AND/OR graph representation of assembly plans

NASA Technical Reports Server (NTRS)

Homem De Mello, Luiz S.; Sanderson, Arthur C.

1990-01-01

A compact representation of all possible assembly plans of a product using AND/OR graphs is presented as a basis for efficient planning algorithms that allow an intelligent robot to pick a course of action according to instantaneous conditions. The AND/OR graph is equivalent to a state transition graph but requires fewer nodes and simplifies the search for feasible plans. Three applications are discussed: (1) the preselection of the best assembly plan, (2) the recovery from execution errors, and (3) the opportunistic scheduling of tasks. An example of an assembly with four parts illustrates the use of the AND/OR graph representation in assembly-plan preselection, based on the weighting of operations according to complexity of manipulation and stability of subassemblies. A hypothetical error situation is discussed to show how a bottom-up search of the AND/OR graph leads to an efficient recovery.

Label Information Guided Graph Construction for Semi-Supervised Learning.

PubMed

Zhuang, Liansheng; Zhou, Zihan; Gao, Shenghua; Yin, Jingwen; Lin, Zhouchen; Ma, Yi

2017-09-01

In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.

"I remember when we stayed still and the computer still made lines": Young children's invented and conventional representations of motion

NASA Astrophysics Data System (ADS)

Kahn, Jason

This dissertation concerns kindergarteners' and second graders' invented representations of motion, their interactions with conventional representations of motion built from the child's movement in front of a motion detector and using real-time graphing tools, and any changes in the invented representations that this interaction brings about. We have known for several decades that advanced learners (high school aged and beyond) struggle with physics concepts of motion and sometimes Cartesian graph-based representations of motion. Little has been known about how younger students approach the same concepts. In this study, eighteen children (10 kindergarteners and eight second graders) completed a three-hour clinical interview spread out evenly over three weeks. In the first and last interviews, the child was asked to produce external representations of movement and interpret conventional distance and time graphs of motion. In the second interview the children interacted with a motion detector and real-time graphing tools in a semi-self-directed format. Qualitative and quantitative results are presented and discussed. Qualitative data shows that children are adroit at representing motion and their productions are systematic and purposeful. Children produce drawings that both give context to the physical environment around them and also redescribe the drawn environment, meaning that they provide a potential audience with information otherwise imperceptible, by making certain implicit aspects more explicit. Second graders quickly appropriate the Cartesian graph during the intervention, though at times misinterpret the meaning associated with slope. Children correctly associate slope with direction, but at times misattribute sign of slope (positive or negative) and its corresponding direction (i.e. some children do not ascribe positive slope with motion away from a point of reference, but toward it). Kindergarteners showed a range of experiences during the intervention, one of the students showed a near mastery in interpretation of a Cartesian graph as a representation of motion, while another vehemently resisted graph as a representation of motion. Quantitative data gives a mechanism for comparing pre- and post-assessment productions. Both kindergarten and second grade students provide richer post-assessment representations, with kindergarteners more likely to include a figurative point of reference in the post-assessment and second graders including more explicit information about speed. The implications of this study are that invented representations of motion are a powerful tool for providing insights into children's thinking. The motion detector and real-time graphing tool can be used as early as kindergarten to help children build resources in their representations of motion; second grade students could find the same benefit and potentially begin to build conventional ideas about graphing and movement.

deBGR: an efficient and near-exact representation of the weighted de Bruijn graph

PubMed Central

Pandey, Prashant; Bender, Michael A.; Johnson, Rob; Patro, Rob

2017-01-01

Abstract Motivation: Almost all de novo short-read genome and transcriptome assemblers start by building a representation of the de Bruijn Graph of the reads they are given as input. Even when other approaches are used for subsequent assembly (e.g. when one is using ‘long read’ technologies like those offered by PacBio or Oxford Nanopore), efficient k-mer processing is still crucial for accurate assembly, and state-of-the-art long-read error-correction methods use de Bruijn Graphs. Because of the centrality of de Bruijn Graphs, researchers have proposed numerous methods for representing de Bruijn Graphs compactly. Some of these proposals sacrifice accuracy to save space. Further, none of these methods store abundance information, i.e. the number of times that each k-mer occurs, which is key in transcriptome assemblers. Results: We present a method for compactly representing the weighted de Bruijn Graph (i.e. with abundance information) with essentially no errors. Our representation yields zero errors while increasing the space requirements by less than 18–28% compared to the approximate de Bruijn graph representation in Squeakr. Our technique is based on a simple invariant that all weighted de Bruijn Graphs must satisfy, and hence is likely to be of general interest and applicable in most weighted de Bruijn Graph-based systems. Availability and implementation: https://github.com/splatlab/debgr. Contact: rob.patro@cs.stonybrook.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881995

From Number Lines to Graphs in the Coordinate Plane: Investigating Problem Solving across Mathematical Representations

ERIC Educational Resources Information Center

Earnest, Darrell

2015-01-01

This article reports on students' problem-solving approaches across three representations--number lines, coordinate planes, and function graphs--the axes of which conventional mathematics treats in terms of consistent geometric and numeric coordinations. I consider these representations to be a part of a "hierarchical representational…

Reduced graphs and their applications in chemoinformatics.

PubMed

Birchall, Kristian; Gillet, Valerie J

2011-01-01

Reduced graphs provide summary representations of chemical structures by collapsing groups of connected atoms into single nodes while preserving the topology of the original structures. This chapter reviews the extensive work that has been carried out on reduced graphs at The University of Sheffield and includes discussion of their application to the representation and search of Markush structures in patents, the varied approaches that have been implemented for similarity searching, their use in cluster representation, the different ways in which they have been applied to extract structure-activity relationships and their use in encoding bioisosteres.

Analysis of graphic representation ability in oscillation phenomena

NASA Astrophysics Data System (ADS)

Dewi, A. R. C.; Putra, N. M. D.; Susilo

2018-03-01

This study aims to investigates how the ability of students to representation graphs of linear function and harmonic function in understanding of oscillation phenomena. Method of this research used mix methods with concurrent embedded design. The subjects were 35 students of class X MIA 3 SMA 1 Bae Kudus. Data collection through giving essays and interviews that lead to the ability to read and draw graphs in material of Hooke's law and oscillation characteristics. The results of study showed that most of the students had difficulty in drawing graph of linear function and harmonic function of deviation with time. Students’ difficulties in drawing the graph of linear function is the difficulty of analyzing the variable data needed in graph making, confusing the placement of variable data on the coordinate axis, the difficulty of determining the scale interval on each coordinate, and the variation of how to connect the dots forming the graph. Students’ difficulties in representing the graph of harmonic function is to determine the time interval of sine harmonic function, the difficulty to determine the initial deviation point of the drawing, the difficulty of finding the deviation equation of the case of oscillation characteristics and the confusion to different among the maximum deviation (amplitude) with the length of the spring caused the load.Complexity of the characteristic attributes of the oscillation phenomena graphs, students tend to show less well the ability of graphical representation of harmonic functions than the performance of the graphical representation of linear functions.

