Computational Complexity

Currently in most global meteorological applications, low-order finite difference or finite element methods, or the spectral transform method are used. The spectral transform method, which yields high-order approximations, requires Legendre transforms. The Legendre transforms have a computational complexity of O(N 3 ), where N is the number of subintervals in one dimension, and thus render the spectral transform method unscalable. In this study, we present an alternative numerical method for solving the shallow water equations (SWEs) on a sphere in spherical coordinates. In this implementation, the SWEs are discretized in time using the two-level semi-Lagrangian semi-implicit method, and in space on staggered grids using the quadratic spline Galerkin method. We show that, when applied to a simplified version of the SWEs, the method yields a neutrally stable solution for the meteorologically significant Rossby waves. Moreover, we demonstrate that the Helmholtz equation arising from the discretization and solution of the SWEs should be derived algebraically rather than analytically, in order for the method to be stable with respect to the Rossby waves. These results are verified numerically using Boyd's equatorial wave equations [J.P. Boyd, Equatorial solitary waves. Part I. Rossby solitons, J. Phys. Oceanogr. 10 (1980) [1699][1700][1701][1702][1703][1704][1705][1706][1707][1708][1709][1710][1711][1712][1713][1714][1715][1716][1717] with initial conditions chosen to generate a soliton.

A thesis submitted in conformity wit h the requirements for the degree of Master of Science Graduate Department of Computer Science University of Toronto Copyright @ 2000 by Kit Sun Ng The author has gcanted a nonexclusive licence dîowing the National L i i of Canada to copies of diis thesis in microform, paper or electronic formats. The author retains ownership of the copyright in this thesis. Neither the may be printed or othawise reproduced without the author's L'auteur a accord6 me licence non exclusive parnettant A la Bibliothdque nationale du Canada de reproduire, pr&r, distribuer ou vendre des copies de cette Wse sous la forme de microfiche/iiim, de reproduction sur papier ou siu fonnat électronique. L'auteur conserve la propriété du droit d'auteur qui protège cette bitse. Ni la these ni des extraits substantiels de celle-ci ne doivent &ûe imprimes ou autrement reproduits sans son autorisation.

In Ramsey theory, very large numbers and fast-growing functions are more of a rule than an exception. The classical Ramsey numbers R(n,m) are known to be of exponential size: the original proof directly gives the upper bound R(n,m) ≤ ( m+n−2 n−1 ) , and an exponential lower bound is also known. For the van der Waerden numbers, the original proof produced upper bounds that were not even primitive recursive in the case of 2 colors, and even Shelah’s improved proof only gives the bound W (n) = O(g4(n)), where g4(n) = g n 3 (1) and g3(1) = 2, g3(n) = 2 g3(n−1). There are even Ramsey-type functions which provably do not have primitive recursive lower bounds, implying that the Ramsey-type theorems that show their existence cannot be proven in logical systems that can only handle primitive recursion. Peano Arithmetic is a system capable of dealing with primitive recursion and beyond, but even in this system, natural Ramsey theoretic questions can be asked for which a solution exists (in st...

A reconfigurable space-time coding technique is investigated, for a high-speed downlink packet access multiple-antenna network, which combats the effects of antenna correlation. Reconfigurability is achieved at the link level by introducing a linear precoder in a space-time block coded system. The technique assumes knowledge of the long-term characteristics of the channel, namely the channel correlation matrix at the transmitter. The benefits of the proposed reconfigurable technique as compared to the conventional non-reconfigurable versions are evaluated via system-level simulations. In order to characterize the system-level performance accurately and, at the same time, use a feasible approach in terms of computational complexity, a suitable linkto-system interface has been developed. The average system throughput and the number of satisfied users are the performance metrics of interest. Simulation results demonstrate the performance enhancements achieved by the application of reconfigurable techniques as compared to their conventional counterparts.

