Towards Understanding Reasoning Complexity in Practice

Although the computational complexity of the logic underlying the standard OWL 2 for the Web Ontology Language (OWL) appears discouraging for real applications, several contributions have shown that reasoning with OWL ontologies is feasible in practice. It turns out that reasoning in practice is often far less complex than is suggested by the established theoretical complexity bound, which reflects the worstcase scenario. State-of-the reasoners like FACT++, HERMIT, PELLET and RACER have demonstrated that, even with fairly expressive fragments of OWL 2, acceptable performances can be achieved. However, it is still not well understood why reasoning is feasible in practice and it is rather unclear how to study this problem. In this paper, we suggest first steps that in our opinion could lead to a better understanding of practical complexity. We also provide and discuss some initial empirical results with HERMIT on prominent ontologies

The correlation between white-matter microstructure and the complexity of spontaneous brain activity: A difussion tensor imaging-MEG study

The advent of new signal processing methods, such as non-linear analysis techniques, represents a new perspective which adds further value to brain signals' analysis. Particularly, Lempel–Ziv's Complexity (LZC) has proven to be useful in exploring the complexity of the brain electromagnetic activity. However, an important problem is the lack of knowledge about the physiological determinants of these measures. Although acorrelation between complexity and connectivity has been proposed, this hypothesis was never tested in vivo. Thus, the correlation between the microstructure of the anatomic connectivity and the functional complexity of the brain needs to be inspected. In this study we analyzed the correlation between LZC and fractional anisotropy (FA), a scalar quantity derived from diffusion tensors that is particularly useful as an estimate of the functional integrity of myelinated axonal fibers, in a group of sixteen healthy adults (all female, mean age 65.56 ± 6.06 years, intervals 58–82). Our results showed a positive correlation between FA and LZC scores in regions including clusters in the splenium of the corpus callosum, cingulum, parahipocampal regions and the sagittal stratum. This study supports the notion of a positive correlation between the functional complexity of the brain and the microstructure of its anatomical connectivity. Our investigation proved that a combination of neuroanatomical and neurophysiological techniques may shed some light on the underlying physiological determinants of brain's oscillations

Complexity analysis of spontaneous brain activity: effects of depression and antidepressant treatment

Magnetoencephalography (MEG) allows the real-time recording of neural activity and oscillatory activity in distributed neural networks. We applied a non-linear complexity analysis to resting-state neural activity as measured using whole-head MEG. Recordings were obtained from 20 unmedicated patients with major depressive disorder and 19 matched healthy controls. Subsequently, after 6 months of pharmacological treatment with the antidepressant mirtazapine 30 mg/day, patients received a second MEG scan. A measure of the complexity of neural signals, the Lempel–Ziv Complexity (LZC), was derived from the MEG time series. We found that depressed patients showed higher pre-treatment complexity values compared with controls, and that complexity values decreased after 6 months of effective pharmacological treatment, although this effect was statistically significant only in younger patients. The main treatment effect was to recover the tendency observed in controls of a positive correlation between age and complexity values. Importantly, the reduction of complexity with treatment correlated with the degree of clinical symptom remission. We suggest that LZC, a formal measure of neural activity complexity, is sensitive to the dynamic physiological changes observed in depression and may potentially offer an objective marker of depression and its remission after treatment.

Lempel-Ziv complexity in schizophrenia: A MEG study

Objective The neurodevelopmental–neurodegenerative debate is a basic issue in the field of the neuropathological basis of schizophrenia (SCH). Neurophysiological techniques have been scarcely involved in such debate, but nonlinear analysis methods may contribute to it. Methods Fifteen patients (age range 23–42 years) matching DSM IV-TR criteria for SCH, and 15 sex- and age-matched control subjects (age range 23–42 years) underwent a resting-state magnetoencephalographic evaluation and Lempel–Ziv complexity (LZC) scores were calculated. Results Regression analyses indicated that LZC values were strongly dependent on age. Complexity scores increased as a function of age in controls, while SCH patients exhibited a progressive reduction of LZC values. A logistic model including LZC scores, age and the interaction of both variables allowed the classification of patients and controls with high sensitivity and specificity. Conclusions Results demonstrated that SCH patients failed to follow the “normal” process of complexity increase as a function of age. In addition, SCH patients exhibited a significant reduction of complexity scores as a function of age, thus paralleling the pattern observed in neurodegenerative diseases. Significance Our results support the notion of a progressive defect in SCH, which does not contradict the existence of a basic neurodevelopmental alteration. Highlights ► Schizophrenic patients show higher complexity values as compared to controls. ► Schizophrenic patients showed a tendency to reduced complexity values as a function of age while controls showed the opposite tendency. ► The tendency observed in schizophrenic patients parallels the tendency observed in Alzheimer disease patients.