GraDit: graph-based data repair algorithm for multiple data edits rule violations

NASA Astrophysics Data System (ADS)

Ode Zuhayeni Madjida, Wa; Gusti Bagus Baskara Nugraha, I.

2018-03-01

Constraint-based data cleaning captures data violation to a set of rule called data quality rules. The rules consist of integrity constraint and data edits. Structurally, they are similar, where the rule contain left hand side and right hand side. Previous research proposed a data repair algorithm for integrity constraint violation. The algorithm uses undirected hypergraph as rule violation representation. Nevertheless, this algorithm can not be applied for data edits because of different rule characteristics. This study proposed GraDit, a repair algorithm for data edits rule. First, we use bipartite-directed hypergraph as model representation of overall defined rules. These representation is used for getting interaction between violation rules and clean rules. On the other hand, we proposed undirected graph as violation representation. Our experimental study showed that algorithm with undirected graph as violation representation model gave better data quality than algorithm with undirected hypergraph as representation model.

Visualization of Morse connection graphs for topologically rich 2D vector fields.

PubMed

Szymczak, Andrzej; Sipeki, Levente

2013-12-01

Recent advances in vector field topologymake it possible to compute its multi-scale graph representations for autonomous 2D vector fields in a robust and efficient manner. One of these representations is a Morse Connection Graph (MCG), a directed graph whose nodes correspond to Morse sets, generalizing stationary points and periodic trajectories, and arcs - to trajectories connecting them. While being useful for simple vector fields, the MCG can be hard to comprehend for topologically rich vector fields, containing a large number of features. This paper describes a visual representation of the MCG, inspired by previous work on graph visualization. Our approach aims to preserve the spatial relationships between the MCG arcs and nodes and highlight the coherent behavior of connecting trajectories. Using simulations of ocean flow, we show that it can provide useful information on the flow structure. This paper focuses specifically on MCGs computed for piecewise constant (PC) vector fields. In particular, we describe extensions of the PC framework that make it more flexible and better suited for analysis of data on complex shaped domains with a boundary. We also describe a topology simplification scheme that makes our MCG visualizations less ambiguous. Despite the focus on the PC framework, our approach could also be applied to graph representations or topological skeletons computed using different methods.

Flexibility in data interpretation: effects of representational format.

PubMed

Braithwaite, David W; Goldstone, Robert L

2013-01-01

Graphs and tables differentially support performance on specific tasks. For tasks requiring reading off single data points, tables are as good as or better than graphs, while for tasks involving relationships among data points, graphs often yield better performance. However, the degree to which graphs and tables support flexibility across a range of tasks is not well-understood. In two experiments, participants detected main and interaction effects in line graphs and tables of bivariate data. Graphs led to more efficient performance, but also lower flexibility, as indicated by a larger discrepancy in performance across tasks. In particular, detection of main effects of variables represented in the graph legend was facilitated relative to detection of main effects of variables represented in the x-axis. Graphs may be a preferable representational format when the desired task or analytical perspective is known in advance, but may also induce greater interpretive bias than tables, necessitating greater care in their use and design.

A fast algorithm for vertex-frequency representations of signals on graphs

PubMed Central

Jestrović, Iva; Coyle, James L.; Sejdić, Ervin

2016-01-01

The windowed Fourier transform (short time Fourier transform) and the S-transform are widely used signal processing tools for extracting frequency information from non-stationary signals. Previously, the windowed Fourier transform had been adopted for signals on graphs and has been shown to be very useful for extracting vertex-frequency information from graphs. However, high computational complexity makes these algorithms impractical. We sought to develop a fast windowed graph Fourier transform and a fast graph S-transform requiring significantly shorter computation time. The proposed schemes have been tested with synthetic test graph signals and real graph signals derived from electroencephalography recordings made during swallowing. The results showed that the proposed schemes provide significantly lower computation time in comparison with the standard windowed graph Fourier transform and the fast graph S-transform. Also, the results showed that noise has no effect on the results of the algorithm for the fast windowed graph Fourier transform or on the graph S-transform. Finally, we showed that graphs can be reconstructed from the vertex-frequency representations obtained with the proposed algorithms. PMID:28479645

Supervoxels for graph cuts-based deformable image registration using guided image filtering

NASA Astrophysics Data System (ADS)

Szmul, Adam; Papież, Bartłomiej W.; Hallack, Andre; Grau, Vicente; Schnabel, Julia A.

2017-11-01

We propose combining a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for three-dimensional (3-D) deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to two-dimensional (2-D) applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation combined with graph cuts-based optimization can be applied to 3-D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model "sliding motion." Applying this method to lung image registration results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available computed tomography lung image dataset leads to the observation that our approach compares very favorably with state of the art methods in continuous and discrete image registration, achieving target registration error of 1.16 mm on average per landmark.

Supervoxels for Graph Cuts-Based Deformable Image Registration Using Guided Image Filtering.

PubMed

Szmul, Adam; Papież, Bartłomiej W; Hallack, Andre; Grau, Vicente; Schnabel, Julia A

2017-10-04

In this work we propose to combine a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for 3D deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to 2D applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation, combined with graph cuts-based optimization can be applied to 3D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model 'sliding motion'. Applying this method to lung image registration, results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available Computed Tomography lung image dataset (www.dir-lab.com) leads to the observation that our new approach compares very favorably with state-of-the-art in continuous and discrete image registration methods achieving Target Registration Error of 1.16mm on average per landmark.

Supervoxels for Graph Cuts-Based Deformable Image Registration Using Guided Image Filtering

PubMed Central

Szmul, Adam; Papież, Bartłomiej W.; Hallack, Andre; Grau, Vicente; Schnabel, Julia A.

2017-01-01

In this work we propose to combine a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for 3D deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to 2D applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation, combined with graph cuts-based optimization can be applied to 3D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model ‘sliding motion’. Applying this method to lung image registration, results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available Computed Tomography lung image dataset (www.dir-lab.com) leads to the observation that our new approach compares very favorably with state-of-the-art in continuous and discrete image registration methods achieving Target Registration Error of 1.16mm on average per landmark. PMID:29225433

Enhancing graphical literacy skills in the high school science classroom via authentic, intensive data collection and graphical representation exposure

NASA Astrophysics Data System (ADS)

Palmeri, Anthony

This research project was developed to provide extensive practice and exposure to data collection and data representation in a high school science classroom. The student population engaged in this study included 40 high school sophomores enrolled in two microbiology classes. Laboratory investigations and activities were deliberately designed to include quantitative data collection that necessitated organization and graphical representation. These activities were embedded into the curriculum and conducted in conjunction with the normal and expected course content, rather than as a separate entity. It was expected that routine practice with graph construction and interpretation would result in improved competency when graphing data and proficiency in analyzing graphs. To objectively test the effectiveness in achieving this goal, a pre-test and post-test that included graph construction, interpretation, interpolation, extrapolation, and analysis was administered. Based on the results of a paired T-Test, graphical literacy was significantly enhanced by extensive practice and exposure to data representation.

Phase unwrapping with graph cuts optimization and dual decomposition acceleration for 3D high-resolution MRI data.