Mobility tracking based on data from wireless cellular networks is a key challenge that has been recently investigated both from a theoretical and practical point of view. This paper proposes Monte Carlo techniques for mobility tracking in wireless communication networks by means of received signal strength indications. These techniques allow for accurate estimation of Mobile Station's (MS) position and speed. The command process of the MS is represented by a first-order Markov model which can take values from a finite set of acceleration levels. The wide range of acceleration changes is covered by a set of preliminary determined acceleration values. A particle filter and a Rao-Blackwellised particle filter are proposed and their performance is evaluated both over synthetic and real data. A comparison with an Extended Kalman Filter (EKF) is performed with respect to accuracy and computational complexity. With a small number of particles the RBPF gives more accurate results than the PF and the EKF. A posterior Cramér Rao lower bound (PCRLB) is calculated and it is compared with the filters' rootmean-square error performance.

Evolutionary data has been traditionally modeled via phylogenetic trees; however, branching alone cannot model conflicting phylogenetic signals, so networks are used instead. Ancestral recombination graphs (ARGs) are used to model the evolution of incompatible sets of SNP data, allowing each site to mutate only once. The model often aims to minimize the number of recombinations. Similarly, incompatible cluster data can be represented by a reticulation network that minimizes reticulation events. The ARG literature has traditionally been disjoint from the reticulation network literature. By building on results from the reticulation network literature, we resolve an open question of interest to the ARG community. We explicitly prove that the History Bound, a lower bound on the number of recombinations in an ARG for a binary matrix, which was previously only defined procedurally, is equal to the minimum number of reticulation nodes in a network for the corresponding cluster data. To facilitate the proof, we give an algorithm that constructs this network using intermediate values from the procedural History Bound definition. We then develop a top-down algorithm for computing the History Bound, which has the same worst-case runtime as the known dynamic program, and show that it is likely to run faster in typical cases.

An extractor is a function that receives some randomness and either "improves" it or produces "new" randomness. There are statistical and algorithmical specifications of this notion. We study an algorithmical one called Kolmogorov extractors and modify it to resourcebounded version of Kolmogorov complexity. Following Zimand we prove the existence of such objects with certain parameters. The utilized technique is "naive" derandomization: we replace random constructions employed by Zimand by pseudo-random ones obtained by Nisan-Wigderson generator.

Purpose Mesh Parameterization is central to reverse engineering, tool path planning, etc. This work synthesizes parameterizations with un-constrained borders, overall minimum angle plus area distortion. This study aims to present an assessment of the sensitivity of the minimized distortion with respect to weighed area and angle distortions. Design/methodology/approach A Mesh Parameterization which does not constrain borders is implemented by performing: isometry maps for each triangle to the plane Z = 0; an affine transform within the plane Z = 0 to glue the triangles back together; and a Levenberg–Marquardt minimization algorithm of a nonlinear F penalty function that modifies the parameters of the first two transformations to discourage triangle flips, angle or area distortions. F is a convex weighed combination of area distortion (weight: α with 0 ≤ α ≤ 1) and angle distortion (weight: 1 − α). Findings The present study parameterization algorithm has linear complexity [𝒪(n), n = ...

Face Recognition plays a major role in Biometrics. Feature selection is a measure issue in face recognition. This paper proposes a survey on face recognition. There are many methods to extract face features. In some advanced methods it can be extracted faster in a single scan through the raw image and lie in a lower dimensional space, but still retaining facial information efficiently. The methods which are used to extract features are robust to low-resolution images. The method is a trainable system for selecting face features. After the feature selection procedure next procedure is matching for face recognition. The recognition accuracy is increased by advanced methods.

In orthogonal frequency division multiplexing (OFDM), carrier frequency offset (CFO) must be mitigated since it generates interference between received symbols transmitted through different sub-carriers. This paper presents a new algorithm for CFO estimation with reduced computational complexity. The new approach is based on the segmentation of the input-signal autocorrelation matrix into the noise and signal subspaces, the latter being employed to estimate the desired CFO. Simulations validate the effectiveness of the new algorithm in comparison to the traditional parametric estimation technique ESPRIT.

This article presents a low-complexity parametric algorithm for the estimation of the carrier frequency offset (CFO) in orthogonal frequency division multiplexing systems. The proposed algorithm is equivalent to the Unitary ESPRIT algorithm, originally devised for estimating the directionof-arrival of a wavefront, now applied to the CFO scenario. It is verified that the proposed algorithm reduces the overall computational complexity in around 40%, while sustaining the error-rate performance achieved by the standard ESPRIT algorithm.

