Graphical Framework Research Articles

Concept of decision graphical framework for optimising adaptation of civil infrastructure to a changing climate

This article proposes a framework for decision optimisation for adaptation of civil infrastructure to a changing climate, taking basis in decision graphical representation. The focus of the article is to develop a platform on which multiple climate scenarios and general circulation models, results from probabilistic hazard analysis as well as consequence analysis – together with their uncertainties – are represented in a practically operational manner. The output from the decision framework is the optimal timing and magnitude of the adaptation. First, assumptions underlying the proposed decision framework and types of information required are provided. Thereafter, the proposed decision framework is presented. This work provides the concept only; however, it serves as an exercise and thus provides a research direction on how to collect and summarise information for the purpose to approach to the civil infrastructure adaptation in an emerging climate change.

Testing Differential Networks with Applications to Detecting Gene-by-Gene Interactions.

Model organisms and human studies have led to increasing empirical evidence that interactions among genes contribute broadly to genetic variation of complex traits. In the presence of gene-by-gene interactions, the dimensionality of the feature space becomes extremely high relative to the sample size. This imposes a significant methodological challenge in identifying gene-by-gene interactions. In the present paper, through a Gaussian graphical model framework, we translate the problem of identifying gene-by-gene interactions associated with a binary trait D into an inference problem on the difference of two high-dimensional precision matrices, which summarize the conditional dependence network structures of the genes. We propose a procedure for testing the differential network globally that is particularly powerful against sparse alternatives. In addition, a multiple testing procedure with false discovery rate control is developed to infer the specific structure of the differential network. Theoretical justification is provided to ensure the validity of the proposed tests and optimality results are derived under sparsity assumptions. A simulation study demonstrates that the proposed tests maintain the desired error rates under the null and have good power under the alternative. The methods are applied to a breast cancer gene expression study.

Separation of therapeutic peptides with cyclofructan and glycopeptide based columns in hydrophilic interaction liquid chromatography

Three cyclofructan-based, two glycopeptide-based, and one zwitterionic column used in the HILIC mode were assessed within a graphical framework based on different functional characteristics contributing to selectivity. The characteristics of these six HILIC columns are put in the perspective of 33 columns evaluated previously. The isopropyl carbamate modified cyclofructan 6 (CF6) stationary phase, Larihc P, showed reduced component contributions for hydrophilicity and hydrogen bonding relative to the native cyclofructan 6 column (Frulic N). Both Frulic N and Larihc P exhibited cation exchange attributed primarily to deprotonation of residual unsubstituted silica with the greater exchange ascribed to the reduced loading of CF6 observed for Larihc P. The cyclofructan 6 column with a polymeric styrene divinylbenzene support (MCI GEL™ CRS100) showed distinct selectivities consistent with its decreased cation exchange attributable to its nonionic core. The Chirobiotic T, Chirobiotic V, and ZI-DPPS columns displayed hydrophilicity and ion exchange selectivities similar to other zwitterionic stationary phases. All of the more hydrophilic columns showed excellent separation for the four classes of therapeutic peptides investigated: microbial secondary metabolites used as immune suppressants, synthetic gonadotropin hormones, synthetic cyclic disulfide-linked hormone-regulating hormones, and non-ribosomally derived polycyclic antibiotics. Resolution provided by these columns and ZIC-HILIC is compared for each class of peptide. Frulic N is primarily suitable for use in the HILIC mode whereas Chirobiotic T, because of its increased efficiency and selectivity, can be useful in both HILIC and reverse phase modes. In some Chirobiotic T applications, addition of low levels of a strong additive (trifluoroacetic acid, formic acid, etc.) to the mobile phase can be beneficial. In these peptide analyses, a relative weakening of the often-dominant ionic interaction between analyte and residual charge on the stationary phase improved resolution and selectivity.

Word-level language modeling for P300 spellers based on discriminative graphical models

Objective. In this work we propose a probabilistic graphical model framework that uses language priors at the level of words as a mechanism to increase the performance of P300-based spellers. Approach. This paper is concerned with brain-computer interfaces based on P300 spellers. Motivated by P300 spelling scenarios involving communication based on a limited vocabulary, we propose a probabilistic graphical model framework and an associated classification algorithm that uses learned statistical models of language at the level of words. Exploiting such high-level contextual information helps reduce the error rate of the speller. Main results. Our experimental results demonstrate that the proposed approach offers several advantages over existing methods. Most importantly, it increases the classification accuracy while reducing the number of times the letters need to be flashed, increasing the communication rate of the system. Significance. The proposed approach models all the variables in the P300 speller in a unified framework and has the capability to correct errors in previous letters in a word, given the data for the current one. The structure of the model we propose allows the use of efficient inference algorithms, which in turn makes it possible to use this approach in real-time applications.