Some improvements over the continuation call tabling implementation technique

Tabled evaluation has been proved an effective method to improve several aspeets of goal-oriented query evaluation, including termination and complexity. Several "native" implementations of tabled evaluation have been developed which offer good performance, but many of them need significant changes to the underlying Prolog implementation. More portable approaches, generally using program transformation, have been proposed but they often result in lower efficieney. We explore some techniques aimed at combining the best of these worlds, i.e., developing a portable and extensible implementation, with minimal modifications at the abstract machine level, and with reasonably good performance. Our preliminary results indícate promising results.

Análisis y clasificación de maderas mediante técnicas no invasivas de baja energía

El estudio de materiales, especialmente biológicos, por medios no destructivos está adquiriendo una importancia creciente tanto en las aplicaciones científicas como industriales. Las ventajas económicas de los métodos no destructivos son múltiples. Existen numerosos procedimientos físicos capaces de extraer información detallada de las superficie de la madera con escaso o nulo tratamiento previo y mínima intrusión en el material. Entre los diversos métodos destacan las técnicas ópticas y las acústicas por su gran versatilidad, relativa sencillez y bajo coste. Esta tesis pretende establecer desde la aplicación de principios simples de física, de medición directa y superficial, a través del desarrollo de los algoritmos de decisión mas adecuados basados en la estadística, unas soluciones tecnológicas simples y en esencia, de coste mínimo, para su posible aplicación en la determinación de la especie y los defectos superficiales de la madera de cada muestra tratando, en la medida de lo posible, no alterar su geometría de trabajo. Los análisis desarrollados han sido los tres siguientes: El primer método óptico utiliza las propiedades de la luz dispersada por la superficie de la madera cuando es iluminada por un laser difuso. Esta dispersión produce un moteado luminoso (speckle) cuyas propiedades estadísticas permiten extraer propiedades muy precisas de la estructura tanto microscópica como macroscópica de la madera. El análisis de las propiedades espectrales de la luz laser dispersada genera ciertos patrones mas o menos regulares relacionados con la estructura anatómica, composición, procesado y textura superficial de la madera bajo estudio que ponen de manifiesto características del material o de la calidad de los procesos a los que ha sido sometido. El uso de este tipo de láseres implica también la posibilidad de realizar monitorizaciones de procesos industriales en tiempo real y a distancia sin interferir con otros sensores. La segunda técnica óptica que emplearemos hace uso del estudio estadístico y matemático de las propiedades de las imágenes digitales obtenidas de la superficie de la madera a través de un sistema de scanner de alta resolución. Después de aislar los detalles mas relevantes de las imágenes, diversos algoritmos de clasificacion automatica se encargan de generar bases de datos con las diversas especies de maderas a las que pertenecían las imágenes, junto con los márgenes de error de tales clasificaciones. Una parte fundamental de las herramientas de clasificacion se basa en el estudio preciso de las bandas de color de las diversas maderas. Finalmente, numerosas técnicas acústicas, tales como el análisis de pulsos por impacto acústico, permiten complementar y afinar los resultados obtenidos con los métodos ópticos descritos, identificando estructuras superficiales y profundas en la madera así como patologías o deformaciones, aspectos de especial utilidad en usos de la madera en estructuras. La utilidad de estas técnicas esta mas que demostrada en el campo industrial aun cuando su aplicación carece de la suficiente expansión debido a sus altos costes y falta de normalización de los procesos, lo cual hace que cada análisis no sea comparable con su teórico equivalente de mercado. En la actualidad gran parte de los esfuerzos de investigación tienden a dar por supuesto que la diferenciación entre especies es un mecanismo de reconocimiento propio del ser humano y concentran las tecnologías en la definición de parámetros físicos (módulos de elasticidad, conductividad eléctrica o acústica, etc.), utilizando aparatos muy costosos y en muchos casos complejos en su aplicación de campo. Abstract The study of materials, especially the biological ones, by non-destructive techniques is becoming increasingly important in both scientific and industrial applications. The economic advantages of non-destructive methods are multiple and clear due to the related costs and resources necessaries. There are many physical processes capable of extracting detailed information on the wood surface with little or no previous treatment and minimal intrusion into the material. Among the various methods stand out acoustic and optical techniques