Instances Of Model Research Articles

My body in the brain: A neurocognitive model of body-ownership

Empirical research on the bodily self has only recently started to investigate how the link between a body and the experience of this body as mine is developed, maintained or disturbed. The Rubber Hand Illusion has been used as a model instance of the normal sense of embodiment to investigate the processes that underpin the experience of body-ownership. This review puts forward a neurocognitive model according to which body-ownership arises as an interaction between current multisensory input and internal models of the body. First, a pre-existing stored model of the body distinguishes between objects that may or may not be part of one's body. Second, on-line anatomical and postural representations of the body modulate the integration of multisensory information that leads to the recalibration of visual and tactile coordinate systems. Third, the resulting referral of tactile sensation will give rise to the subjective experience of body-ownership. These processes involve a neural network comprised of the right temporoparietal junction which tests the incorporeability of the external object, the secondary somatosensory cortex which maintains an on-line representation of the body, the posterior parietal and ventral premotor cortices which code for the recalibration of the hand-centred coordinate systems, and the right posterior insula which underpins the subjective experience of body-ownership. The experience of body-ownership may represent a critical component of self-specificity as evidenced by the different ways in which multisensory integration in interaction with internal models of the body can actually manipulate important physical and psychological aspects of the self.

Research on Multi-Agent Dynamic Influence Diagrams and Its Approximate Inference Algorithm

As MAIDs can not model dynamic environment and Multi-Agent MDPs are difficult to represent structural relations among Agents,Multi-Agent dynamic influences diagrams(MADIDs)are presented for modeling structural relations of Multi-Agent system in dynamic environment.For efficiently inferring in MADIDs,an extensional BK(EBK)algorithm is proposed,and the extensions are in three aspects:The marginalizing operation of utility nodes is added in BK algorithm;the separators are added for decreasing the error of inference;the junction tree that obtained by hierarchical decomposition of MADIDs is used for improving the efficiency of inference.Given model instances,the experiment results show the validity of MADIDs and EBK algorithm.

Frequency Spectrum Neutrality Tests: One for All and All for One

Neutrality tests based on the frequency spectrum (e.g., Tajima's D or Fu and Li's F) are commonly used by population geneticists as routine tests to assess the goodness-of-fit of the standard neutral model on their data sets. Here, I show that these neutrality tests are specific instances of a general model that encompasses them all. I illustrate how this general framework can be taken advantage of to devise new more powerful tests that better detect deviations from the standard model. Finally, I exemplify the usefulness of the framework on SNP data by showing how it supports the selection hypothesis in the lactase human gene by overcoming the ascertainment bias. The framework presented here paves the way for constructing novel tests optimized for specific violations of the standard model that ultimately will help to unravel scenarios of evolution.

Geoweb Services for Sharing Modelling Results in Biodiversity Networks

AbstractBiodiversity researchers in different institutions deal with predictive models for species distribution. These models are useful for biodiversity conservation policies. Species distribution modelling tools need large datasets from different sources and use many algorithms. To improve biodiversity science, scientists need to share models, data and results, and should be able to reproduce experiments from others. This article presents a geoweb service architecture that supports sharing of modelling results and enables researchers to perform new modelling experiments. We show the feasibility of the proposed architecture by developing a set of prototype services, called Web Biodiversity Collaborative Modelling Services – WBCMS. They provide a set of geospatial web services that support the sharing of species distribution models. The article includes an example of a model instance that explains the WBCMS prototype. We believe that WBCMS shows how to set up a cooperative research network on biodiversity research.

Lattice gauge theory in technicolor

The methods of lattice gauge theory may be applied to gauge theories besides QCD, in fact to any gauge group and any representation of matter fields (as long as the coupling is not chiral). Such theories are useful for model building beyond the Standard Model, for instance in technicolor models. We have carried out Monte Carlo simulations of the SU(3) gauge theory with color sextet fermions. Our result for its discrete beta function indicate an infrared fixed point that makes the theory conformal rather than confining. The lattice theory's phase diagram shows no separation of chiral and confinement scales, measured when the quark mass is nonzero.

MILP-based approaches for medium-term planning of single-stage continuous multiproduct plants with parallel units

In this paper, we address the problem of medium-term planning of single-stage continuous multiproduct plants with multiple processing units in parallel. Sequence-dependent changeover times and costs occur when switching from one type of product to another. A traveling salesman problem (TSP)-based mixed-integer linear programming (MILP) model is proposed based on a hybrid discrete/continuous time representation. We develop additional constraints and variables to ensure that subtours do not occur in the solution. The model is successfully applied to an example of a polymer processing plant to illustrate its applicability. In order to solve larger model instances and planning horizons, a rolling horizon approach is developed to reduce the computational expense. Finally, the proposed model is compared to a recently published approach through literature examples, and the results show that the computational performance of the proposed model is superior.

