A Simple Fractionally Integrated Model with a Time-varying Long Memory Parameter <Emphasis Type="Italic">d</Emphasis><Subscript><Emphasis Type="Italic">t</Emphasis></Subscript>

This paper generalizes the standard long memory modeling by assuming that the long memory parameter d is stochastic and time-varying: we introduce a STAR process on this parameter characterized by a logistic function. We propose an estimation method of this model. Some simulation experiments are conducted. The empirical results suggest that this new model offers an interesting alternative competing framework to describe the persistent dynamics in modeling some financial series.      

Nonblocking <Emphasis Type="Italic">k</Emphasis>-Compare-Single-Swap

The current literature offers two extremes of nonblocking software synchronization support for concurrent data structure design: intricate designs of specific structures based on single-location operations such as compare-and-swap (CAS), and general-purpose multilocation transactional memory implementations. While the former are sometimes efficient, they are invariably hard to extend and generalize. The latter are flexible and general, but costly. This paper aims at a middle ground: reasonably efficient multilocation operations that are general enough to reduce the design difficulties of algorithms based on CAS alone. We present an obstruction-free implementation of an atomic k -location-compare single-location-swap (KCSS) operation. KCSS allows for simple nonblocking manipulation of linked data structures by overcoming the key algorithmic difficulty in their design: making sure that while a pointer is being manipulated, neighboring parts of the data structure remain unchanged. Our algorithm is efficient in the common uncontended case: A successful k-location KCSS operation requires only two CAS operations, two stores, and 2k noncached loads when there is no contention. We therefore believe our results lend themselves to efficient and flexible nonblocking manipulation of list-based data structures in today’s architectures. A preliminary version of this paper appeared in the Proceedings of the Fifteenth Annual ACM Symposium on Parallel Algorithms and Architectures, pages 314–323, San Diego, California, USA, 2003.     

An a posteriori error estimate and a comparison theorem for the nonconforming <Emphasis Type="Italic">P</Emphasis><Subscript>1</Subscript> element

A posteriori error estimates for the nonconforming P ₁ element are easily determined by the hypercircle method via Marini’s observation on the relation to the mixed method of Raviart–Thomas. Another tool is Ainsworth’s application of the hypercircle method to mixed methods. The relation on the finite element solutions is also extended to an a priori relation of the errors, and the errors of four different finite element methods can be compared.      

An <Emphasis Type="Italic">hp</Emphasis>-local Discontinuous Galerkin Method for Parabolic Integro-Differential Equations

In this article, a priori error bounds are derived for an hp-local discontinuous Galerkin (LDG) approximation to a parabolic integro-differential equation. It is shown that error estimates in L ²-norm of the gradient as well as of the potential are optimal in the discretizing parameter h and suboptimal in the degree of polynomial p. Due to the presence of the integral term, an introduction of an expanded mixed type Ritz-Volterra projection helps us to achieve optimal estimates. Further, it is observed that a negative norm estimate of the gradient plays a crucial role in our convergence analysis. As in the elliptic case, similar results on order of convergence are established for the semidiscrete method after suitably modifying the numerical fluxes. The optimality of these theoretical results is tested in a series of numerical experiments on two dimensional domains.     

State‐of‐the‐Art in GPU‐Based Large‐Scale Volume Visualization

This survey gives an overview of the current state of the art in GPU techniques for interactive large‐scale volume visualization. Modern techniques in this field have brought about a sea change in how interactive visualization and analysis of giga‐, tera‐ and petabytes of volume data can be enabled on GPUs. In addition to combining the parallel processing power of GPUs with out‐of‐core methods and data streaming, a major enabler for interactivity is making both the computational and the visualization effort proportional to the amount and resolution of data that is actually visible on screen, i.e. ‘output‐sensitive’ algorithms and system designs. This leads to recent output‐sensitive approaches that are ‘ray‐guided’, ‘visualization‐driven’ or ‘display‐aware’. In this survey, we focus on these characteristics and propose a new categorization of GPU‐based large‐scale volume visualization techniques based on the notions of actual output‐resolution visibility and the current working set of volume bricks—the current subset of data that is minimally required to produce an output image of the desired display resolution. Furthermore, we discuss the differences and similarities of different rendering and data traversal strategies in volume rendering by putting them into a common context—the notion of address translation. For our purposes here, we view parallel (distributed) visualization using clusters as an orthogonal set of techniques that we do not discuss in detail but that can be used in conjunction with what we present in this survey.     