PubMed

Dong, Jianwu; Chen, Feng; Zhou, Dong; Liu, Tian; Yu, Zhaofei; Wang, Yi

2017-03-01

Existence of low SNR regions and rapid-phase variations pose challenges to spatial phase unwrapping algorithms. Global optimization-based phase unwrapping methods are widely used, but are significantly slower than greedy methods. In this paper, dual decomposition acceleration is introduced to speed up a three-dimensional graph cut-based phase unwrapping algorithm. The phase unwrapping problem is formulated as a global discrete energy minimization problem, whereas the technique of dual decomposition is used to increase the computational efficiency by splitting the full problem into overlapping subproblems and enforcing the congruence of overlapping variables. Using three dimensional (3D) multiecho gradient echo images from an agarose phantom and five brain hemorrhage patients, we compared this proposed method with an unaccelerated graph cut-based method. Experimental results show up to 18-fold acceleration in computation time. Dual decomposition significantly improves the computational efficiency of 3D graph cut-based phase unwrapping algorithms. Magn Reson Med 77:1353-1358, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Graphical Language for Data Processing

NASA Technical Reports Server (NTRS)

Alphonso, Keith

2011-01-01

A graphical language for processing data allows processing elements to be connected with virtual wires that represent data flows between processing modules. The processing of complex data, such as lidar data, requires many different algorithms to be applied. The purpose of this innovation is to automate the processing of complex data, such as LIDAR, without the need for complex scripting and programming languages. The system consists of a set of user-interface components that allow the user to drag and drop various algorithmic and processing components onto a process graph. By working graphically, the user can completely visualize the process flow and create complex diagrams. This innovation supports the nesting of graphs, such that a graph can be included in another graph as a single step for processing. In addition to the user interface components, the system includes a set of .NET classes that represent the graph internally. These classes provide the internal system representation of the graphical user interface. The system includes a graph execution component that reads the internal representation of the graph (as described above) and executes that graph. The execution of the graph follows the interpreted model of execution in that each node is traversed and executed from the original internal representation. In addition, there are components that allow external code elements, such as algorithms, to be easily integrated into the system, thus making the system infinitely expandable.

A clustering-based graph Laplacian framework for value function approximation in reinforcement learning.

PubMed

Xu, Xin; Huang, Zhenhua; Graves, Daniel; Pedrycz, Witold

2014-12-01

In order to deal with the sequential decision problems with large or continuous state spaces, feature representation and function approximation have been a major research topic in reinforcement learning (RL). In this paper, a clustering-based graph Laplacian framework is presented for feature representation and value function approximation (VFA) in RL. By making use of clustering-based techniques, that is, K-means clustering or fuzzy C-means clustering, a graph Laplacian is constructed by subsampling in Markov decision processes (MDPs) with continuous state spaces. The basis functions for VFA can be automatically generated from spectral analysis of the graph Laplacian. The clustering-based graph Laplacian is integrated with a class of approximation policy iteration algorithms called representation policy iteration (RPI) for RL in MDPs with continuous state spaces. Simulation and experimental results show that, compared with previous RPI methods, the proposed approach needs fewer sample points to compute an efficient set of basis functions and the learning control performance can be improved for a variety of parameter settings.

Process synthesis involving multi-period operations by the P-graph framework

EPA Science Inventory

The P-graph (process graph) framework is an effective tool for process-network synthesis (PNS). Here we extended it to multi-period operations. The efficacy of the P-graph methodology has been demonstrated by numerous applications. The unambiguous representation of processes and ...

Flexibility in data interpretation: effects of representational format

PubMed Central

Braithwaite, David W.; Goldstone, Robert L.

2013-01-01

Graphs and tables differentially support performance on specific tasks. For tasks requiring reading off single data points, tables are as good as or better than graphs, while for tasks involving relationships among data points, graphs often yield better performance. However, the degree to which graphs and tables support flexibility across a range of tasks is not well-understood. In two experiments, participants detected main and interaction effects in line graphs and tables of bivariate data. Graphs led to more efficient performance, but also lower flexibility, as indicated by a larger discrepancy in performance across tasks. In particular, detection of main effects of variables represented in the graph legend was facilitated relative to detection of main effects of variables represented in the x-axis. Graphs may be a preferable representational format when the desired task or analytical perspective is known in advance, but may also induce greater interpretive bias than tables, necessitating greater care in their use and design. PMID:24427145

Composing Data Parallel Code for a SPARQL Graph Engine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Castellana, Vito G.; Tumeo, Antonino; Villa, Oreste

Big data analytics process large amount of data to extract knowledge from them. Semantic databases are big data applications that adopt the Resource Description Framework (RDF) to structure metadata through a graph-based representation. The graph based representation provides several benefits, such as the possibility to perform in memory processing with large amounts of parallelism. SPARQL is a language used to perform queries on RDF-structured data through graph matching. In this paper we present a tool that automatically translates SPARQL queries to parallel graph crawling and graph matching operations. The tool also supports complex SPARQL constructs, which requires more than basicmore » graph matching for their implementation. The tool generates parallel code annotated with OpenMP pragmas for x86 Shared-memory Multiprocessors (SMPs). With respect to commercial database systems such as Virtuoso, our approach reduces memory occupation due to join operations and provides higher performance. We show the scaling of the automatically generated graph-matching code on a 48-core SMP.« less

Discovery of Influenza A Virus Sequence Pairs and Their Combinations for Simultaneous Heterosubtypic Targeting that Hedge against Antiviral Resistance

PubMed Central

Lin, Jing; Pramono, Zacharias Aloysius Dwi; Maurer-Stroh, Sebastian

2016-01-01

The multiple circulating human influenza A virus subtypes coupled with the perpetual genomic mutations and segment reassortment events challenge the development of effective therapeutics. The capacity to drug most RNAs motivates the investigation on viral RNA targets. 123,060 segment sequences from 35,938 strains of the most prevalent subtypes also infecting humans–H1N1, 2009 pandemic H1N1, H3N2, H5N1 and H7N9, were used to identify 1,183 conserved RNA target sequences (≥15-mer) in the internal segments. 100% theoretical coverage in simultaneous heterosubtypic targeting is achieved by pairing specific sequences from the same segment (“Duals”) or from two segments (“Doubles”); 1,662 Duals and 28,463 Doubles identified. By combining specific Duals and/or Doubles to form a target graph wherein an edge connecting two vertices (target sequences) represents a Dual or Double, it is possible to hedge against antiviral resistance besides maintaining 100% heterosubtypic coverage. To evaluate the hedging potential, we define the hedge-factor as the minimum number of resistant target sequences that will render the graph to become resistant i.e. eliminate all the edges therein; a target sequence or a graph is considered resistant when it cannot achieve 100% heterosubtypic coverage. In an n-vertices graph (n ≥ 3), the hedge-factor is maximal (= n– 1) when it is a complete graph i.e. every distinct pair in a graph is either a Dual or Double. Computational analyses uncover an extensive number of complete graphs of different sizes. Monte Carlo simulations show that the mutation counts and time elapsed for a target graph to become resistant increase with the hedge-factor. Incidentally, target sequences which were reported to reduce virus titre in experiments are included in our target graphs. The identity of target sequence pairs for heterosubtypic targeting and their combinations for hedging antiviral resistance are useful toolkits to construct target graphs for different therapeutic objectives. PMID:26771381

Supporting Generative Thinking about Number Lines, the Cartesian Plane, and Graphs of Linear Functions

ERIC Educational Resources Information Center

Earnest, Darrell Steven

2012-01-01

This dissertation explores fifth and eighth grade students' interpretations of three kinds of mathematical representations: number lines, the Cartesian plane, and graphs of linear functions. Two studies were conducted. In Study 1, I administered the paper-and-pencil Linear Representations Assessment (LRA) to examine students'…

An Evaluation of the Conservative Dual-Criterion Method for Teaching University Students to Visually Inspect AB-Design Graphs

ERIC Educational Resources Information Center

Stewart, Kelise K.; Carr, James E.; Brandt, Charles W.; McHenry, Meade M.