for their great versatility, relative simplicity and low cost. This thesis aims to establish from the application of simple principles of physics, surface direct measurement and through the development of the more appropriate decision algorithms based on statistics, a simple technological solutions with the minimum cost for possible application in determining the species and the wood surface defects of each sample. Looking for a reasonable accuracy without altering their work-location or properties is the main objetive. There are three different work lines: Empirical characterization of wood surfaces by means of iterative autocorrelation of laser speckle patterns: A simple and inexpensive method for the qualitative characterization of wood surfaces is presented. it is based on the iterative autocorrelation of laser speckle patterns produced by diffuse laser illumination of the wood surfaces. The method exploits the high spatial frequency content of speckle images. A similar approach with raw conventional photographs taken with ordinary light would be very difficult. A few iterations of the algorithm are necessary, typically three or four, in order to visualize the most important periodic features of the surface. The processed patterns help in the study of surface parameters, to design new scattering models and to classify the wood species. Fractal-based image enhancement techniques inspired by differential interference contrast microscopy: Differential interference contrast microscopy is a very powerful optical technique for microscopic imaging. Inspired by the physics of this type of microscope, we have developed a series of image processing algorithms aimed at the magnification, noise reduction, contrast enhancement and tissue analysis of biological samples. These algorithms use fractal convolution schemes which provide fast and accurate results with a performance comparable to the best present image enhancement algorithms. These techniques can be used as post processing tools for advanced microscopy or as a means to improve the performance of less expensive visualization instruments. Several examples of the use of these algorithms to visualize microscopic images of raw pine wood samples with a simple desktop scanner are provided. Wood species identification using stress-wave analysis in the audible range: Stress-wave analysis is a powerful and flexible technique to study mechanical properties of many materials. We present a simple technique to obtain information about the species of wood samples using stress-wave sounds in the audible range generated by collision with a small pendulum. Stress-wave analysis has been used for flaw detection and quality control for decades, but its use for material identification and classification is less cited in the literature. Accurate wood species identification is a time consuming task for highly trained human experts. For this reason, the development of cost effective techniques for automatic wood classification is a desirable goal. Our proposed approach is fully non-invasive and non-destructive, reducing significantly the cost and complexity of the identification and classification process.

Termination and cost analysis: Complexity and precision issues

The research in this thesis is related to static cost and termination analysis. Cost analysis aims at estimating the amount of resources that a given program consumes during the execution, and termination analysis aims at proving that the execution of a given program will eventually terminate. These analyses are strongly related, indeed cost analysis techniques heavily rely on techniques developed for termination analysis. Precision, scalability, and applicability are essential in static analysis in general. Precision is related to the quality of the inferred results, scalability to the size of programs that can be analyzed, and applicability to the class of programs that can be handled by the analysis (independently from precision and scalability issues). This thesis addresses these aspects in the context of cost and termination analysis, from both practical and theoretical perspectives. For cost analysis, we concentrate on the problem of solving cost relations (a form of recurrence relations) into closed-form upper and lower bounds, which is the heart of most modern cost analyzers, and also where most of the precision and applicability limitations can be found. We develop tools, and their underlying theoretical foundations, for solving cost relations that overcome the limitations of existing approaches, and demonstrate superiority in both precision and applicability. A unique feature of our techniques is the ability to smoothly handle both lower and upper bounds, by reversing the corresponding notions in the underlying theory. For termination analysis, we study the hardness of the problem of deciding termination for a speci�c form of simple loops that arise in the context of cost analysis. This study gives a better understanding of the (theoretical) limits of scalability and applicability for both termination and cost analysis.

