUTHOR]

The central role played by uncertainty in medical decision making explains why medicine was amongst the first areas where applications based on Bayesian networks were developed. Biomedical research is firmly grounded on statistical methods; new methods are adopted only slowly by the field. During the past decade, however, Bayesian networks have become important tools for building decision-support systems in medicine and are now steadily becoming main stream. More recently, Bayesian networks have also been adopted as analytic tools in human biology, mainly in research that aims to elucidate the biological mechanisms underlying disease. In this paper, we review some of the applications in both medicine and human biology and we make an attempt to unravel some of the characteristics of Bayesian networks in biomedicine. [ABSTRACT FROM AUTHOR]

When using the influence diagrams framework for solving a decision problem with several different quantitative utilities, the traditional approach has been to convert the utilities into one common currency. This conversion is carried out using a tacit transformation, under the assumption that the converted problem is equivalent to the original one. In this paper we present an extension of the influence diagram framework. The extension allows for these decision problems to be modelled in their original form. We present an algorithm that, given a linear conversion function between the currencies of the original utilities, discovers a characterisation of all other such functions, which induce the same optimal strategy. As this characterisation can potentially be very complex, we give methods to present it in an approximate way. [ABSTRACT FROM AUTHOR]

Sensitivity analysis is a general technique for investigating the robustness of the output of a mathematical model and is performed for various different purposes. The practicability of conducting such an analysis of a probabilistic network has recently been studied extensively, resulting in a variety of new insights and effective methods, ranging from properties of the mathematical relation between a parameter and an output probability of interest, to methods for establishing the effects of parameter variation on decisions based on the output distribution computed from a network. In this paper, we present a survey of some of these research results and explain their significance. [ABSTRACT FROM AUTHOR]

Over the past decades there has been considerable research to develop various dynamic forms of Bayesian Networks (BNs) and a parallel development in the field of causal BNs. However, linking these two fields is subtle. In this paper we demonstrate that, for classes of models exhibiting mass balance, it is necessary to first redefine the stochastic variables in a process using a decomposition which gives rise to a class of particular Dynamic Linear Models. These models on the transformed space can be interpreted as a dynamic form of a causal BN. A manipulation algebra is then defined to enable the prediction of effects of interventions that would not have been obtainable using the current Causal algebras. The necessary deconstruction of the processes and the algorithms to determine the effects of manipulations on the original process are demonstrated using a simple example of a supply chain in a hypothetical product market. [ABSTRACT FROM AUTHOR]

We review the Lévy-driven interest rate theory that has been developed in recent years. The intimate relations between the various approaches, as well as the differences, are outlined. The main purpose of this paper is to elaborate on calibration in the real world as well as in the risk-neutral setting. [ABSTRACT FROM AUTHOR]

To prove their formulae for the moments of the characteristic polynomial of the generic matrix of U(N), Keating and Snaith [8] (see also Keating [7]) use Selberg's integrals as a ‘black box.' In this note, we point out some identities in law which are equivalent to the expressions of Selberg's integrals and which involve beta, gamma, and normal variables. However, this is a mere probabilistic translation of Selberg's results, and does not provide an independent proof of them. An outcome of some of these translations is that certain logarithms of (Vandermonde) random discriminants are self-decomposable, which hinges on the self-decomposability of the logarithms of the beta (a, b) (2a +b = ≥ 1) and gamma (a > 0) variables. Such self-decomposability properties have been of interest in some joint papers with D. Madan. [ABSTRACT FROM AUTHOR]

This paper provides a brief tutorial on the notions of a zero volatility (ZV) spread and an option adjusted spread (OAS), as applied to fixed income securities. Using the standard definitions, it is shown that the zero volatility spread measures the percentage of a security's spread due to any embedded options and any mispricings. The mispricings could be due to either market or model error. In contrast, the OAS only measures the percentage of the security's spread due to mis-pricings. Refinements and alternative measures of a bond's embedded optionality and mispricings are also provided. [ABSTRACT FROM AUTHOR]

A number of remarkable properties of gamma processes are gathered in this paper, including realisation of their bridges, absolute continuity relationships, realisation of a gamma process as an inverse local time, and the effect of a gamma process as a time change. Some of them are put in perspective with their Brownian counterparts. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new p2p network based location search algorithm. The algorithm is built upon the concept of a gradient search and is applicable to unstructured networks. It is inspired by a biological phenomenon called haptotaxis. The algorithm performs much better than the random walk algorithm and is also more efficient than flooding. We also present some mathematical reasoning to explain the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

Nature is a source of inspiration for computational techniques which have been successfully applied to a wide variety of complex application domains. In keeping with this we examine Cell Signaling Networks (CSN) which are chemical networks responsible for coordinating cell activities within their environment. Through evolution they have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. Realising (and evolving) Artificial Cell Signaling Networks (ACSNs) may provide new computational paradigms for a variety of application areas. In this paper we introduce an abstraction of Cell Signaling Networks focusing on four characteristic properties distinguished as follows: Computation, Evolution, Crosstalk and Robustness. These properties are also desirable for potential applications in the control systems, computation and signal processing field. These characteristics are used as a guide for the development of an ACSN evolutionary simulation platform. Following this we describe a novel class of Artificial Chemistry named Molecular Classifier Systems (MCS) to simulate ACSNs. The MCS can be regarded as a special purpose derivation of Hollands Learning Classifier System (LCS). We propose an instance of the MCS called the MCS.b that extends the precursor of the LCS: the broadcast language. We believe the MCS.b can offer a general purpose tool that can assist in the study of real CSNs in Silico The research we are currently involved in is part of the multi disciplinary European funded project, ESIGNET, with the central question of the study of the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs. [ABSTRACT FROM AUTHOR]