Finding a Minimum-depth Embedding of a Planar Graph in <Emphasis Type="Italic">O</Emphasis>(<Emphasis Type="Italic">n</Emphasis><Superscript>4</Superscript>) Time

Consider an n-vertex planar graph G. The depth of an embedding Γ of G is the maximum distance of its internal faces from the external one. Several researchers pointed out that the quality of a planar embedding can be measured in terms of its depth. We present an O(n ⁴)-time algorithm for computing an embedding of G with minimum depth. This bound improves on the best previous bound by an O(nlog n) factor. As a side effect, our algorithm improves the bounds of several algorithms that require the computation of a minimum-depth embedding.     

An O(2<Superscript>O(k)</Superscript>n<Superscript>3</Superscript>) FPT Algorithm for the Undirected Feedback Vertex Set Problem

We describe an algorithm for the Feedback Vertex Set problem on undirected graphs, parameterized by the size k of the feedback vertex set, that runs in time O(c^kn³) where c = 10.567 and n is the number of vertices in the graph. The best previous algorithms were based on the method of bounded search trees, branching on short cycles. The best previous running time of an FPT algorithm for this problem, due to Raman, Saurabh and Subramanian, has a parameter function of the form 2^{O(k log k /log log k)}. Whether an exponentially linear in k FPT algorithm for this problem is possible has been previously noted as a significant challenge. Our algorithm is based on the new FPT technique of iterative compression. Our result holds for a more general form of the problem, where a subset of the vertices may be marked as forbidden to belong to the feedback set. We also establish "exponential optimality" for our algorithm by proving that no FPT algorithm with a parameter function of the form O(2^o(k)) is possible, unless there is an unlikely collapse of parameterized complexity classes, namely FPT = M[1].     

H∞Fuzzy State‐Feedback Control Plus State‐Derivative‐Feedback Control Synthesis for Photovoltaic Systems

This paper addresses the problem of designing an H_∞fuzzy state‐ feedback (SF) plus state‐derivative‐feedback (SDF) control system for photovoltaic (PV) systems based on a linear matrix inequality (LMI) approach. The TS fuzzy controller is designed on the basis of the Takagi‐Sugeno (TS) fuzzy model. The sufficient condition is found such that the system with the fuzzy controller is asymptotically stable and an H_∞performance is satisfied. First, a dc/dc buck converter is considered to regulate the power output by controlling state and state‐derivative variables of PV systems. The dynamic model of PV systems is approximated by the TS fuzzy model in the form of nonlinear systems. Then, based on a well‐known Lyapunov functional approach, the synthetic is formulated of an H_∞fuzzy SF plus SDF control law, which guarantees the L₂‐gain from an exogenous input to the regulated output to be less than or equal to some prescribed value. Finally, to show effectiveness, the simulation of the PV systems with the proposed control is assessed by the computer programme. The proposed control method shows good performance for power output and high stability for the PV system.     

Geometric design of motions constrained by a contacting surface pair

We discuss the following problem which arises in robot motion planning, NC machining and computer animation: Given are a fixed surface Ψ and N positions Φ_i of a moving surface Φ such that the Φ_i are in point contact with Ψ. Compute a smooth and fair Euclidean gliding motion Φ(t) of the surface Φ on the surface Ψ which interpolates (or approximates) the given positions Φ_i at time instances t_i. First we generalize interpolatory variational subdivision algorithms for curves to curves on surfaces. Second we study an unconstraint motion design algorithm which we then extend to the main contribution of this paper, an algorithm for the design of a motion constraint by a contacting surface pair. Both motion design algorithms use a feature point representation of the moving surface, subdivision algorithms for curves, instantaneous kinematics, and ideas from line geometry. Geometric methods are used for the numerical solution of the arising optimization problems.     