This work presents a new version, with reduced computational complexity, of the covariance-based direction-of-arrival (CB-DoA) algorithm. The new algorithm incorporates the concept of beamspace projection before performing the DoA estimation. Such modification reduces the dimensions of the matrices employed by the elementspace CB-DoA, simplifying the resulting computations while preserving the detectability of the original algorithm. The Beamspace CB-DoA algorithm is compared to the traditional algorithm Beamspace ESPRIT, as well as to elementspace CB-DoA.

We consider multicarrier transmission schemes in which the elementary signal pulses relate to the elements of a Weyl-Heisenberg system, i.e., resulting from a prototype function shifted in time and frequency. The overlapping of the information-bearing signal parts at the output of doubly dispersive channels and the resulting interference are confined by utilizing a prototype function whose energy is concentrated in both time and frequency. We derive a symbol detector which first calculates a sufficient statistic for the unknown data symbols from the linearly combined output signals of a filter bank, and second, performs an iterative maximization of the likelihood function. The presented receiver takes full advantage of the confined pulse overlapping to limit the computational effort. An analysis of the computational complexity and bit-error rate performance of the iterative detection scheme is provided for wide-sense stationary uncorrelated scattering channels.

One of the most popular clustering techniques is the k-means clustering algorithm. However, the utilization of the k-means is severely limited by its high computational complexity. In this study, we propose a new strategy to accelerate the k-means clustering algorithm through the Partial Distance (PD) logic. The proposed strategy avoids many unnecessary distance calculations by applying efficient PD strategy. Experiments show the efficiency of the proposed strategy when applied to different data sets.

Transcoding is a fast technique for video adaptation, which offers great flexibility for changing the characteristics of already coded video sequences in order to match the properties of the terminal display devices and the bandwidth availability of the transmission networks. For reduction of the bitrate, requantization transcoding is often used. In our previous work, we introduced a hybrid architecture for requantization transcoding, which is able to reduce the bitrate for H.264/AVC bitstreams with quality results approaching the rate-distortion optimal decoderencoder cascade. In this paper, we focus on the filters used for interpolation of the accumulated requantization errors, which are used for compensation of the current (sub-)macroblock partition. We show that by using reduced complexity filters, computational complexity can be significantly reduced without quality loss.

In this paper, efficient solutions for requantization transcoding in H.264/AVC are presented. By requantizing residual coefficients in the bitstream, different error components can appear in the transcoded video stream. Firstly, a requantization error is present due to successive quantization in encoder and transcoder. In addition to the requantization error, the loss of information caused by coarser quantization will propagate due to dependencies in the bitstream. Because of the use of intra prediction and motion-compensated prediction in H.264/AVC, both spatial and temporal drift propagation arise in transcoded H.264/AVC video streams. The spatial drift in intra-predicted blocks results from mismatches in the surrounding prediction pixels as a consequence of requantization. In this paper, both spatial and temporal drift components are analyzed. As is shown, spatial drift has a determining impact on the visual quality of transcoded video streams in H.264/AVC. In particular, this type of drift results in serious distortion and disturbing artifacts in the transcoded video stream. In order to avoid the spatially propagating

In this paper, we discuss motion-refined rewriting of single-layer H.264/AVC streams to SVC streams with multiple quality layers. First, we elaborate on techniques we developed for efficient rewriting of residual data from H.264/AVC to SVC. We investigate if rate-distortion performance can further be improved by extending these architectures with motion refinement techniques, which exploit the inter-layer motion prediction mechanisms available in SVC. For optimum performance, we discuss a fast rate-distortion technique based on Lagrangian relaxation. Although motion refinement in the transformdomain leads to extra distortion in the bitstream, we show that our ratedistortion model successfully takes into account both base and enhancement layer rate and distortion during optimization. Implementation results show that motion-refined rewriting in the transform domain can increase ratedistortion performance, with gains of up to 0.5 dB for the SVC base layer. The presented rewriting architectures significantly reduce the computational complexity when compared to reencoding, with a speed-up by a factor of forty or more, even in the case of motion refinement.

