Applying semiotic analysis to the design and modeling of distributed multimedia systems

Semiotics is the study of signs. Application of semiotics in information systems design is based on the notion that information systems are organizations within which agents deploy signs in the form of actions according to a set of norms. An analysis of the relationships among the agents, their actions and the norms would give a better specification of the system. Distributed multimedia systems (DMMS) could be viewed as a system consisted of many dynamic, self-controlled normative agents engaging in complex interaction and processing of multimedia information. This paper reports the work of applying the semiotic approach to the design and modeling of DMMS, with emphasis on using semantic analysis under the semiotic framework. A semantic model of DMMS describing various components and their ontological dependencies is presented, which then serves as a design model and implemented in a semantic database. Benefits of using the semantic database are discussed with reference to various design scenarios.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Sparse modeling using orthogonal forward regression with PRESS statistic and regularization

The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete-1 cross validation concept and the associated leave-one-out test error also known as the predicted residual sums of squares (PRESS) statistic, without resorting to any other validation data set for model evaluation in the model construction process. Computational efficiency is ensured using an orthogonal forward regression, but the algorithm incrementally minimizes the PRESS statistic instead of the usual sum of the squared training errors. A local regularization method can naturally be incorporated into the model selection procedure to further enforce model sparsity. The proposed algorithm is fully automatic, and the user is not required to specify any criterion to terminate the model construction procedure. Comparisons with some of the existing state-of-art modeling methods are given, and several examples are included to demonstrate the ability of the proposed algorithm to effectively construct sparse models that generalize well.

Modeling the effects of node heterogeneity on the performance of grid applications

The performance benefit when using grid systems comes from different strategies, among which partitioning the applications into parallel tasks is the most important. However, in most cases the enhancement coming from partitioning is smoothed by the effects of synchronization overheads, mainly due to the high variability in the execution times of the different tasks, which, in turn, is accentuated by the large heterogeneity of grid nodes. In this paper we design hierarchical, queuing network performance models able to accurately analyze grid architectures and applications. Thanks to the model results, we introduce a new allocation policy based on a combination between task partitioning and task replication. The models are used to study two real applications and to evaluate the performance benefits obtained with allocation policies based on task replication.

Mixed initial-boundary value problem in particle modeling of microelectronic devices

The Boltzmann equation in presence of boundary and initial conditions, which describes the general case of carrier transport in microelectronic devices is analysed in terms of Monte Carlo theory. The classical Ensemble Monte Carlo algorithm which has been devised by merely phenomenological considerations of the initial and boundary carrier contributions is now derived in a formal way. The approach allows to suggest a set of event-biasing algorithms for statistical enhancement as an alternative of the population control technique, which is virtually the only algorithm currently used in particle simulators. The scheme of the self-consistent coupling of Boltzmann and Poisson equation is considered for the case of weighted particles. It is shown that particles survive the successive iteration steps.

Classification-based probabilistic modeling of texture transition for fast line search tracking and delineation

We introduce a classification-based approach to finding occluding texture boundaries. The classifier is composed of a set of weak learners, which operate on image intensity discriminative features that are defined on small patches and are fast to compute. A database that is designed to simulate digitized occluding contours of textured objects in natural images is used to train the weak learners. The trained classifier score is then used to obtain a probabilistic model for the presence of texture transitions, which can readily be used for line search texture boundary detection in the direction normal to an initial boundary estimate. This method is fast and therefore suitable for real-time and interactive applications. It works as a robust estimator, which requires a ribbon-like search region and can handle complex texture structures without requiring a large number of observations. We demonstrate results both in the context of interactive 2D delineation and of fast 3D tracking and compare its performance with other existing methods for line search boundary detection.

Resource availability and the persistence of seed-eating bird populations in agricultural landscapes – a mechanistic modelling approach

1. Reductions in resource availability, associated with land-use change and agricultural intensification in the UK and Europe, have been linked with the widespread decline of many farmland bird species over recent decades. However, the underlying ecological processes which link resource availability and population trends are poorly understood. 2. We construct a spatial depletion model to investigate the relationship between the population persistence of granivorous birds within the agricultural landscape and the temporal dynamics of stubble field availability, an important source of winter food for many of those species. 3. The model is capable of accurately predicting the distribution of a given number of finches and buntings amongst patches of different stubble types in an agricultural landscape over the course of a winter and assessing the relative value of different landscapes in terms of resource availability. 4. Sensitivity analyses showed that the model is relatively robust to estimates of energetic requirements, search efficiency and handling time but that daily seed survival estimates have a strong influence on model fit. Understanding resource dynamics in agricultural landscapes is highlighted as a key area for further research. 5. There was a positive relationship between the predicted number of bird days supported by a landscape over-winter and the breeding population trend for yellowhammer Emberiza citrinella, a species for which survival has been identified as the primary driver of population dynamics, but not for linnet Carduelis cannabina, a species for which productivity has been identified as the primary driver of population dynamics. 6. Synthesis and applications. We believe this model can be used to guide the effective delivery of over-winter food resources under agri-environment schemes and to assess the impacts on granivorous birds of changing resource availability associated with novel changes in land use. This could be very important in the future as farming adapts to an increasingly dynamic trading environment, in which demands for increased agricultural production must be reconciled with objectives for environmental protection, including biodiversity conservation.