2007-01-01

The present study evaluated the effects of both a traditional lecture and the conservative dual-criterion (CDC) judgment aid on the ability of 6 university students to visually inspect AB-design line graphs. The traditional lecture reliably failed to improve visual inspection accuracy, whereas the CDC method substantially improved the performance…

Graph characterization via Ihara coefficients.

PubMed

Ren, Peng; Wilson, Richard C; Hancock, Edwin R

2011-02-01

The novel contributions of this paper are twofold. First, we demonstrate how to characterize unweighted graphs in a permutation-invariant manner using the polynomial coefficients from the Ihara zeta function, i.e., the Ihara coefficients. Second, we generalize the definition of the Ihara coefficients to edge-weighted graphs. For an unweighted graph, the Ihara zeta function is the reciprocal of a quasi characteristic polynomial of the adjacency matrix of the associated oriented line graph. Since the Ihara zeta function has poles that give rise to infinities, the most convenient numerically stable representation is to work with the coefficients of the quasi characteristic polynomial. Moreover, the polynomial coefficients are invariant to vertex order permutations and also convey information concerning the cycle structure of the graph. To generalize the representation to edge-weighted graphs, we make use of the reduced Bartholdi zeta function. We prove that the computation of the Ihara coefficients for unweighted graphs is a special case of our proposed method for unit edge weights. We also present a spectral analysis of the Ihara coefficients and indicate their advantages over other graph spectral methods. We apply the proposed graph characterization method to capturing graph-class structure and clustering graphs. Experimental results reveal that the Ihara coefficients are more effective than methods based on Laplacian spectra.

Knowledge Representation Issues in Semantic Graphs for Relationship Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barthelemy, M; Chow, E; Eliassi-Rad, T

2005-02-02

An important task for Homeland Security is the prediction of threat vulnerabilities, such as through the detection of relationships between seemingly disjoint entities. A structure used for this task is a ''semantic graph'', also known as a ''relational data graph'' or an ''attributed relational graph''. These graphs encode relationships as typed links between a pair of typed nodes. Indeed, semantic graphs are very similar to semantic networks used in AI. The node and link types are related through an ontology graph (also known as a schema). Furthermore, each node has a set of attributes associated with it (e.g., ''age'' maymore » be an attribute of a node of type ''person''). Unfortunately, the selection of types and attributes for both nodes and links depends on human expertise and is somewhat subjective and even arbitrary. This subjectiveness introduces biases into any algorithm that operates on semantic graphs. Here, we raise some knowledge representation issues for semantic graphs and provide some possible solutions using recently developed ideas in the field of complex networks. In particular, we use the concept of transitivity to evaluate the relevance of individual links in the semantic graph for detecting relationships. We also propose new statistical measures for semantic graphs and illustrate these semantic measures on graphs constructed from movies and terrorism data.« less

An image understanding system using attributed symbolic representation and inexact graph-matching

NASA Astrophysics Data System (ADS)

Eshera, M. A.; Fu, K.-S.

1986-09-01

A powerful image understanding system using a semantic-syntactic representation scheme consisting of attributed relational graphs (ARGs) is proposed for the analysis of the global information content of images. A multilayer graph transducer scheme performs the extraction of ARG representations from images, with ARG nodes representing the global image features, and the relations between features represented by the attributed branches between corresponding nodes. An efficient dynamic programming technique is employed to derive the distance between two ARGs and the inexact matching of their respective components. Noise, distortion and ambiguity in real-world images are handled through modeling in the transducer mapping rules and through the appropriate cost of error-transformation for the inexact matching of the representation. The system is demonstrated for the case of locating objects in a scene composed of complex overlapped objects, and the case of target detection in noisy and distorted synthetic aperture radar image.

Resilience of Self-Organised and Top-Down Planned Cities—A Case Study on London and Beijing Street Networks

PubMed Central

Wang, Jiaqiu

2015-01-01

The success or failure of the street network depends on its reliability. In this article, using resilience analysis, the author studies how the shape and appearance of street networks in self-organised and top-down planned cities influences urban transport. Considering London and Beijing as proxies for self-organised and top-down planned cities, the structural properties of London and Beijing networks first are investigated based on their primal and dual representations of planar graphs. The robustness of street networks then is evaluated in primal space and dual space by deactivating road links under random and intentional attack scenarios. The results show that the reliability of London street network differs from that of Beijing, which seems to rely more on its architecture and connectivity. It is found that top-down planned Beijing with its higher average degree in the dual space and assortativity in the primal space is more robust than self-organised London using the measures of maximum and second largest cluster size and network efficiency. The article offers an insight, from a network perspective, into the reliability of street patterns in self-organised and top-down planned city systems. PMID:26682551

Challenges in Designing Appropriate Scaffolding to Improve Students' Representational Consistency: The Case of a Gauss's Law Problem

ERIC Educational Resources Information Center

Maries, Alexandru; Lin, Shih-Yin; Singh, Chandralekha

2017-01-01

Prior research suggests that introductory physics students have difficulty with graphing and interpreting graphs. Here, we discuss an investigation of student difficulties in translating between mathematical and graphical representations for a problem in electrostatics and the effect of increasing levels of scaffolding on students'…

Graph-Theoretic Representations for Proximity Matrices through Strongly-Anti-Robinson or Circular Strongly-Anti-Robinson Matrices.

ERIC Educational Resources Information Center

Hubert, Lawrence; Arabie, Phipps; Meulman, Jacqueline

1998-01-01

Introduces a method for fitting order-constrained matrices that satisfy the strongly anti-Robinson restrictions (SAR). The method permits a representation of the fitted values in a (least-squares) SAR approximating matrix as lengths of paths in a graph. The approach is illustrated with a published proximity matrix. (SLD)

Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering.

PubMed

Peng, Xi; Yu, Zhiding; Yi, Zhang; Tang, Huajin

2017-04-01

Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i.e., intrasubspace data points). Recent works achieve good performance by modeling errors into their objective functions to remove the errors from the inputs. However, these approaches face the limitations that the structure of errors should be known prior and a complex convex problem must be solved. In this paper, we present a novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space. We first prove that l 1 -, l 2 -, l ∞ -, and nuclear-norm-based linear projection spaces share the property of intrasubspace projection dominance, i.e., the coefficients over intrasubspace data points are larger than those over intersubspace data points. Based on this property, we introduce a method to construct a sparse similarity graph, called L2-graph. The subspace clustering and subspace learning algorithms are developed upon L2-graph. We conduct comprehensive experiment on subspace learning, image clustering, and motion segmentation and consider several quantitative benchmarks classification/clustering accuracy, normalized mutual information, and running time. Results show that L2-graph outperforms many state-of-the-art methods in our experiments, including L1-graph, low rank representation (LRR), and latent LRR, least square regression, sparse subspace clustering, and locally linear representation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Demeure, I.M.

