Hunting for warped extra dimensions via loop-induced processes

This thesis is on loop-induced processes in theories with warped extra dimensions where the fermions and gauge bosons are allowed to propagate in the bulk, while the Higgs sector is localized on or near the infra-red brane. These so-called Randall-Sundrum (RS) models have the potential to simultaneously explain the hierarchy problem and address the question of what causes the large hierarchies in the fermion sector of the Standard Model (SM). The Kaluza-Klein (KK) excitations of the bulk fields can significantly affect the loop-level processes considered in this thesis and, hence, could indirectly indicate the existence of warped extra dimensions. The analytical part of this thesis deals with the detailed calculation of three loop-induced processes in the RS models in question: the Higgs production process via gluon fusion, the Higgs decay into two photons, and the flavor-changing neutral current b → sγ. A comprehensive, five-dimensional (5D) analysis will show that the amplitudes of the Higgs processes can be expressed in terms of integrals over 5D propagators with the Higgs-boson profile along the extra dimension, which can be used for arbitrary models with a compact extra dimension. To this end, both the boson and fermion propagators in a warped 5D background are derived. It will be shown that the seemingly contradictory results for the gluon fusion amplitude in the literature can be traced back to two distinguishable, not smoothly-connected incarnations of the RS model. The investigation of the b → sγ transition is performed in the KK decomposed theory. It will be argued that summing up the entire KK tower leads to a finite result, which can be well approximated by a closed, analytical expression.rnIn the phenomenological part of this thesis, the analytic results of all relevant Higgs couplings in the RS models in question are compared with current and in particular future sensitivities of the Large Hadron Collider (LHC) and the planned International Linear Collider. The latest LHC Higgs data is then used to exclude significant portions of the parameter space of each RS scenario. The analysis will demonstrate that especially the loop-induced Higgs couplings are sensitive to KK particles of the custodial RS model with masses in the multi tera-electronvolt range. Finally, the effect of the RS model on three flavor observables associated with the b → sγ transition are examined. In particular, we study the branching ratio of the inclusive decay B → X_s γ

Mass Transport Perspective on an Accelerated Exclusion Process: Analysis of Augmented Current and Unit-Velocity Phases

In an accelerated exclusion process (AEP), each particle can "hop" to its adjacent site if empty as well as "kick" the frontmost particle when joining a cluster of size ℓ⩽ℓ_{max}. With various choices of the interaction range, ℓ_{max}, we find that the steady state of AEP can be found in a homogeneous phase with augmented currents (AC) or a segregated phase with holes moving at unit velocity (UV). Here we present a detailed study on the emergence of the novel phases, from two perspectives: the AEP and a mass transport process (MTP). In the latter picture, the system in the UV phase is composed of a condensate in coexistence with a fluid, while the transition from AC to UV can be regarded as condensation. Using Monte Carlo simulations, exact results for special cases, and analytic methods in a mean field approach (within the MTP), we focus on steady state currents and cluster sizes. Excellent agreement between data and theory is found, providing an insightful picture for understanding this model system.

Moving beyond a descriptive aquatic toxicology: the value of biological process and trait information

In order to improve the ability to link chemical exposure to toxicological and ecological effects, aquatic toxicology will have to move from observing what chemical concentrations induce adverse effects to more explanatory approaches, that are concepts which build on knowledge of biological processes and pathways leading from exposure to adverse effects, as well as on knowledge on stressor vulnerability as given by the genetic, physiological and ecological (e.g., life history) traits of biota. Developing aquatic toxicology in this direction faces a number of challenges, including (i) taking into account species differences in toxicant responses on the basis of the evolutionarily developed diversity of phenotypic vulnerability to environmental stressors, (ii) utilizing diversified biological response profiles to serve as biological read across for prioritizing chemicals, categorizing them according to modes of action, and for guiding targeted toxicity evaluation; (iii) prediction of ecological consequences of toxic exposure from knowledge of how biological processes and phenotypic traits lead to effect propagation across the levels of biological hierarchy; and (iv) the search for concepts to assess the cumulative impact of multiple stressors. An underlying theme in these challenges is that, in addition to the question of what the chemical does to the biological receptor, we should give increasing emphasis to the question how the biological receptor handles the chemicals, i.e., through which pathways the initial chemical-biological interaction extends to the adverse effects, how this extension is modulated by adaptive or compensatory processes as well as by phenotypic traits of the biological receptor.