Combinations of <Emphasis Type="Italic">Stit</Emphasis> and Actions

We present a simple theory of actions against the background of branching time, based on which we propose two versions of an extended stit theory, one equipped with particular actions and the other with sets of such actions. After reporting some basic results of a formal development of such a theory, we briefly explore its connection to a version of branching ETL.     

Extensions of fuzzy hyperideals in <Emphasis Type="Italic">H</Emphasis><Subscript><Emphasis Type="Italic">V</Emphasis></Subscript>-semigroups

The object of the present paper is the investigation and study of (fuzzy) hyperideals in H _v - semigroups. Regular equivalence relations play in H _v - semigroup theory a role analogous to congruences in semigroup theory, so we introduce fuzzy regular equivalence relations on H _v -semigroups and then we study fuzzy Rees regular relations on H _v -semigroups. Using this ideas, we establish a relation between fuzzy hyperideal of an H _v -semigroup H and fuzzy hyperideal of a quotient H _v -semigroup of H. Some characterizations of them are then shown.      

Pre-compensator selection for <Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> loop shaping control

In this paper, an attempt has been made to address on the design of pre-compensator to obtain the solution of H _∞ loop-shaping control problem. Two different design techniques have been proposed where the first one is based on singular value decomposition (SVD) technique along with the matrix perturbation approach; the other one is focused in linear matrix inequality (LMI) framework leading to minimize the condition number of the pre-compensator that, in turn, reduces loop deterioration. A numerical example has been considered to illustrate the effectiveness of the proposed method.     

Aggregate features and A<Emphasis Type="SmallCaps">DA</Emphasis>B<Emphasis Type="SmallCaps">OOST</Emphasis> for music classification

We present an algorithm that predicts musical genre and artist from an audio waveform. Our method uses the ensemble learner ADABOOST to select from a set of audio features that have been extracted from segmented audio and then aggregated. Our classifier proved to be the most effective method for genre classification at the recent MIREX 2005 international contests in music information extraction, and the second-best method for recognizing artists. This paper describes our method in detail, from feature extraction to song classification, and presents an evaluation of our method on three genre databases and two artist-recognition databases. Furthermore, we present evidence collected from a variety of popular features and classifiers that the technique of classifying features aggregated over segments of audio is better than classifying either entire songs or individual short-timescale features. Editor: Gerhard Widmer     

Categorical top-<Emphasis Type="Italic">k</Emphasis> spatial influence query

The influence of a spatial facility object depicts the importance of the object in the whole data space. In this paper, we present a novel definition of object influence in applications where objects are of different categories. We study the problem of Spatial Influence Query which considers the contribution of an object in forming functional units consisting of a given set of objects with different categories designated by users. We first show that the problem of spatial influence query is NP-hard with respect to the number of object categories in the functional unit. To tackle the computational hardness, we develop an efficient framework following two main steps, possible participants finding and optimal functional unit computation. Based on this framework, for the first step, novel and efficient pruning techniques are developed based on the nearest neighbor set (NNS) approach. To find the optimal functional unit efficiently, we propose two algorithms, an exact algorithm and an efficient approximate algorithm with performance guarantee. Comprehensive experiments on both real and synthetic datasets demonstrate the effectiveness and efficiency of our techniques.     

Low-density incomplete <Emphasis Type="Italic">LDL</Emphasis><Superscript><Emphasis Type="Italic">T</Emphasis></Superscript> factorizations

A particular class of incomplete factorizations is proposed as preconditioners for the linear system Ax = b where A is a symmetric, large and sparse matrix. The ILDL ^T< (p) factorization (p = 1,2,3, …) determines the density of the lower triangular matrix L selecting the p largest off-diagonal entries of each column during the Gaussian elimination process. This selection may be computationally expensive, but the effectiveness of the preconditioner allows us to choose very low-density factors to reduce both work time and storage requirements. This incomplete factorization can be performed reliably on H-matrices. When A is a positive definite matrix, but not an H-matrix, one can perform an incomplete factorization if positive off-diagonal entries are removed or reduced and diagonally compensated. Numerical results for a variety of problems and comparisons with other incomplete factorizations are presented. Received: August 2002 / Accepted: December 2002 RID="*" ID="*"This work was supported by the Spanish grant BFM 2001-2641.     