The research presented here is concerned with representation techniques and tools to support the design, prototyping, simulation, and evaluation of message-based parallel, distributed computations. The author describes ParaDiGM-Parallel, Distributed computation Graph Model-a visual representation technique for parallel, message-based distributed computations. ParaDiGM provides several views of a computation depending on the aspect of concern. It is made of two complementary submodels, the DCPG-Distributed Computing Precedence Graph-model, and the PAM-Process Architecture Model-model. DCPGs are precedence graphs used to express the functionality of a computation in terms of tasks, message-passing, and data. PAM graphs are used to represent the partitioning of a computationmore » into schedulable units or processes, and the pattern of communication among those units. There is a natural mapping between the two models. He illustrates the utility of ParaDiGM as a representation technique by applying it to various computations (e.g., an adaptive global optimization algorithm, the client-server model). ParaDiGM representations are concise. They can be used in documenting the design and the implementation of parallel, distributed computations, in describing such computations to colleagues, and in comparing and contrasting various implementations of the same computation. He then describes VISA-VISual Assistant, a software tool to support the design, prototyping, and simulation of message-based parallel, distributed computations. VISA is based on the ParaDiGM model. In particular, it supports the editing of ParaDiGM graphs to describe the computations of interest, and the animation of these graphs to provide visual feedback during simulations. The graphs are supplemented with various attributes, simulation parameters, and interpretations which are procedures that can be executed by VISA.« less

Expanding our understanding of students' use of graphs for learning physics

NASA Astrophysics Data System (ADS)

Laverty, James T.

It is generally agreed that the ability to visualize functional dependencies or physical relationships as graphs is an important step in modeling and learning. However, several studies in Physics Education Research (PER) have shown that many students in fact do not master this form of representation and even have misconceptions about the meaning of graphs that impede learning physics concepts. Working with graphs in classroom settings has been shown to improve student abilities with graphs, particularly when the students can interact with them. We introduce a novel problem type in an online homework system, which requires students to construct the graphs themselves in free form, and requires no hand-grading by instructors. A study of pre/post-test data using the Test of Understanding Graphs in Kinematics (TUG-K) over several semesters indicates that students learn significantly more from these graph construction problems than from the usual graph interpretation problems, and that graph interpretation alone may not have any significant effect. The interpretation of graphs, as well as the representation translation between textual, mathematical, and graphical representations of physics scenarios, are frequently listed among the higher order thinking skills we wish to convey in an undergraduate course. But to what degree do we succeed? Do students indeed employ higher order thinking skills when working through graphing exercises? We investigate students working through a variety of graph problems, and, using a think-aloud protocol, aim to reconstruct the cognitive processes that the students go through. We find that to a certain degree, these problems become commoditized and do not trigger the desired higher order thinking processes; simply translating ``textbook-like'' problems into the graphical realm will not achieve any additional educational goals. Whether the students have to interpret or construct a graph makes very little difference in the methods used by the students. We will also look at the results of using graph problems in an online learning environment. We will show evidence that construction problems lead to a higher degree of difficulty and degree of discrimination than other graph problems and discuss the influence the course has on these variables.

Beyond Slopes and Points: Teaching Students How Graphs Describe the Relationships between Scientific Pheomena

ERIC Educational Resources Information Center

Harris, David; Gomez Zwiep, Susan

2013-01-01

Graphs represent complex information. They show relationships and help students see patterns and compare data. Students often do not appreciate the illuminating power of graphs, interpreting them literally rather than as symbolic representations (Leinhardt, Zaslavsky, and Stein 1990). Students often read graphs point by point instead of seeing…

Supporting Fourth Graders' Ability to Interpret Graphs through Real-Time Graphing Technology: A Preliminary Study

ERIC Educational Resources Information Center

Deniz, Hasan; Dulger, Mehmet F.

2012-01-01

This study examined to what extent inquiry-based instruction supported with real-time graphing technology improves fourth grader's ability to interpret graphs as representations of physical science concepts such as motion and temperature. This study also examined whether there is any difference between inquiry-based instruction supported with…

Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning.

PubMed

Peng, Yong; Lu, Bao-Liang; Wang, Suhang

2015-05-01

Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labeled and unlabeled samples, where the edge weights are calculated based on the LRR coefficients. However, most of existing LRR related approaches fail to consider the geometrical structure of data, which has been shown beneficial for discriminative tasks. In this paper, we propose an enhanced LRR via sparse manifold adaption, termed manifold low-rank representation (MLRR), to learn low-rank data representation. MLRR can explicitly take the data local manifold structure into consideration, which can be identified by the geometric sparsity idea; specifically, the local tangent space of each data point was sought by solving a sparse representation objective. Therefore, the graph to depict the relationship of data points can be built once the manifold information is obtained. We incorporate a regularizer into LRR to make the learned coefficients preserve the geometric constraints revealed in the data space. As a result, MLRR combines both the global information emphasized by low-rank property and the local information emphasized by the identified manifold structure. Extensive experimental results on semi-supervised classification tasks demonstrate that MLRR is an excellent method in comparison with several state-of-the-art graph construction approaches. Copyright © 2015 Elsevier Ltd. All rights reserved.

Why Representations?

ERIC Educational Resources Information Center

Schultz, James E.; Waters, Michael S.

2000-01-01

Discusses representations in the context of solving a system of linear equations. Views representations (concrete, tables, graphs, algebraic, matrices) from perspectives of understanding, technology, generalization, exact versus approximate solution, and learning style. (KHR)

Constructing Graphical Representations: Middle Schoolers' Intuitions and Developing Knowledge about Slope and Y-Intercept

ERIC Educational Resources Information Center

Hattikudur, Shanta; Prather, Richard W.; Asquith, Pamela; Alibali, Martha W.; Knuth, Eric J.; Nathan, Mitchell

2012-01-01

Middle-school students are expected to understand key components of graphs, such as slope and y-intercept. However, constructing graphs is a skill that has received relatively little research attention. This study examined students' construction of graphs of linear functions, focusing specifically on the relative difficulties of graphing slope and…

Interpretations of Graphs by University Biology Students and Practicing Scientists: Toward a Social Practice View of Scientific Representation Practices.

ERIC Educational Resources Information Center

Bowen, G. Michael; Roth, Wolff-Michael; McGinn, Michelle K.

1999-01-01

Describes a study of the similarities and differences in graph-related interpretations between scientists and college students engaged in collective graph interpretation. Concludes that while many students learned to provide correct answers to scientific graphing questions, they did not come to make linguistic distinctions or increase their…

Representations of mechanical assembly sequences

NASA Technical Reports Server (NTRS)

Homem De Mello, Luiz S.; Sanderson, Arthur C.