Control systems that help the driver avoid accidents, or limit the damage in case of an accident, have become ubiquitous in modern passenger cars. For example, new cars typically have an anti-lock braking system (ABS), which prevents the wheels from locking during hard braking, and they often have an electronic stability control system (ESC), which stabilizes the lateral motion of the vehicle to prevent skidding. Collision warning and avoidance, rollover prevention, crosswind stabilization, and preparation for an impending accident by adjusting seat positions and seat belts are additional examples of control systems for automotive safety. These systems rely on information about the state of the vehicle and its surroundings. To obtain this information, modern cars are equipped with various sensors. For a typical car with an ESC system, necessary measurements include the steering wheel angle, wheel angular velocities, lateral acceleration, and the rate of rotation around the vertical body-fixed axis, known as the yaw rate. These measurements alone contain a great deal of information about the state of the vehicle. The speed of the car can be estimated using the wheel angular velocities, and a linear reference model taking the speed, steering wheel angle, and additional measurements as inputs can be used to predict the behavior of the car under normal driving conditions. The predicted

machine-independent) complexity theory is basically a theory which establishes limits on what may be said about the complexity of specific recursive functions. (For example, the speed-up theorem shows that it is not always possible to discover close upper and lower bounds for the complexity of a function.) As such, its basic proof method is diagonalization. Sacks, Spector, etc. [2], [3] have developed extensive diagonalization machinery for theorems about degrees of unsolvability. Thus far, we have not applied any except the simplest of the methods devised by the recursion theorists to complexity theory. In this paper, we present two results which arise very naturally out of complexity theory and whose proofs seem to require use of priority constructions.

The main result of this paper is that, given a Turing machine with several readwrite heads per tape, one can effectively construct an equivalent multitape Turing machine with a single read-write head per tape, which runs at precisely the same speed. This result implies that serial storage may be used to handle files requiring several points of immediate two-way read-write access without interruptions for rewinds, etc. It also yields simplified proofs of several results in the literature of computational complexity.

Width parameterizations of SAT, such as tree-width and path-width, enable the study of computationally more tractable and practical SAT instances. We give two simple algorithms. One that runs simultaneously in time-space O * (2 2T W(φ) ), O * (2 T W(φ) ) and another that runs in time-space O * (3 T W(φ) log |φ| ), |φ| O(1) , where T W(φ) is the tree-width of a formula φ with |φ| many clauses and variables. This partially answers the question of Alekhnovitch and Razborov [AR02], who also gave algorithms exponential both in time and space, and asked whether the space can be made smaller. We conjecture that every algorithm for this problem that runs in time 2 T W(φ)o(log |φ|) necessarily blows up the space to exponential in T W(φ). We introduce a novel way to combine the two simple algorithms that allows us to trade constant factors in the exponents between running time and space. Our technique gives rise to a family of algorithms controlled by two parameters. By fixing one parameter we obtain an algorithm that runs in time-space O * (3 1.441(1-ǫ)T W(φ) log |φ| ), O * (2 2ǫT W(φ) ) , for every 0 < ǫ < 1. We systematically study the limitations of this technique, and show that these algorithmic results are the best achievable using this technique. We also study further the computational complexity of width parameterizations of SAT. We prove non-sparsification lower bounds for formulas of path-width ω(log |φ|), and a separation between the complexity of path-width and tree-width parametrized SAT modulo plausible complexity assumptions.

We give new characterizations and lower bounds relating classes in the communication complexity polynomial hierarchy and circuit complexity to limited memory communication models. We introduce the notion of rectangle overlay complexity of a function f : {0, 1}×{0, 1} → {0, 1}. This is a natural combinatorial complexity measure in terms of combinatorial rectangles in the communication matrix of f . Furthermore, we consider memoryless and limited-memory communication models, originally introduced in [11] with slightly different terminology. In these communication models there are two parameters of interest: (i) the message length or space, and (ii) the number of memory states. Specifically, these are one-way protocols which proceed in rounds. In each round, Alice sends one message of bounded length to Bob; receiving a message from Alice, Bob has to decide on the spot whether to output 0 or 1, or to continue the protocol. If he decides to continue, he immediately forgets Alice’s messag...

In this paper, we show that it is possible to accomplish belief revision in any logic which is translatable to classical logic. We give the example of the propositional modal logic K and show that a belief operation in K dened in terms of K 0 s translation to classical logic veries the AGM postulates.