A numerical solution to the matrix ℋ︁2/ℋ︁∞ optimal control problem

In this paper a numerical solution is obtained to the problem of minimizing an ℋ︁₂-type cost subject to an ℋ︁_∞-norm constraint. The method employed is based on the convex alternating projection algorithm and generalizes a recent technique to the multivariable case. The solution is derived in terms of the Markov parameters of an FIR filter of arbitrary length; this is finally approximated by a low-order IIR filter using Hankel-norm model-reduction techniques. The results are illustrated with a numerical example. © 1997 John Wiley & Sons, Ltd.     

SAT‐based and CP‐based declarative approaches for Top‐Rank‐K closed frequent itemset mining

Top‐Rank‐K Frequent Itemset (or Pattern) Mining (FPM) is an important data mining task, where user decides on the number of top frequency ranks of patterns (itemsets) they want to mine from a transactional dataset. This problem does not require the minimum support threshold parameter that is typically used in FPM problems. Rather, the algorithms solving the Top‐Rank‐K FPM problem are fed with $K$, the number of frequency ranks of itemsets required, to compute the threshold internally. This paper presents two declarative approaches to tackle the Top‐Rank‐K Closed FPM problem. The first approach is Boolean Satisfiability‐based (SAT‐based) where we propose an effective encoding for the problem along with an efficient algorithm employing this encoding. The second approach is CP‐based, that is, utilizes Constraint Programming technique, where a simple CP model is exploited in an innovative manner to mine the Top‐Rank‐K Closed FPM itemsets from transactional datasets. Both approaches are evaluated experimentally against other declarative and imperative algorithms. The proposed SAT‐based approach significantly outperforms IM, another SAT‐based approach, and outperforms the proposed CP‐approach for sparse and moderate datasets, whereas the latter excels on dense datasets. An extensive study has been conducted to assess the proposed approaches in terms of their feasibility, performance factors, and practicality of use.     

Development of flexible displays using back‐channel‐etched In–Sn–Zn–O thin‐film transistors and air‐stable inverted organic light‐emitting diodes

We developed flexible displays using back‐channel‐etched In–Sn–Zn–O (ITZO) thin‐film transistors (TFTs) and air‐stable inverted organic light‐emitting diodes (iOLEDs). The TFTs fabricated on a polyimide film exhibited high mobility (32.9 cm²/Vs) and stability by utilization of a solution‐processed organic passivation layer. ITZO was also used as an electron injection layer (EIL) in the iOLEDs instead of conventional air‐sensitive materials. The iOLED with ITZO as an EIL exhibited higher efficiency and a lower driving voltage than that of conventional iOLEDs. Our approach of the simultaneous formation of ITZO film as both of a channel layer in TFTs and of an EIL in iOLEDs offers simple fabrication process.     

Image Compression and Reconstruction Using pi<Subscript><Emphasis Type="Italic">t</Emphasis></Subscript>-Sigma Neural Networks

A high-order feedforward neural architecture, called pi _t -sigma (π _t σ) neural network, is proposed for lossy digital image compression and reconstruction problems. The π _t σ network architecture is composed of an input layer, a single hidden layer, and an output layer. The hidden layer is composed of classical additive neurons, whereas the output layer is composed of translated multiplicative neurons (π _t -neurons). A two-stage learning algorithm is proposed to adjust the parameters of the π _t σ network: first, a genetic algorithm (GA) is used to avoid premature convergence to poor local minima; in the second stage, a conjugate gradient method is used to fine-tune the solution found by GA. Experiments using the Standard Image Database and infrared satellite images show that the proposed π _t σ network performs better than classical multilayer perceptron, improving the reconstruction precision (measured by the mean squared error) in about 56%, on average.     

<Emphasis Type="Italic">A</Emphasis>-equilibrium and fuzzy <Emphasis Type="Italic">A</Emphasis>-core in pure exchange model with externalities

The paper suggests the concept of A-equilibrium that is a concretization of the “altruistic” Berge equilibrium adapted to the pure exchange models with externalities. In contrast to the classical markets, these models consider the external influence on the preferences of economic agents. In terms of an appropriate fuzzy domination, a cooperative characterization of the A-equilibrium allocations is given, and an analog of the classic core equivalence theorem is established.