1991-01-01

Five types of representations for assembly sequences are reviewed: the directed graph of feasible assembly sequences, the AND/OR graph of feasible assembly sequences, the set of establishment conditions, and two types of sets of precedence relationships. (precedence relationships between the establishment of one connection between parts and the establishment of another connection, and precedence relationships between the establishment of one connection and states of the assembly process). The mappings of one representation into the others are established. The correctness and completeness of these representations are established. The results presented are needed in the proof of correctness and completeness of algorithms for the generation of mechanical assembly sequences.

Deep graphs-A general framework to represent and analyze heterogeneous complex systems across scales.

PubMed

Traxl, Dominik; Boers, Niklas; Kurths, Jürgen

2016-06-01

Network theory has proven to be a powerful tool in describing and analyzing systems by modelling the relations between their constituent objects. Particularly in recent years, a great progress has been made by augmenting "traditional" network theory in order to account for the multiplex nature of many networks, multiple types of connections between objects, the time-evolution of networks, networks of networks and other intricacies. However, existing network representations still lack crucial features in order to serve as a general data analysis tool. These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales. In this paper, we introduce a collection of definitions resulting in a framework that, on the one hand, entails and unifies existing network representations (e.g., network of networks and multilayer networks), and on the other hand, generalizes and extends them by incorporating the above features. To implement these features, we first specify the nodes and edges of a finite graph as sets of properties (which are permitted to be arbitrary mathematical objects). Second, the mathematical concept of partition lattices is transferred to the network theory in order to demonstrate how partitioning the node and edge set of a graph into supernodes and superedges allows us to aggregate, compute, and allocate information on and between arbitrary groups of nodes. The derived partition lattice of a graph, which we denote by deep graph, constitutes a concise, yet comprehensive representation that enables the expression and analysis of heterogeneous properties, relations, and interactions on all scales of a complex system in a self-contained manner. Furthermore, to be able to utilize existing network-based methods and models, we derive different representations of multilayer networks from our framework and demonstrate the advantages of our representation. On the basis of the formal framework described here, we provide a rich, fully scalable (and self-explanatory) software package that integrates into the PyData ecosystem and offers interfaces to popular network packages, making it a powerful, general-purpose data analysis toolkit. We exemplify an application of deep graphs using a real world dataset, comprising 16 years of satellite-derived global precipitation measurements. We deduce a deep graph representation of these measurements in order to track and investigate local formations of spatio-temporal clusters of extreme precipitation events.

Couple Graph Based Label Propagation Method for Hyperspectral Remote Sensing Data Classification

NASA Astrophysics Data System (ADS)

Wang, X. P.; Hu, Y.; Chen, J.

2018-04-01

Graph based semi-supervised classification method are widely used for hyperspectral image classification. We present a couple graph based label propagation method, which contains both the adjacency graph and the similar graph. We propose to construct the similar graph by using the similar probability, which utilize the label similarity among examples probably. The adjacency graph was utilized by a common manifold learning method, which has effective improve the classification accuracy of hyperspectral data. The experiments indicate that the couple graph Laplacian which unite both the adjacency graph and the similar graph, produce superior classification results than other manifold Learning based graph Laplacian and Sparse representation based graph Laplacian in label propagation framework.

Holographic hierarchy in the Gaussian matrix model via the fuzzy sphere

NASA Astrophysics Data System (ADS)

Garner, David; Ramgoolam, Sanjaye

2013-10-01

The Gaussian Hermitian matrix model was recently proposed to have a dual string description with worldsheets mapping to a sphere target space. The correlators were written as sums over holomorphic (Belyi) maps from worldsheets to the two-dimensional sphere, branched over three points. We express the matrix model correlators by using the fuzzy sphere construction of matrix algebras, which can be interpreted as a string field theory description of the Belyi strings. This gives the correlators in terms of trivalent ribbon graphs that represent the couplings of irreducible representations of su(2), which can be evaluated in terms of 3j and 6j symbols. The Gaussian model perturbed by a cubic potential is then recognised as a generating function for Ponzano-Regge partition functions for 3-manifolds having the worldsheet as boundary, and equipped with boundary data determined by the ribbon graphs. This can be viewed as a holographic extension of the Belyi string worldsheets to membrane worldvolumes, forming part of a holographic hierarchy linking, via the large N expansion, the zero-dimensional QFT of the Matrix model to 2D strings and 3D membranes. Note that if, after removing the white vertices, the graph contains a blue edge connecting to the same black vertex at both ends, then the triangulation generated from the black edges will contain faces that resemble cut discs. These faces are triangles with two of the edges identified.

Graph representation of hepatic vessel based on centerline extraction and junction detection

NASA Astrophysics Data System (ADS)

Zhang, Xing; Tian, Jie; Deng, Kexin; Li, Xiuli; Yang, Fei

2012-02-01

In the area of computer-aided diagnosis (CAD), segmentation and analysis of hepatic vessel is a prerequisite for hepatic diseases diagnosis and surgery planning. For liver surgery planning, it is crucial to provide the surgeon with a patient-individual three-dimensional representation of the liver along with its vasculature and lesions. The representation allows an exploration of the vascular anatomy and the measurement of vessel diameters, following by intra-patient registration, as well as the analysis of the shape and volume of vascular territories. In this paper, we present an approach for generation of hepatic vessel graph based on centerline extraction and junction detection. The proposed approach involves the following concepts and methods: 1) Flux driven automatic centerline extraction; 2) Junction detection on the centerline using hollow sphere filtering; 3) Graph representation of hepatic vessel based on the centerline and junction. The approach is evaluated on contrast-enhanced liver CT datasets to demonstrate its availability and effectiveness.

Examining the Development of Students' Covariational Reasoning in the Context of Graphing

ERIC Educational Resources Information Center

Frank, Kristin Marianna

2017-01-01

Researchers have documented the importance of seeing a graph as an emergent trace of how two quantities' values vary simultaneously in order to reason about the graph in terms of quantitative relationships. If a student does not see a graph as a representation of how quantities change together then the student is limited to reasoning about…

Discriminative graph embedding for label propagation.

PubMed

Nguyen, Canh Hao; Mamitsuka, Hiroshi

2011-09-01

In many applications, the available information is encoded in graph structures. This is a common problem in biological networks, social networks, web communities and document citations. We investigate the problem of classifying nodes' labels on a similarity graph given only a graph structure on the nodes. Conventional machine learning methods usually require data to reside in some Euclidean spaces or to have a kernel representation. Applying these methods to nodes on graphs would require embedding the graphs into these spaces. By embedding and then learning the nodes on graphs, most methods are either flexible with different learning objectives or efficient enough for large scale applications. We propose a method to embed a graph into a feature space for a discriminative purpose. Our idea is to include label information into the embedding process, making the space representation tailored to the task. We design embedding objective functions that the following learning formulations become spectral transforms. We then reformulate these spectral transforms into multiple kernel learning problems. Our method, while being tailored to the discriminative tasks, is efficient and can scale to massive data sets. We show the need of discriminative embedding on some simulations. Applying to biological network problems, our method is shown to outperform baselines.