CrowdWON: A Modelling Language for Crowd Processes based on Workflow Nets

Although crowdsourcing has been proven efficient as a mechanism to solve independent tasks for on-line production, it is still unclear how to define and manage workflows in complex tasks that require the participation and coordination of different workers. Despite the existence of different frameworks to define workflows, we still lack a commonly accepted solution that is able to describe the most common workflows in current and future platforms. In this paper, we propose CrowdWON, a new graphical framework to describe and monitor crowd processes, the proposed language is able to represent the workflow of most well-known existing applications, extend previous modelling frameworks, and assist in the future generation of crowdsourcing platforms. Beyond previous proposals, CrowdWON allows for the formal definition of adaptative workflows, that depend on the skills of the crowd workers and/or process deadlines. CrowdWON also allows expressing constraints on workers based on previous individual contributions. Finally, we show how our proposal can be used to describe well known crowdsourcing workflows.

Task-Based Robot Grasp Planning Using Probabilistic Inference

Grasping and manipulating everyday objects in a goal-directed manner is an important ability of a service robot. The robot needs to reason about task requirements and ground these in the sensorimotor information. Grasping and interaction with objects are challenging in real-world scenarios, where sensorimotor uncertainty is prevalent. This paper presents a probabilistic framework for the representation and modeling of robot-grasping tasks. The framework consists of Gaussian mixture models for generic data discretization, and discrete Bayesian networks for encoding the probabilistic relations among various task-relevant variables, including object and action features as well as task constraints. We evaluate the framework using a grasp database generated in a simulated environment including a human and two robot hand models. The generative modeling approach allows the prediction of grasping tasks given uncertain sensory data, as well as object and grasp selection in a task-oriented manner. Furthermore, the graphical model framework provides insights into dependencies between variables and features relevant for object grasping.

Efficient local updates for undirected graphical models

We present a new Bayesian approach for undirected Gaussian graphical model determination. We provide some graph theory results for local updates that facilitate a fast exploration of the graph space. Specifically, we show how to locally update, after either edge deletion or inclusion, the perfect sequence of cliques and the perfect elimination order of the nodes associated to an oriented, directed acyclic version of a decomposable graph. Building upon the decomposable graphical models framework, we propose a more flexible methodology that extends to the class of nondecomposable graphs. Posterior probabilities of edge inclusion are interpreted as a natural measure of edge selection uncertainty. When applied to a protein expression data set, the model leads to fast estimation of the protein interaction network.

Vine Copula Specifications for Stationary Multivariate Markov Chains

Vine copulae provide a graphical framework in which multiple bivariate copulae may be combined in a consistent fashion to yield a more complex multivariate copula. In this article, we discuss the use of vine copulae to build flexible semiparametric models for stationary multivariate higher‐order Markov chains. We propose a new vine structure, the M‐vine, that is particularly well suited to this purpose. Stationarity may be imposed by requiring the equality of certain copulae in the M‐vine, while the Markov property may be imposed by requiring certain copulae to be independence copulae.

Graphical programming interface: A development environment for MRI methods.

To introduce a multiplatform, Python language-based, development environment called graphical programming interface for prototyping MRI techniques. The interface allows developers to interact with their scientific algorithm prototypes visually in an event-driven environment making tasks such as parameterization, algorithm testing, data manipulation, and visualization an integrated part of the work-flow. Algorithm developers extend the built-in functionality through simple code interfaces designed to facilitate rapid implementation. This article shows several examples of algorithms developed in graphical programming interface including the non-Cartesian MR reconstruction algorithms for PROPELLER and spiral as well as spin simulation and trajectory visualization of a FLORET example. The graphical programming interface framework is shown to be a versatile prototyping environment for developing numeric algorithms used in the latest MR techniques.