A process model of Rho GTP-binding proteins

Rho GTP-binding proteins play a key role as molecular switches in many cellular activities. In response to extracellular stimuli and with the help of regulators (GEF, GAP, Effector, GDI), these proteins serve as switches that interact with their environment in a complex manner. Based on the structure of a published ordinary differential equations (ODE) model, we first present a generic process model for the Rho GTP-binding proteins, and compare it with the ODE model. We then extend the basic model to include the behaviour of the GDI regulators and explore the parameter space for the extended model with respect to biological data from the literature. We discuss the challenges this extension brings and the directions of further research. In particular, we present techniques for modular representation and refinement of process models, where, for example, different Rho proteins with different rates for regulator interactions can be given as instances of the same parametric model.

Representation of product model in collaborative design based on semantic web technology

During product collaborative design, product models lack enough semantics to express the product data explicitly and formally because of the diversity and heterogeneity of product data as well as the limitation of the existing product model expression. To overcome these weak points, semantic web technology is employed to represent the product model. Ontology is used to describe the product model and web ontology language is used to express product model instances concretely in collaborative design. In this case, collaborative design can be effective in achieving sharing, exchange, reuse, and collaboration of product data based on Internet.

Antichains for the Automata-Based Approach to Model-Checking

We propose and evaluate antichain algorithms to solve the universality and language inclusion problems for nondeterministic Buechi automata, and the emptiness problem for alternating Buechi automata. To obtain those algorithms, we establish the existence of simulation pre-orders that can be exploited to efficiently evaluate fixed points on the automata defined during the complementation step (that we keep implicit in our approach). We evaluate the performance of the algorithm to check the universality of Buechi automata using the random automaton model recently proposed by Tabakov and Vardi. We show that on the difficult instances of this probabilistic model, our algorithm outperforms the standard ones by several orders of magnitude.

Selecting Baysian-Network Models Based on Simulated Expectation

Identifying the best network structure from a myriad of candidates is not an easy task, and we propose a supervised learning method for this task. We test the idea with an instance of learning student models from students’ responses to test items, because student models are very important for intelligent tutoring systems. The training data for the classifiers were simulated based on the expectation about students’ item responses when students learn in different ways, and the trained classifier was used to select the model from the list of candidate models based on the observed item responses. Experimental results indicate that, even when item responses do not faithfully reflect students’ competence in the concepts, our classifiers still help us differentiate very similar models with indirect observations.

An objective test of stochastic behavior in riverine water quality models

Predictions of river water quality models are subject to substantial uncertainties, which depend not only on parameterization and calibration strategies but also on the structure of the conceptual model itself. To evaluate the importance of this effect and associated implications for stochastic models, investigations were conducted based on a segment of the Potomac River in the eastern USA. Two commonly used conceptual representations of real-world processes were used, and their simulation of DO, BOD and NH(4) components scrutinized. A GLUE approach to the inverse problem was then used to examine how uncertainty changed along the river network for each conceptual model. Differences were observed not only between deterministic instances of each conceptual model, but also between their response surfaces as a whole. Uncertainties within the river network are substantially influenced by the selection of calibration data used, as well as the primary source of the constituent examined. The suggested methodology can be used to test conceptual model validity for specific applications. The results of this study will help users select and assess models for varied problems, and refine appropriate data collection and monitoring schemes.

Complexity Results for Throughput and Latency Optimization of Replicated and Data-parallel Workflows

Mapping applications onto parallel platforms is a challenging problem, even for simple application patterns such as pipeline or fork graphs. Several antagonist criteria should be optimized for workflow applications, such as throughput and latency (or a combination). In this paper, we consider a simplified model with no communication cost, and we provide an exhaustive list of complexity results for different problem instances. Pipeline or fork stages can be replicated in order to increase the throughput by sending consecutive data sets onto different processors. In some cases, stages can also be data-parallelized, i.e. the computation of one single data set is shared between several processors. This leads to a decrease of the latency and an increase of the throughput. Some instances of this simple model are shown to be NP-hard, thereby exposing the inherent complexity of the mapping problem. We provide polynomial algorithms for other problem instances. Altogether, we provide solid theoretical foundations for the study of mono-criterion or bi-criteria mapping optimization problems.

HYPOTHESES MANAGEMENT FOR DISORDER DIAGNOSIS IN A HIERARCHICAL FRAMEWORK

We propose the use of a knowledge based framework for diagnosis in which the knowledge base consists of particular instances of general hierarchical disorder models. We study how to select which manifestation (symptom, malfunction) to query in order to reduce a set of competing diagnosis hypotheses (disorders), none of them completely satisfying, considering only the observed manifestations. We propose to use general information about the order in which competing disorder models should be probed first to guide us on the task of selecting which particular disorder instances to try to confirm first. We propose to then order which manifestation instances to probe, the presence or absence of which will help us to either confirm or eliminate that hypothesis, according to the principle that "(manifestation) instances that share some characteristics with the instance of manifestation that generated the whole process, but which completely disagree in relation to other characteristics" should be probed first.