Riding out of the storm: How to deal with the complexity of grid and cloud management

Over the last decade, Grid computing paved the way for a new level of large scale distributed systems. This infrastructure made it possible to securely and reliably take advantage of widely separated computational resources that are part of several different organizations. Resources can be incorporated to the Grid, building a theoretical virtual supercomputer. In time, cloud computing emerged as a new type of large scale distributed system, inheriting and expanding the expertise and knowledge that have been obtained so far. Some of the main characteristics of Grids naturally evolved into clouds, others were modified and adapted and others were simply discarded or postponed. Regardless of these technical specifics, both Grids and clouds together can be considered as one of the most important advances in large scale distributed computing of the past ten years; however, this step in distributed computing has came along with a completely new level of complexity. Grid and cloud management mechanisms play a key role, and correct analysis and understanding of the system behavior are needed. Large scale distributed systems must be able to self-manage, incorporating autonomic features capable of controlling and optimizing all resources and services. Traditional distributed computing management mechanisms analyze each resource separately and adjust specific parameters of each one of them. When trying to adapt the same procedures to Grid and cloud computing, the vast complexity of these systems can make this task extremely complicated. But large scale distributed systems complexity could only be a matter of perspective. It could be possible to understand the Grid or cloud behavior as a single entity, instead of a set of resources. This abstraction could provide a different understanding of the system, describing large scale behavior and global events that probably would not be detected analyzing each resource separately. In this work we define a theoretical framework that combines both ideas, multiple resources and single entity, to develop large scale distributed systems management techniques aimed at system performance optimization, increased dependability and Quality of Service (QoS). The resulting synergy could be the key 350 J. Montes et al. to address the most important difficulties of Grid and cloud management.

Collaborative data stream mining in ubiquitous environments using dynamic classifier selection

In ubiquitous data stream mining applications, different devices often aim to learn concepts that are similar to some extent. In these applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real world datasets.

Hybrid choice model for propensity to travel and tour complexity

During the last years cities around the world have invested important quantities of money in measures for reducing congestion and car-trips. Investments which are nothing but potential solutions for the well-known urban sprawl phenomenon, also called the “development trap” that leads to further congestion and a higher proportion of our time spent in slow moving cars. Over the path of this searching for solutions, the complex relationship between urban environment and travel behaviour has been studied in a number of cases. The main question on discussion is, how to encourage multi-stop tours? Thus, the objective of this paper is to verify whether unobserved factors influence tour complexity. For this purpose, we use a data-base from a survey conducted in 2006-2007 in Madrid, a suitable case study for analyzing urban sprawl due to new urban developments and substantial changes in mobility patterns in the last years. A total of 943 individuals were interviewed from 3 selected neighbourhoods (CBD, urban and suburban). We study the effect of unobserved factors on trip frequency. This paper present the estimation of an hybrid model where the latent variable is called propensity to travel and the discrete choice model is composed by 5 alternatives of tour type. The results show that characteristics of the neighbourhoods in Madrid are important to explain trip frequency. The influence of land use variables on trip generation is clear and in particular the presence of commercial retails. Through estimation of elasticities and forecasting we determine to what extent land-use policy measures modify travel demand. Comparing aggregate elasticities with percentage variations, it can be seen that percentage variations could lead to inconsistent results. The result shows that hybrid models better explain travel behavior than traditional discrete choice models.

Towards a fuzzy-based multi-classifier selection module for activity recognition applications

Performing activity recognition using the information provided by the different sensors embedded in a smartphone face limitations due to the capabilities of those devices when the computations are carried out in the terminal. In this work a fuzzy inference module is implemented in order to decide which classifier is the most appropriate to be used at a specific moment regarding the application requirements and the device context characterized by its battery level, available memory and CPU load. The set of classifiers that is considered is composed of Decision Tables and Trees that have been trained using different number of sensors and features. In addition, some classifiers perform activity recognition regardless of the on-body device position and others rely on the previous recognition of that position to use a classifier that is trained with measurements gathered with the mobile placed on that specific position. The modules implemented show that an evaluation of the classifiers allows sorting them so the fuzzy inference module can choose periodically the one that best suits the device context and application requirements.