R(4, 4) As a Computational Framework for 3-Dimensional Computer Graphics

We investigate the efficacy of the Clifford algebra R(4, 4) as a computational framework for contemporary 3-dimensional computer graphics. We give explicit rotors in R(4, 4) for all the standard affine and projective transformations in the graphics pipeline, including translation, rotation, reflection, uniform and nonuniform scaling, classical and scissor shear, orthogonal and perspective projection, and pseudoperspective. We also explain how to represent planes by vectors and quadric surfaces by bivectors in R(4, 4), and we show how to apply rotors in R(4, 4) to these vectors and bivectors to transform planes and quadric surfaces by affine transformations.

A Third-Order Item Response Theory Model for Modeling the Effects of Domains and Subdomains in Large-Scale Educational Assessment Surveys

Second-order item response theory models have been used for assessments consisting of several domains, such as content areas. We extend the second-order model to a third-order model for assessments that include subdomains nested in domains. Using a graphical model framework, it is shown how the model does not suffer from the curse of multidimensionality. We apply unidimensional, second-order, and third-order item response models to the 2007 Trends in International Mathematics and Science Study. Our findings suggest that deviations from unidimensionality are more pronounced at the content domain level than at the cognitive domain level and that deviations from unidimensionality at the content domain level become negligible after taking into account topic areas.

Applied Technology in "GNSS if Signal Acquisition System" and Software Receiver Design

The paper completed the software and hardware design of GNSS intermediate frequency signal acquisition system under Linux operating system, realized the GNSS software receiver under Linux. Basing on QT C++ graphical user interface application development framework and Qwt plug-in, the software and data graphical display interface was also accomplished. By caching the data of RF module and controlling read and write signals of CY7C68013A through FPGA, the high-speed data transmission function was completed. The test results show that the data transmission rate of this data acquisition system can reach more than 200 Mbps, software receiver satisfies the requirement of positioning accuracy, has good flexibility and higher adaptability, and the algorithm of which was easy to be expanded and upgraded.

Marco Conceptual para Ayudar al Maestro de Instrumento a Integrar la Improvisación Musical en su Práctica Pedagógica

AbstractThis essay aims at offering a conceptual framework to help instrument teachers to include improvisation in their pedagogical practice. In order to do so, we have reviewed and analyzed the literature addressing musical improvisation, with a special focus on theoretical models and pedagogical applications. The analysis allowed us to develop a functional definition of musical improvisation, to identify its positive contribution to the learning process and to other aspects, to identify factors and cognitive processes involved in production of musical improvisation, and to inventory pedagogical recommendations found in this literature. Based on the analyzed literature, we finally developed a graphic conceptual framework, which puts together the necessary knowledge to operationalize the process of teaching musical improvisation. ResumenEste artículo tiene como objetivo proponer un marco conceptual para ayudar al maestro de música a integrar la improvisación musical en su acto pedagógico. Para desarrollar este marco, primero se llevó a cabo una revisión y análisis de la literatura disponible sobre el tema de la improvisación musical, prestando especial atención en los modelos teóricos y las aplicaciones pedagógicas. Este análisis nos permitió: 1) formular una definición operativa de la improvisación musical; 2) identificar los efectos positivos del aprendizaje de la improvisación musical sobre otras variables; 3) identificar los factores y los procesos cognitivos que entran en juego durante la realización de la improvisación musical; 4) destacar las recomendaciones pedagógicas encontradas en la literatura para la enseñanza de la improvisación musical. Finalmente, hemos desarrollado, a partir de la literatura consultada, un marco conceptual que presenta gráficamente los conocimientos necesarios para poner en práctica el acto de enseñar la improvisación musical.

Graph-based sensor fusion for classification of transient acoustic signals.

Advances in acoustic sensing have enabled the simultaneous acquisition of multiple measurements of the same physical event via co-located acoustic sensors. We exploit the inherent correlation among such multiple measurements for acoustic signal classification, to identify the launch/impact of munition (i.e., rockets, mortars). Specifically, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between the cepstral features extracted from these different measurements. Additionally, we employ symbolic dynamic filtering-based features, which offer improvements over the traditional cepstral features in terms of robustness to signal distortions. Experiments on real acoustic data sets show that our proposed algorithm outperforms conventional classifiers as well as the recently proposed joint sparsity models for multisensor acoustic classification. Additionally our proposed algorithm is less sensitive to insufficiency in training samples compared to competing approaches.