Non-isolated Resolving Sets of certain Graphs Cartesian Product with a Path

NASA Astrophysics Data System (ADS)

Hasibuan, I. M.; Salman, A. N. M.; Saputro, S. W.

2018-04-01

Let G be a connected, simple, and finite graph. For an ordered subset W = {w 1 , w 2 , · · ·, wk } of vertices in a graph G and a vertex v of G, the metric representation of v with respect to W is the k-vector r(v|W ) = (d(v, w 1), d(v, w 2), · · ·, d(v, wk )). The set W is called a resolving set for G if every vertex of G has a distinct representation. The minimum cardinality of W is called the metric dimension of G, denoted by dim(G). If the induced subgraph < W> has no isolated vertices, then W is called a non-isolated resolving set. The minimum cardinality of non-isolated resolving set of G is called the non-isolated resolving number of G, denoted by nr(G). In this paper, we consider H\\square {P}n that is a graph obtained from Cartesian product between a connected graph H and a path Pn . We determine nr(H\\square {P}n), for some classes of H, including cycles, complete graphs, complete bipartite graphs, and friendship graphs.

Reflecting on Graphs: Attributes of Graph Choice and Construction Practices in Biology

ERIC Educational Resources Information Center

Angra, Aakanksha; Gardner, Stephanie M.

2017-01-01

Undergraduate biology education reform aims to engage students in scientific practices such as experimental design, experimentation, and data analysis and communication. Graphs are ubiquitous in the biological sciences, and creating effective graphical representations involves quantitative and disciplinary concepts and skills. Past studies…

Classification of forensic autopsy reports through conceptual graph-based document representation model.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2018-06-01

Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation, a clinical report is transformed into a format that is suitable for classification. The traditional document representation technique for text categorization is the bag-of-words (BoW) technique. In this study, the traditional BoW technique is ineffective in classifying forensic autopsy reports because it merely extracts frequent but discriminative features from clinical reports. Moreover, this technique fails to capture word inversion, as well as word-level synonymy and polysemy, when classifying autopsy reports. Hence, the BoW technique suffers from low accuracy and low robustness unless it is improved with contextual and application-specific information. To overcome the aforementioned limitations of the BoW technique, this research aims to develop an effective conceptual graph-based document representation (CGDR) technique to classify 1500 forensic autopsy reports from four (4) manners of death (MoD) and sixteen (16) causes of death (CoD). Term-based and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) based conceptual features were extracted and represented through graphs. These features were then used to train a two-level text classifier. The first level classifier was responsible for predicting MoD. In addition, the second level classifier was responsible for predicting CoD using the proposed conceptual graph-based document representation technique. To demonstrate the significance of the proposed technique, its results were compared with those of six (6) state-of-the-art document representation techniques. Lastly, this study compared the effects of one-level classification and two-level classification on the experimental results. The experimental results indicated that the CGDR technique achieved 12% to 15% improvement in accuracy compared with fully automated document representation baseline techniques. Moreover, two-level classification obtained better results compared with one-level classification. The promising results of the proposed conceptual graph-based document representation technique suggest that pathologists can adopt the proposed system as their basis for second opinion, thereby supporting them in effectively determining CoD. Copyright © 2018 Elsevier Inc. All rights reserved.

Framework for Querying and Analysis of Evolving Graphs

ERIC Educational Resources Information Center

Moffitt, Vera Zaychik

2017-01-01

Graph representations underlie many modern computer applications, capturing the structure of such diverse networks as the Internet, personal associations, roads, sensors, and metabolic pathways. While the static structure of graphs is a well-explored field, a new emphasis is being placed on understanding and representing the way these networks…

Multiclass Data Segmentation using Diffuse Interface Methods on Graphs

DTIC Science & Technology

2014-01-01

37] that performs interac- tive image segmentation using the solution to a combinatorial Dirichlet problem. Elmoataz et al . have developed general...izations of the graph Laplacian [25] for image denoising and manifold smoothing. Couprie et al . in [18] define a conve- niently parameterized graph...continuous setting carry over to the discrete graph representation. For general data segmentation, Bresson et al . in [8], present rigorous convergence

Students' Representational Fluency at University: A Cross-Sectional Measure of How Multiple Representations Are Used by Physics Students Using the Representational Fluency Survey

ERIC Educational Resources Information Center

Hill, Matthew; Sharma, Manjula Devi

2015-01-01

To succeed within scientific disciplines, using representations, including those based on words, graphs, equations, and diagrams, is important. Research indicates that the use of discipline specific representations (sometimes referred to as expert generated representations), as well as multi-representational use, is critical for problem solving…

Orbit-product representation and correction of Gaussian belief propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Jason K; Chertkov, Michael; Chernyak, Vladimir

We present a new interpretation of Gaussian belief propagation (GaBP) based on the 'zeta function' representation of the determinant as a product over orbits of a graph. We show that GaBP captures back-tracking orbits of the graph and consider how to correct this estimate by accounting for non-backtracking orbits. We show that the product over non-backtracking orbits may be interpreted as the determinant of the non-backtracking adjacency matrix of the graph with edge weights based on the solution of GaBP. An efficient method is proposed to compute a truncated correction factor including all non-backtracking orbits up to a specified length.

Learning of Multimodal Representations With Random Walks on the Click Graph.

PubMed

Wu, Fei; Lu, Xinyan; Song, Jun; Yan, Shuicheng; Zhang, Zhongfei Mark; Rui, Yong; Zhuang, Yueting

2016-02-01

In multimedia information retrieval, most classic approaches tend to represent different modalities of media in the same feature space. With the click data collected from the users' searching behavior, existing approaches take either one-to-one paired data (text-image pairs) or ranking examples (text-query-image and/or image-query-text ranking lists) as training examples, which do not make full use of the click data, particularly the implicit connections among the data objects. In this paper, we treat the click data as a large click graph, in which vertices are images/text queries and edges indicate the clicks between an image and a query. We consider learning a multimodal representation from the perspective of encoding the explicit/implicit relevance relationship between the vertices in the click graph. By minimizing both the truncated random walk loss as well as the distance between the learned representation of vertices and their corresponding deep neural network output, the proposed model which is named multimodal random walk neural network (MRW-NN) can be applied to not only learn robust representation of the existing multimodal data in the click graph, but also deal with the unseen queries and images to support cross-modal retrieval. We evaluate the latent representation learned by MRW-NN on a public large-scale click log data set Clickture and further show that MRW-NN achieves much better cross-modal retrieval performance on the unseen queries/images than the other state-of-the-art methods.

Exact and approximate graph matching using random walks.