Resumo. Neste trabalho, mostramos que o reticulado Λ D obtido via Construção D a partir de uma cadeia de códigos lineares q-ários é igual ao reticulado qΛ * D ⊥ , onde Λ D ⊥ é o reticulado obtido via Construção D a partir da cadeia de códigos lineares duais associada e Λ * D ⊥ é o reticulado dual de Λ D ⊥ . Além disso, a cadeia de códigos lineares duais associada é fechada sob a adição zero-um se e somente se Γ D ⊥ é um reticulado e Λ D = q a Γ * D ⊥ , onde Γ D ⊥ é obtido via Construção D a partir da cadeia de códigos lineares duais associada. Finalmente, sob certas condições, fornecemos uma base para o reticulado Λ D em função dos parâmetros utilizados em sua construção.

Parametric coding is a technique in which data is processed to extract meaningful information and then representing it compactly using appropriate parameters. Parametric Coding exploits redundancy in information to provide a very compact representation and thus achieves very high compression ratios. However, this is achieved at the cost of higher computation complexity. This disadvantage is now being offset by the availability of high speed processors, thus making it possible to exploit the high compression ratios of the parametric video coding techniques. In this paper a novel idea for efficient parametric representation of video is proposed. We perform Oct-Tree Decomposition on a video stack, followed by parameter extraction using Radial Basis Function Networks (RBFN) to achieve exceptionally high compression ratios, even higher than the state of art H.264 codec. The proposed technique exploits spatialtemporal redundancy and therefore inherently achieves multiframe prediction.

Digital twins are enjoying widespread and growing success in both theoretical and practical applications. A recent development that is gaining increasing traction is the application of digital twins to cities. The aim of this article is to discuss whether there are inherent limitations in this case. At present, the scientific literature on urban digital twins is dominated by "technical" approaches. Critical investigation of digital twins-especially from a philosophical perspective-is still at its beginnings. This article aims to contribute to this line of inquiry. It is mainly theoretical and analytical. On the basis of a specific conceptual framework, it examines digital twins and their applications in urban contexts. It starts by distinguishing among simple, complicated and complex systems, and reaches the conclusion that, while using digital twins is generally appropriate (and often helpful) in the first two of these systems, there are some structural limitations on their use in the case of complex systems. In the latter case, inherent limitations depend on certain distinctive aspects of complex systems, such as their emergent and unpredictable nature, and the role played in this regard by "dispersed knowledge" (that is, is a form of diffused practical knowledge that is crucial for the functioning of large urban systems but that cannot be collected and re-unified because, as a coherent and integrated whole, it does not and cannot exist anywhere).

In this paper we analyze the notion of "stopping time complexity", the amount of information needed to specify when to stop while reading an infinite sequence. This notion was introduced by Vovk and Pavlovic . It turns out that plain stopping time complexity of a binary string x could be equivalently defined as (a) the minimal plain complexity of a Turing machine that stops after reading x on a one-directional input tape; (b) the minimal plain complexity of an algorithm that enumerates a prefix-free set containing x; (c) the conditional complexity C(x | x * ) where x in the condition is understood as a prefix of an infinite binary sequence while the first x is understood as a terminated binary string; (d) as a minimal upper semicomputable function K such that each binary sequence has at most 2 n prefixes z such that K(z) < n; (e) as max C X (x) where C X (z) is plain Kolmogorov complexity of z relative to oracle X and the minimum is taken over all extensions X of x. We also show that some of these equivalent definitions become non-equivalent in the more general setting where the condition y and the object x may differ, and answer some open question from Chernov, Hutter and Schmidhuber .