Linear Approach to the Orbiting Spacecraft Thermal Problem

A linear method is developed for solving the nonlinear differential equations of a lumped-parameter thermal model of a spacecraft moving in a closed orbit. This method, based on perturbation theory, is compared with heuristic linearizations of the same equations. The essential feature of the linear approach is that it provides a decomposition in thermal modes, like the decomposition of mechanical vibrations in normal modes. The stationary periodic solution of the linear equations can be alternately expressed as an explicit integral or as a Fourier series. This method is applied to a minimal thermal model of a satellite with ten isothermal parts (nodes), and the method is compared with direct numerical integration of the nonlinear equations. The computational complexity of this method is briefly studied for general thermal models of orbiting spacecraft, and it is concluded that it is certainly useful for reduced models and conceptual design but it can also be more efficient than the direct integration of the equations for large models. The results of the Fourier series computations for the ten-node satellite model show that the periodic solution at the second perturbative order is sufficiently accurate.

On the complexity of radiation models for PV energy production calculation

Several authors have analysed the changes of the probability density function of the solar radiation with different time resolutions. Some others have approached to study the significance of these changes when produced energy calculations are attempted. We have undertaken different transformations to four Spanish databases in order to clarify the interrelationship between radiation models and produced energy estimations. Our contribution is straightforward: the complexity of a solar radiation model needed for yearly energy calculations, is very low. Twelve values of monthly mean of solar radiation are enough to estimate energy with errors below 3%. Time resolutions better than hourly samples do not improve significantly the result of energy estimations.

Low-complexity timing synchronization scheme for MB-OFDM UWB receiver based on sign-bit

A unified low complexity sign-bit correlation based symbol timing synchronization scheme for Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) Ultra Wideband (UWB) receiver system is proposed. By using the time domain sequence of the packet/frame synchronization preamble, the proposed scheme is in charge of detecting the upcoming MB-OFDM symbol and it estimates the exact boundary of the start of Fast Fourier Transform (FFT) window. The proposed algorithm is implemented by using an efficient Hardware-Software co-simulation methodology. The effectiveness of the proposed synchronization scheme and the optimization criteria is confirmed by hardware implementation results.

Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer's disease using spectral entropies and statistical complexity measures

Alzheimer's disease (AD) is the most common cause of dementia. Over the last few years, a considerable effort has been devoted to exploring new biomarkers. Nevertheless, a better understanding of brain dynamics is still required to optimize therapeutic strategies. In this regard, the characterization of mild cognitive impairment (MCI) is crucial, due to the high conversion rate from MCI to AD. However, only a few studies have focused on the analysis of magnetoencephalographic (MEG) rhythms to characterize AD and MCI. In this study, we assess the ability of several parameters derived from information theory to describe spontaneous MEG activity from 36 AD patients, 18 MCI subjects and 26 controls. Three entropies (Shannon, Tsallis and Rényi entropies), one disequilibrium measure (based on Euclidean distance ED) and three statistical complexities (based on Lopez Ruiz–Mancini–Calbet complexity LMC) were used to estimate the irregularity and statistical complexity of MEG activity. Statistically significant differences between AD patients and controls were obtained with all parameters (p < 0.01). In addition, statistically significant differences between MCI subjects and controls were achieved by ED and LMC (p < 0.05). In order to assess the diagnostic ability of the parameters, a linear discriminant analysis with a leave-one-out cross-validation procedure was applied. The accuracies reached 83.9% and 65.9% to discriminate AD and MCI subjects from controls, respectively. Our findings suggest that MCI subjects exhibit an intermediate pattern of abnormalities between normal aging and AD. Furthermore, the proposed parameters provide a new description of brain dynamics in AD and MCI.