Mechanistic insights into a gas–solid reaction in molecular crystals: the role of hydrogen bonding

Reactions in (molecular) organic crystalline solids have been shown to be important for exerting control that is unattainable over chemical transformations in solution. Such control has also been achieved for reactions within metal– organic cages. In these examples, the reactants are already in place within the crystals following the original crystal growth. The post-synthetic modification of metal–organic frameworks (MOFs and indeed reactions and catalysis within MOFs have been recently demonstrated; in these cases the reactants enter the crystals through permanent channels. Another growing area of interest within molecular solid-state chemistry is synthesis by mechanical co-grinding of solid reactants—often referred to as mechanochemistry. Finally, in a small number of reported examples, molecules also have been shown to enter nonporous crystals directly from the gas or vapor phase, but in only a few of these examples does a change in covalent bonding result, which indicates that a reaction occurs within the nonporous crystals. It is this latter type of highly uncommon reaction that is the focus of the present study.

Numerical modeling of the propagation environment in the atmospheric boundary layer over the Persian Gulf

Strong vertical gradients at the top of the atmospheric boundary layer affect the propagation of electromagnetic waves and can produce radar ducts. A three-dimensional, time-dependent, nonhydrostatic numerical model was used to simulate the propagation environment in the atmosphere over the Persian Gulf when aircraft observations of ducting had been made. A division of the observations into high- and low-wind cases was used as a framework for the simulations. Three sets of simulations were conducted with initial conditions of varying degrees of idealization and were compared with the observations taken in the Ship Antisubmarine Warfare Readiness/Effectiveness Measuring (SHAREM-115) program. The best results occurred with the initialization based on a sounding taken over the coast modified by the inclusion of data on low-level atmospheric conditions over the Gulf waters. The development of moist, cool, stable marine internal boundary layers (MIBL) in air flowing from land over the waters of the Gulf was simulated. The MIBLs were capped by temperature inversions and associated lapses of humidity and refractivity. The low-wind MIBL was shallower and the gradients at its top were sharper than in the high-wind case, in agreement with the observations. Because it is also forced by land–sea contrasts, a sea-breeze circulation frequently occurs in association with the MIBL. The size, location, and internal structure of the sea-breeze circulation were realistically simulated. The gradients of temperature and humidity that bound the MIBL cause perturbations in the refractivity distribution that, in turn, lead to trapping layers and ducts. The existence, location, and surface character of the ducts were well captured. Horizontal variations in duct characteristics due to the sea-breeze circulation were also evident. The simulations successfully distinguished between high- and low-wind occasions, a notable feature of the SHAREM-115 observations. The modeled magnitudes of duct depth and strength, although leaving scope for improvement, were most encouraging.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Modeling entangled dynamics: comparison between stochastic single-chain and multichain models

To test the effectiveness of stochastic single-chain models in describing the dynamics of entangled polymers, we systematically compare one such model; the slip-spring model; to a multichain model solved using stochastic molecular dynamics(MD) simulations (the Kremer-Grest model). The comparison involves investigating if the single-chain model can adequately describe both a microscopic dynamical and a macroscopic rheological quantity for a range of chain lengths. Choosing a particular chain length in the slip-spring model, the parameter values that best reproduce the mean-square displacement of a group of monomers is determined by fitting toMDdata. Using the same set of parameters we then test if the predictions of the mean-square displacements for other chain lengths agree with the MD calculations. We followed this by a comparison of the time dependent stress relaxation moduli obtained from the two models for a range of chain lengths. After identifying a limitation of the original slip-spring model in describing the static structure of the polymer chain as seen in MD, we remedy this by introducing a pairwise repulsive potential between the monomers in the chains. Poor agreement of the mean-square monomer displacements at short times can be rectified by the use of generalized Langevin equations for the dynamics and resulted in significantly improved agreement.