Real-Time Semiparametric Regression

We develop algorithms for performing semiparametric regression analysis in real time, with data processed as it is collected and made immediately available via modern telecommunications technologies. Our definition of semiparametric regression is quite broad and includes, as special cases, generalized linear mixed models, generalized additive models, geostatistical models, wavelet nonparametric regression models and their various combinations. Fast updating of regression fits is achieved by couching semiparametric regression into a Bayesian hierarchical model or, equivalently, graphical model framework and employing online mean field variational ideas. An Internet site attached to this article, realtime-semiparametric-regression.net, illustrates the methodology for continually arriving stock market, real estate, and airline data. Flexible real-time analyses based on increasingly ubiquitous streaming data sources stand to benefit. This article has online supplementary material.

Probabilistic graphical model representation in phylogenetics.

Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (i) reproducibility of an analysis, (ii) model development, and (iii) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and nonspecialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis–Hastings or Gibbs sampling of the posterior distribution. [Computation; graphical models; inference; modularization; statistical phylogenetics; tree plate.]

Instrumentino: An open-source modular Python framework for controlling Arduino based experimental instruments

Instrumentino is an open-source modular graphical user interface framework for controlling Arduino based experimental instruments. It expands the control capability of Arduino by allowing instruments builders to easily create a custom user interface program running on an attached personal computer. It enables the definition of operation sequences and their automated running without user intervention. Acquired experimental data and a usage log are automatically saved on the computer for further processing. The use of the programming language Python also allows easy extension. Complex devices, which are difficult to control using an Arduino, may be integrated as well by incorporating third party application programming interfaces into the Instrumentino framework. Program summaryProgram title: Instrumentino, ControlinoCatalogue identifier: AETJ_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AETJ_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU General Public License, version 3No. of lines in distributed program, including test data, etc.: 17097No. of bytes in distributed program, including test data, etc.: 3425023Distribution format: tar.gzProgramming language: Python, C.Computer: i386, x86-64.Operating system: Linux, Mac OS X, Windows.RAM: 60 MBClassification: 16.4.External routines: wxPython, pySerial, matplotlib, agw (Instrumentino), SoftwareSerial (Controlino)Nature of problem:Control and monitor purpose-made experimental instrumentsSolution method:Modular Graphical User Interface for hardware controlRunning time:Depends on the user.

Paying on the margin for medical care: Evidence from breast cancer treatments.

We present a simple graphical framework to illustrate the potential welfare gains from a "top-up" health insurance policy requiring patients to pay the incremental price for more expensive treatment options. We apply this framework to breast cancer treatments, where lumpectomy with radiation therapy is more expensive than mastectomy but generates similar average health benefits. We estimate the relative demand for lumpectomy using variation in distance to the nearest radiation facility, and estimate that the "top-up" policy increases social welfare by $700-2,500 per patient relative to two common alternatives. We briefly discuss additional tradeoffs that arise from an ex-ante perspective.

Index of Joint Condition for PVC push-fit joints

The Index of Joint Condition (IJC) for polyvinyl chloride (PVC) push-fit joints, discussed in this article, was derived from installation guidelines and from destructive laboratory tests. The IJC is presented in a graphical framework and is a powerful tool to employ in order to visualize and compare, in situ, results obtained during condition assessment of PVC joints. The graphical results can also be translated into a numerical grade that allows comparing the conditions of various pipes and of individual joints. The applicability of the IJC is demonstrated in the condition assessment of 222 joints inspected in eight different sessions that encapsulate more than 2 km of older (more than 40 years) and newer pipes (less than 2 months). While, for the new pipe, all joints were considered to be in good condition – low replacement priority –, several joints in older pipes were considered to have high replacement priority.

Allocation tradeoffs and life histories: a conceptual and graphical framework

Tradeoffs – negative reciprocal causal relationships in net benefits between trait magnitudes – have not always been studied in depth appropriate to their central role in life‐history analysis. Here we focus on allocation tradeoffs, in which acquisition of a limiting resource requires allocation of resource to alternative traits. We identify the components of this allocation process and emphasize the importance of quantifying them. We then propose categorizing allocation tradeoffs into linear, concave and convex relationships based on the way that resource allocation yields trait magnitudes under the tradeoff. Linear relationships are over‐represented in the literature because of typically small data sets over restricted ranges of trait magnitudes, an emphasis on simple correlation analysis, and a failure to remove variation associated with acquisition of the limiting resource in characterizing the tradeoff. (We provide methods for controlling these acquisition effects.) Non‐linear relationships have been documented and are expected under plausible conditions that we summarize. We note ways that shifting environments and biological features yield plasticity of tradeoff graphs. Finally, we illustrate these points using case studies and close with priorities for future work.