The signature recognition is a topic of intensive research due to its great importance, among others, in the financial system. However it does not exist yet an enough reliable method for signature recognition and verification, especially in the forgeries detection. This paper presents an off-line signature recognition using features extracted from the off-line signature and an array of five growing cell neural network. The proposed system was evaluated using 950 signatures of 19 different persons. Experimental results show that proposed system achieves a fairly good recognition rate with a relatively low computational complexity

The signature recognition is a topic of intensive research due to its great importance, among others, in the financial system. However it does not exist yet an enough reliable method for signature recognition and verification, especially in the forgeries detection. This paper presents an off-line signature recognition using features extracted from the off-line signature and an array of five growing cell neural network. The proposed system was evaluated using 950 signatures of 19 different persons. Experimental results show that proposed system achieves a fairly good recognition rate with a relatively low computational complexity

Antunes, Fortnow, van Melkebeek and Vinodchandran captured the notion of non-random information by computational depth, the difference between the polynomialtime-bounded Kolmogorov complexity and traditional Kolmogorov complexity. We show unconditionally how to probabilistically find satisfying assignments for formulas that have at least one assignment of logarithmic depth. The converse holds under a standard hardness assumption though fails if BPP = FewP = EXP. We also show that assuming good pseudorandom generators one cannot increase the depth of a string efficiently.

Esta obra colectiva tiene como objetivo explorar las posibilidades epistemológicas y metodológicas de la sociología computacional a través de estudios de caso, reflexiones teóricas y propuestas aplicadas. El enfoque central del libro se basa en el uso de modelos computacionales —principalmente modelos basados en agentes, redes complejas, simulaciones y lenguajes de modelado como SysML— para comprender fenómenos sociales emergentes desde una perspectiva interdisciplinaria.

El volumen se inicia con una profunda reflexión de Jorge Zazueta Gutiérrez sobre la naturaleza del conocimiento científico y el papel del modelado formal en las ciencias sociales. Se propone un marco conceptual que enfatiza la falsabilidad, la replicabilidad y la acumulación como pilares del conocimiento científico. Además, se introduce una ontología modificada basada en la propuesta tripartita de Karl Popper (mundo físico, mental y de los productos del intelecto humano), destacando la importancia de la modelación como método unificador que permite la clarificación teórica y el avance acumulativo.

Posteriormente, se presentan metodologías específicas para modelar sistemas sociales. Gabriel Awad introduce el uso del lenguaje de modelado SysML para representar estructuras, comportamientos y propósitos en sistemas sociales humanos. Esta sección destaca el potencial de SysML para integrar disciplinas diversas y facilitar la comunicación entre científicos sociales y expertos técnicos.

Leonardo Rodríguez Zoya aplica el marco computacional SocLab al análisis de políticas públicas, ejemplificando cómo la simulación social puede evaluar la viabilidad social de intervenciones gubernamentales. Este enfoque resalta la dimensión organizacional y estratégica de los actores en contextos institucionales complejos.

El libro también aborda propuestas pedagógicas, como la de Mayra Bárcenas y colaboradores, quienes exploran el uso de isomorfismos metodológicos para la enseñanza-aprendizaje en contextos de alta complejidad, integrando nociones de sistemas adaptativos y redes de interacción.

Otros capítulos aplican técnicas computacionales a fenómenos sociales específicos. Damian Valdés y María del Carmen Pría utilizan el análisis de secuencias sociales (SSA) para estudiar trayectorias vitales y relaciones sociales dinámicas. Luis Castro y Milton Aragón desarrollan un modelo logístico ecosistémico socio-simbólico (LSEM) que representa ecosistemas discursivos y su evolución temporal. Ricardo González y su equipo modelan la cooperación social mediante mecanismos de reciprocidad indirecta individualista, resaltando los factores que favorecen o inhiben comportamientos altruistas.

Finalmente, se presentan modelos de redes complejas y simulaciones de protesta social. Anthony Pérez analiza el desarrollo endógeno en comunidades mexicanas a través de redes causales y booleanas. Andrea España, Edgardo Ugalde y Antonio Aguilera modelan levantamientos sociales utilizando técnicas de simulación basada en agentes. Norma Abrica y Antonio Aguilera complementan este enfoque con el modelo ROPP, que introduce la influencia de la opinión pública y la política en la dinámica de rebelión.

En conjunto, la obra no solo ofrece herramientas técnicas, sino que también promueve una visión crítica y reflexiva sobre cómo las ciencias sociales pueden beneficiarse de enfoques formales y computacionales, sin renunciar a su compromiso con la interpretación y la complejidad de lo social.

---

¿Desea que le prepare una presentación, material de clase o una reseña crítica a partir de este resumen?