Generating instance models from meta models

Meta modeling is a wide-spread technique to define visual languages, with the UML being the most prominent one. Despite several advantages of meta modeling such as ease of use, the meta modeling approach has one disadvantage: it is not constructive, i.e., it does not offer a direct means of generating instances of the language. This disadvantage poses a severe limitation for certain applications. For example, when developing model transformations, it is desirable to have enough valid instance models available for large-scale testing. Producing such a large set by hand is tedious. In the related problem of compiler testing, a string grammar together with a simple generation algorithm is typically used to produce words of the language automatically. In this paper, we introduce instance-generating graph grammars for creating instances of meta models, thereby overcoming the main deficit of the meta modeling approach for defining languages.

Quality of fit of Stable Mass-Spring Models

Shape models are used for many tasks in modern image analysis, e.g., segmentation, tracking, etc. Rarely, the quality of fit of adapted shape models is automatically determined to decide whether the model fitting was successful. This paper develops a principal strategy to measure the success of the model adaptation and defines a quality of fit measure for Stable Mass-Spring Models, which are especially appropriate for this purpose. The parameters of this measure can be estimated from a successfully fitted model instance. The quality of fit measure was tested with two medical segmentation tasks (left ventricles in SPECT data and lymph nodes in CT data) and could be shown to work well. Using application-specific thresholds on the quality of fit, we were able to detect automatically whether a segmentation succeeded or failed.

Scheduling at coal handling facilities using Simulated Annealing

The main thrust of this study is the operational scheduling of the continuous coal handling and blending processes when considering multiple, and sometimes conflicting, objectives. A widely applicable generic goal programming model is proposed. Furthermore, assumptions regarding the certainty of demand during different periods are challenged, endeavoring to provide more robust schedules in a largely stochastic environment. As the study aims to provide scheduling solutions to any coal handling facility, the Simulated Annealing metaheuristic is proposed to ensure that acceptably good solutions for large instances of the generic model can be found in reasonable computational time. The generic approach and its suggested application will be valuable not only in the coal handling environment, but also in the continuous product manufacturing/blending or continuous material handling environment.

Dynamics of non-renormalizable electroweak symmetry breaking

We compute the complete one-loop finite temperature effective potential for electroweak symmetry breaking in the Standard Model with a Higgs potential supplemented by higher dimensional operators as generated for instance in composite Higgs and Little Higgs models. We detail the resolution of several issues that arise, such as the cancellation of infrared divergences at higher order and imaginary contributions to the potential. We follow the dynamics of the phase transition, including the nucleation of bubbles and the effects of supercooling. We characterize the region of parameter space consistent with a strong first-order phase transition which may be relevant to electroweak baryogenesis. Finally, we investigate the prospects of present and future gravity wave detectors to see the effects of a strong first-order electroweak phase transition.

Exploring an Option Space to Engineer a Ubiquitous Computing System

Engineering natural and appropriate interactive behaviour in ubiquitous computing systems presents new challenges to their developers. This paper explores formal models of interactive behaviour in ubiquitous systems. Of particular interest is the way that these models may help engineers to visualise the consequences of different designs. Design options based on a dynamic signage system (GAUDI) are explored using different instances of a generic model of the system.

Translation of Restricted OCL Constraints into Graph Constraints for Generating Meta Model Instances by Graph Grammars

The meta modeling approach to syntax definition of visual modeling techniques has gained wide acceptance, especially by using it for the definition of UML. Since meta-modeling is non-constructive, it does not provide a systematic way to generate all possible meta model instances. In our approach, an instance-generating graph grammar is automatically created from a given meta model. This graph grammar ensures correct typing and cardinality constraints, but OCL constraints for the meta model are not supported yet. To satisfy also the given OCL constraints, well-formedness checks have to be done in addition. We present a restricted form of OCL constraints that can be translated to graph constraints which can be checked during the instance generation process.

Generalized parallel-server fork-join queues with dynamic task scheduling

This paper introduces a generalization of the classical parallel-server fork-join queueing system in which arriving customers fork into multiple tasks, every task is uniquely assigned to one of the set of single-server queues, and each task consists of multiple iterations of different stages of execution, including task vacations and communication among sibling tasks. Several classes of dynamic polices are considered for scheduling multiple tasks at each of the single-server queues to maintain effective server utilization. The paper presents an exact matrix-analytic analysis of generalized parallel-server fork-join queueing systems, for small instances of the stochastic model, and presents an approximate matrix-analytic analysis and fixed-point solution, for larger instances of the model.