A Concept of Semantics Extraction from Web Data by Induction of Fuzzy Ontologies

Traditionally, ontologies describe knowledge representation in a denotational, formalized, and deductive way. In addition, in this paper, we propose a semiotic, inductive, and approximate approach to ontology creation. We define a conceptual framework, a semantics extraction algorithm, and a first proof of concept applying the algorithm to a small set of Wikipedia documents. Intended as an extension to the prevailing top-down ontologies, we introduce an inductive fuzzy grassroots ontology, which organizes itself organically from existing natural language Web content. Using inductive and approximate reasoning to reflect the natural way in which knowledge is processed, the ontology’s bottom-up build process creates emergent semantics learned from the Web. By this means, the ontology acts as a hub for computing with words described in natural language. For Web users, the structural semantics are visualized as inductive fuzzy cognitive maps, allowing an initial form of intelligence amplification. Eventually, we present an implementation of our inductive fuzzy grassroots ontology Thus,this paper contributes an algorithm for the extraction of fuzzy grassroots ontologies from Web data by inductive fuzzy classification.

Endogenous Development as a Social Learning Process

The author perceives endogenous development as a social learning process, which is constructed by all actors involved. To enhance social learning, a methodology called Autodidactic Learning for sustainability is used, in which the perception of both local actors and external actors are highlighted. Reflecting on differences, conflicts and common interests leads to highly motivated debate and shared reflection, which is almost identical with social learning, and flattens the usual hierarchy between local and external actors. The article shows that the energies generated through collective learning can trigger important technical, social and political changes, which take into account the multiple dimensions of local reality.

Exploring professional orientations to community voice in professional practice settings: The IMAP process

The tension between technical experts and the populations they seek to serve is well established in the literature examining professional social problem solving. In this piece, I examine this tension as one between the distinct discursive worlds of technical expertise and community voice. I develop an analytic process, IMAP, for exploring this tension by looking at a wide variety of professional orientations around a relatively fixed concept of community voice. IMAP involves I&barbelow;dentifying social problem solvers, M&barbelow;apping social problem solvers' claims, A&barbelow;nalyzing professional orientations that arise from this mapping, and P&barbelow;redicting, diagnosing, and remediating conflicts. IMAP can be used by analysts external to social problem solving settings or by social problem solvers themselves. The use of IMAP by external experts poses questions of expert alignment with either of the discursive worlds. I examine two cases in public health practice settings: a mobile immunization service and the efforts of a foundation to improve health in an inner-city neighborhood. I develop four modal types that can be anticipated in social problem solving settings or, more specifically, in public health practice. Understanding of these “world views” can enhance mutual understanding between public health professionals and between public health professionals and the communities they seek to serve. IMAP might also address ongoing conflicts to clarify differences in unspoken normative commitments and the impact of these on social problem solving. I discuss implications of the research for public health practice and further research in the area. ^

Classification of Satellite Images by means of Fuzzy Rules generated by a Genetic Algorithm

The data acquired by Remote Sensing systems allow obtaining thematic maps of the earth's surface, by means of the registered image classification. This implies the identification and categorization of all pixels into land cover classes. Traditionally, methods based on statistical parameters have been widely used, although they show some disadvantages. Nevertheless, some authors indicate that those methods based on artificial intelligence, may be a good alternative. Thus, fuzzy classifiers, which are based on Fuzzy Logic, include additional information in the classification process through based-rule systems. In this work, we propose the use of a genetic algorithm (GA) to select the optimal and minimum set of fuzzy rules to classify remotely sensed images. Input information of GA has been obtained through the training space determined by two uncorrelated spectral bands (2D scatter diagrams), which has been irregularly divided by five linguistic terms defined in each band. The proposed methodology has been applied to Landsat-TM images and it has showed that this set of rules provides a higher accuracy level in the classification process

Application of the Extended Kalman filter to fuzzy modeling: Algorithms and practical implementation

Modeling phase is fundamental both in the analysis process of a dynamic system and the design of a control system. If this phase is in-line is even more critical and the only information of the system comes from input/output data. Some adaptation algorithms for fuzzy system based on extended Kalman filter are presented in this paper, which allows obtaining accurate models without renounce the computational efficiency that characterizes the Kalman filter, and allows its implementation in-line with the process

Characterization of vineyard's canopy through fuzzy clustering and svm over color images

In this work we propose an image acquisition and processing methodology (framework) developed for performance in-field grapes and leaves detection and quantification, based on a six step methodology: 1) image segmentation through Fuzzy C-Means with Gustafson Kessel (FCM-GK) clustering; 2) obtaining of FCM-GK outputs (centroids) for acting as seeding for K-Means clustering; 3) Identification of the clusters generated by K-Means using a Support Vector Machine (SVM) classifier. 4) Performance of morphological operations over the grapes and leaves clusters in order to fill holes and to eliminate small pixels clusters; 5)Creation of a mosaic image by Scale-Invariant Feature Transform (SIFT) in order to avoid overlapping between images; 6) Calculation of the areas of leaves and grapes and finding of the centroids in the grape bunches. Image data are collected using a colour camera fixed to a mobile platform. This platform was developed to give a stabilized surface to guarantee that the images were acquired parallel to de vineyard rows. In this way, the platform avoids the distortion of the images that lead to poor estimation of the areas. Our preliminary results are promissory, although they still have shown that it is necessary to implement a camera stabilization system to avoid undesired camera movements, and also a parallel processing procedure in order to speed up the mosaicking process.