This paper presents an informative gene set selection approach to tumor diagnosis based on the Distance Sensitive Rival Penalized Competitive Learning (DSRPCL) algorithm and redundancy analysis. Since the DSRPCL algorithm can allocate an appropriate number of clusters for an input dataset automatically, we can utilize it to classify the genes (expressed by the gene expression levels of all the samples) into certain basic clusters. Then, we apply the post-filtering algorithm to each basic gene cluster to get the typical and independent informative genes. In this way we can obtain a compact set of informative genes. To test the effectiveness of the selected informative gene set, we utilize the support vector machine (SVM) to construct a tumor diagnosis system based on the express profiles of its genes. It is shown by the experiments that the proposed method can achieve a higher diagnosis accuracy with a smaller number of informative genes and less computational complexity in comparison with the previous ones.

Physical-based control using center of mass, center of pressure, and foot placement is used to enable a simulated twelve -degree of freedom, seven-link, three-dimensional bipedal robot to lean sideways, pick up its foot and start walking on a flat surface. Energy analysis is used to compel the same simulated robot to do a side-to-side rocking motion and eventually come to a stop. If the robot is pushed hard enough, it will raise its leg that is in the air in the frontal plane to prevent itself from falling. Center of mass and center of pressure analysis is used to enable the same robot to balance on one foot and stand.

This paper presents an approximate Maximum Common Subgraph (MCS) algorithm, specifically for directed, cyclic graphs representing digital circuits. Because of the application domain, the graphs have nice properties: they are very sparse; have many different labels; and most vertices have only one predecessor. The algorithm iterates over all vertices once and uses heuristics to find the MCS. It is linear in computational complexity with respect to the size of the graph. Experiments show that very large common subgraphs were found in graphs of up to 200,000 vertices within a few minutes, when a quarter or less of the graphs differ. The variation in run-time and quality of the result is low.

For many systems of realistic size the computational complexity of complete model-based diagnosis is prohibitive. In order to overcome this computational hurdle, approximation-based approaches do not solve the complete problem, but compute only the most likely candidates. In this paper we take an alternative approach and investigate the speedup of the diagnosis process by exploiting the hierarchy as is present in well-engineered systems. The approach comprises a compile-time and a runtime step. In the first step, a hierarchical CNF representation of the system is compiled to hierarchical DNF of adjustable depth. In the second step, the diagnoses are computed from the (partially) hierarchical DNF and the actual observations. As in the second step we may apply a complete or an approximation algorithm, our approach can be viewed as complementary towards earlier work on speeding up the diagnosis process. Our hierarchical approach allows large models to be diagnosed, where compile-time investment directly translates to runtime speedup. The benefits of our approach are illustrated by using a 2-bit and 16-bit Full Adder, and the LOFAR Array Telescope which is currently being constructed in The Netherlands. Even for these modestly hierarchical models the speedup compared to traditional approaches ranges in the hundreds.

We investigate the concepts of analyticity, a priori and necessity, propose a new theory based on computability and logicism and critically examine some standard arguments against the analytic vs. synthetic distinction and against non-trivial a priori knowledge.

In this paper we investigate the training of time-lagged recurrent networks having multiple feedback paths and tapped-delay inputs. Network structures of this type are useful in approximating nonlinear dynamical systems. The introduction of additional feedback loops into a network structure may improve the modeling capability of the network, but a significant price can be paid in complexity and computational burden when calculating the dynamic derivatives needed for training. The focus of this paper is on the calculation of the dynamic derivatives which must be determined or approximated in order to use any of the popular methods employed in training neural networks. In this paper we illustrate the effect of multiple feedback loops on the formulation of the equations needed for calculating the dynamic derivatives. We also investigate the effect on network performance and computational complexity when various dynamic derivative approximations are used in training multiple feedback loop networks.

Chordal graphs, which are intersection graph of subtrees of a tree, can be represented on trees. Some representation of a chordal graph often reduces the size of the data structure needed to store the graph, permitting the use of extremely efficient algorithms that take advantage of the compactness of the representation. An extended star graph is the intersection graph of a family of subtrees of a tree that has exactly one vertex of degree at least three. An asteroidal triple in a graph is a set of three non-adjacent vertices such that for any two of them there exists a path between them that does not intersect the neighborhood of the third. Several subclasses of chordal graphs (interval graphs, directed path graphs) have been characterized by forbidden asteroids. In this paper, we define, a subclass of chordal graphs, called extended star graphs,  prove a characterization of this class by forbidden asteroids and show open problems.