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and genes involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studies

See-and-avoid quadcopter using fuzzy control optimized by cross-entropy

In this work we present an optimized fuzzy visual servoing system for obstacle avoidance using an unmanned aerial vehicle. The cross-entropy theory is used to optimise the gains of our controllers. The optimization process was made using the ROS-Gazebo 3D simulation with purposeful extensions developed for our experiments. Visual servoing is achieved through an image processing front-end that uses the Camshift algorithm to detect and track objects in the scene. Experimental flight trials using a small quadrotor were performed to validate the parameters estimated from simulation. The integration of crossentropy methods is a straightforward way to estimate optimal gains achieving excellent results when tested in real flights.

Perfect imaging of three object points with only two analytic lens surfaces in two dimensions

In this work, a new two-dimensional analytic optics design method is presented that enables the coupling of three ray sets with two lens profiles. This method is particularly promising for optical systems designed for wide field of view and with clearly separated optical surfaces. However, this coupling can only be achieved if different ray sets will use different portions of the second lens profile. Based on a very basic example of a single thick lens, the Simultaneous Multiple Surfaces design method in two dimensions (SMS2D) will help to provide a better understanding of the practical implications on the design process by an increased lens thickness and a wider field of view. Fermat?s principle is used to deduce a set of functional differential equations fully describing the entire optical system. The transformation of these functional differential equations into an algebraic linear system of equations allows the successive calculation of the Taylor series coefficients up to an arbitrary order. The evaluation of the solution space reveals the wide range of possible lens configurations covered by this analytic design method. Ray tracing analysis for calculated 20th order Taylor polynomials demonstrate excellent performance and the versatility of this new analytical optics design concept.

Adaptive neuro-fuzzy inference systems (ANFIS) for supervised classification of competence in MIS based on psychomotor skills

INTRODUCTION: Objective assessment of motor skills has become an important challenge in minimally invasive surgery (MIS) training.Currently, there is no gold standard defining and determining the residents' surgical competence.To aid in the decision process, we analyze the validity of a supervised classifier to determine the degree of MIS competence based on assessment of psychomotor skills METHODOLOGY: The ANFIS is trained to classify performance in a box trainer peg transfer task performed by two groups (expert/non expert). There were 42 participants included in the study: the non-expert group consisted of 16 medical students and 8 residents (< 10 MIS procedures performed), whereas the expert group consisted of 14 residents (> 10 MIS procedures performed) and 4 experienced surgeons. Instrument movements were captured by means of the Endoscopic Video Analysis (EVA) tracking system. Nine motion analysis parameters (MAPs) were analyzed, including time, path length, depth, average speed, average acceleration, economy of area, economy of volume, idle time and motion smoothness. Data reduction was performed by means of principal component analysis, and then used to train the ANFIS net. Performance was measured by leave one out cross validation. RESULTS: The ANFIS presented an accuracy of 80.95%, where 13 experts and 21 non-experts were correctly classified. Total root mean square error was 0.88, while the area under the classifiers' ROC curve (AUC) was measured at 0.81. DISCUSSION: We have shown the usefulness of ANFIS for classification of MIS competence in a simple box trainer exercise. The main advantage of using ANFIS resides in its continuous output, which allows fine discrimination of surgical competence. There are, however, challenges that must be taken into account when considering use of ANFIS (e.g. training time, architecture modeling). Despite this, we have shown discriminative power of ANFIS for a low-difficulty box trainer task, regardless of the individual significances between MAPs. Future studies are required to confirm the findings, inclusion of new tasks, conditions and sample population.

Control of a solar dryer through using a fuzzy logic and low-cost model-based sensor

Solar drying is one of the important processes used for extending the shelf life of agricultural products. Regarding consumer requirements, solar drying should be more suitable in terms of curtailing total drying time and preserving product quality. Therefore, the objective of this study was to develop a fuzzy logic-based control system, which performs a ?human-operator-like? control approach through using the previously developed low-cost model-based sensors. Fuzzy logic toolbox of MatLab and Borland C++ Builder tool were utilized to develop a required control system. An experimental solar dryer, constructed by CONA SOLAR (Austria) was used during the development of the control system. Sensirion sensors were used to characterize the drying air at different positions in the dryer, and also the smart sensor SMART-1 was applied to be able to include the rate of wood water extraction into the control system (the difference of absolute humidity of the air between the outlet and the inlet of solar dryer is considered by SMART-1 to be the extracted water). A comprehensive test over a 3 week period for different fuzzy control models has been performed, and data, obtained from these experiments, were analyzed. Findings from this study would suggest that the developed fuzzy logic-based control system is able to tackle difficulties, related to the control of solar dryer process.

Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes

There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.