For building a linear prediction model, Backward Elimination (BE) is a computationally suitable stepwise procedure for sequencing the candidate predictors. This method yields poor results when data contain outliers and other contaminations. Robust model selection procedures, on the other hand, are not computationally efficient or scalable to large dimensions, because they require the fitting of a large number of submodels. Robust version of BE is proposed in this study, which is computationally very suitable and scalable to large high-dimensional data sets. Since BE can be expressed in terms of sample correlations, simple robustifications are obtained by replacing these correlations by their robust counterparts. A pairwise approach is used to construct the robust correlation matrix -- not only because of its computational advantages over the d-dimensional approach, but also because the pairwise approach is more consistent with the idea of step-by-step algorithms. The performance of ...

Statistical data used in the analysis of environmental problems are very often presented in an imprecise way. We make a distinction between random uncertainty that can be described by stochastic models and linguistic lack of precision that may be modelled by fuzzy sets or possibilistic distributions. In the paper we present a short introduction to fuzzy statistics which may be useful for the analysis of such kind of data. This methodology is illustrated with the example of the communal water supply prediction problem when the statistical data include both the results of measurements and imprecise expert opinions.

Competent Genetic Algorithms can efficiently address problems in which the linkage between variables is limited to a small order k. Problems with higher order dependencies can only be addressed efficiently if further problem properties exist that can be exploited. An important class of problems for which this occurs is that of hierarchical problems. Hierarchical problems can contain dependencies between all variables (k = n) while being solvable in polynomial time. An open question so far is what precise properties a hierarchical problem must possess in order to be solvable efficiently. We study this question by investigating several features of hierarchical problems and determining their effect on computational complexity, both analytically and empirically. The analyses are based on the Hierarchical Genetic Algorithm (HGA), which is developed as part of this work. The HGA is tested on ranges of hierarchical problems, produced by a generator for hierarchical problems.

In this paper, we develop a discrete wavelet Petrov–Galerkin method for integral equations of the second kind with weakly singular kernels suitable for solving boundary integral equations. A compression strategy for the design of a fast algorithm is suggested. Estimates for the rate of convergence and computational complexity of the method are provided.

We consider the problem of finding optimal, fair and distributed power-rate strategies to achieve the sum capacity of the Gaussian multiple-access block-fading channel. In here, the transmitters have access to only their own fading coefficients, while the receiver has global access to all the fading coefficients. Outage is not permitted in any communication block. The resulting average sum-throughput is also known as ‘power-controlled adaptive sum-capacity’, which appears as an open problem in literature. This paper presents the power-controlled adaptive sum-capacity of a wide-class of popular MAC models. In particular, we propose a power-rate strategy in the presence of distributed channel state information (CSI), which is throughput optimal when all the users have identical channel statistics. The proposed scheme also has an efficient implementation using successive cancellation and rate-splitting. We propose an upperbound when the channel laws are not identical. Furthermore, the ...

In this paper, we present an efficient complex event processing system tailored toward monitoring a large-scale setup of manufacturing equipment. In particular, the key challenge in the equipment monitoring is to develop an eventbased system for computing complex manufacturing queries coupled with event notifications and event and query result visualization components. Furthermore, we present an experimental evaluation to validate the effectiveness of the proposed solution with respect to both query latency and throughput.

In this paper, we derive an analytical model for the evaluation of the performance of a Video on Demand (VoD) system. The model estimates the mean waiting time achievable by the Popularity-Aware Partial cAching (PAPA) algorithm from our previous work. Two approximation strategies are proposed for low computational complexity. Furthermore, we also consider the influence of a starting point shift on the quality of experience and combine the two factors into a universal user satisfaction metric. In order to find the relation between the two impairments, waiting time and starting point shift, sophisticated subjective tests are performed. With the final score model, a more comprehensive evaluation of the system can be obtained with very low computational complexity.