PubMed

Gori, Marco; Maggini, Marco; Sarti, Lorenzo

2005-07-01

In this paper, we propose a general framework for graph matching which is suitable for different problems of pattern recognition. The pattern representation we assume is at the same time highly structured, like for classic syntactic and structural approaches, and of subsymbolic nature with real-valued features, like for connectionist and statistic approaches. We show that random walk based models, inspired by Google's PageRank, give rise to a spectral theory that nicely enhances the graph topological features at node level. As a straightforward consequence, we derive a polynomial algorithm for the classic graph isomorphism problem, under the restriction of dealing with Markovian spectrally distinguishable graphs (MSD), a class of graphs that does not seem to be easily reducible to others proposed in the literature. The experimental results that we found on different test-beds of the TC-15 graph database show that the defined MSD class "almost always" covers the database, and that the proposed algorithm is significantly more efficient than top scoring VF algorithm on the same data. Most interestingly, the proposed approach is very well-suited for dealing with partial and approximate graph matching problems, derived for instance from image retrieval tasks. We consider the objects of the COIL-100 visual collection and provide a graph-based representation, whose node's labels contain appropriate visual features. We show that the adoption of classic bipartite graph matching algorithms offers a straightforward generalization of the algorithm given for graph isomorphism and, finally, we report very promising experimental results on the COIL-100 visual collection.

Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate.

PubMed

Song, Qi; Wu, Xiaodong; Liu, Yunlong; Smith, Mark; Buatti, John; Sonka, Milan

2009-01-01

We present a novel method for globally optimal surface segmentation of multiple mutually interacting objects, incorporating both edge and shape knowledge in a 3-D graph-theoretic approach. Hard surface interacting constraints are enforced in the interacting regions, preserving the geometric relationship of those partially interacting surfaces. The soft smoothness a priori shape compliance is introduced into the energy functional to provide shape guidance. The globally optimal surfaces can be simultaneously achieved by solving a maximum flow problem based on an arc-weighted graph representation. Representing the segmentation problem in an arc-weighted graph, one can incorporate a wider spectrum of constraints into the formulation, thus increasing segmentation accuracy and robustness in volumetric image data. To the best of our knowledge, our method is the first attempt to introduce the arc-weighted graph representation into the graph-searching approach for simultaneous segmentation of multiple partially interacting objects, which admits a globally optimal solution in a low-order polynomial time. Our new approach was applied to the simultaneous surface detection of bladder and prostate. The result was quite encouraging in spite of the low saliency of the bladder and prostate in CT images.

Developing Data Graph Comprehension. Third Edition

ERIC Educational Resources Information Center

Curcio, Frances

2010-01-01

Since the dawn of civilization, pictorial representations and symbols have been used to communicate simple statistics. Efficient and effective, they are still used today in the form of pictures and graphs to record and present data. Who can tie their shoes? How many calories are in your favorite food? Make data and graphs relevant and interesting…

Searches over graphs representing geospatial-temporal remote sensing data

DOEpatents

Brost, Randolph; Perkins, David Nikolaus

2018-03-06

Various technologies pertaining to identifying objects of interest in remote sensing images by searching over geospatial-temporal graph representations are described herein. Graphs are constructed by representing objects in remote sensing images as nodes, and connecting nodes with undirected edges representing either distance or adjacency relationships between objects and directed edges representing changes in time. Geospatial-temporal graph searches are made computationally efficient by taking advantage of characteristics of geospatial-temporal data in remote sensing images through the application of various graph search techniques.

Multiclass Data Segmentation Using Diffuse Interface Methods on Graphs

DTIC Science & Technology

2014-01-01

interac- tive image segmentation using the solution to a combinatorial Dirichlet problem. Elmoataz et al . have developed general- izations of the graph...Laplacian [25] for image denoising and manifold smoothing. Couprie et al . in [18] define a conve- niently parameterized graph-based energy function that...over to the discrete graph representation. For general data segmentation, Bresson et al . in [8], present rigorous convergence results for two algorithms

Dynamic graph system for a semantic database

DOEpatents

Mizell, David

2016-04-12

A method and system in a computer system for dynamically providing a graphical representation of a data store of entries via a matrix interface is disclosed. A dynamic graph system provides a matrix interface that exposes to an application program a graphical representation of data stored in a data store such as a semantic database storing triples. To the application program, the matrix interface represents the graph as a sparse adjacency matrix that is stored in compressed form. Each entry of the data store is considered to represent a link between nodes of the graph. Each entry has a first field and a second field identifying the nodes connected by the link and a third field with a value for the link that connects the identified nodes. The first, second, and third fields represent the rows, column, and elements of the adjacency matrix.

Dynamic graph system for a semantic database

DOEpatents

Mizell, David

2015-01-27

A method and system in a computer system for dynamically providing a graphical representation of a data store of entries via a matrix interface is disclosed. A dynamic graph system provides a matrix interface that exposes to an application program a graphical representation of data stored in a data store such as a semantic database storing triples. To the application program, the matrix interface represents the graph as a sparse adjacency matrix that is stored in compressed form. Each entry of the data store is considered to represent a link between nodes of the graph. Each entry has a first field and a second field identifying the nodes connected by the link and a third field with a value for the link that connects the identified nodes. The first, second, and third fields represent the rows, column, and elements of the adjacency matrix.

Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning.

PubMed

Li, Bing; Yuan, Chunfeng; Xiong, Weihua; Hu, Weiming; Peng, Houwen; Ding, Xinmiao; Maybank, Steve

2017-12-01

In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (MIL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse -graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the MIL. Experiments and analyses in many practical applications prove the effectiveness of the M IL.

The Effects of Multiple Linked Representations on Student Learning in Mathematics.

ERIC Educational Resources Information Center

Ozgun-Koca, S. Asli

This study investigated the effects on student understanding of linear relationships using the linked representation software VideoPoint as compared to using semi-linked representation software. It investigated students' attitudes towards and preferences for mathematical representations--equations, tables, or graphs. An Algebra I class was divided…

A Robust Concurrent Approach for Road Extraction and Urbanization Monitoring Based on Superpixels Acquired from Spectral Remote Sensing Images

NASA Astrophysics Data System (ADS)

Seppke, Benjamin; Dreschler-Fischer, Leonie; Wilms, Christian

2016-08-01

The extraction of road signatures from remote sensing images as a promising indicator for urbanization is a classical segmentation problem. However, some segmentation algorithms often lead to non-sufficient results. One way to overcome this problem is the usage of superpixels, that represent a locally coherent cluster of connected pixels. Superpixels allow flexible, highly adaptive segmentation approaches due to the possibility of merging as well as splitting and form new basic image entities. On the other hand, superpixels require an appropriate representation containing all relevant information about topology and geometry to maximize their advantages.In this work, we present a combined geometric and topological representation based on a special graph representation, the so-called RS-graph. Moreover, we present the use of the RS-graph by means of a case study: the extraction of partially occluded road networks in rural areas from open source (spectral) remote sensing images by tracking. In addition, multiprocessing and GPU-based parallelization is used to speed up the construction of the representation and the application.

Deep graphs—A general framework to represent and analyze heterogeneous complex systems across scales

NASA Astrophysics Data System (ADS)

Traxl, Dominik; Boers, Niklas; Kurths, Jürgen

2016-06-01

Network theory has proven to be a powerful tool in describing and analyzing systems by modelling the relations between their constituent objects. Particularly in recent years, a great progress has been made by augmenting "traditional" network theory in order to account for the multiplex nature of many networks, multiple types of connections between objects, the time-evolution of networks, networks of networks and other intricacies. However, existing network representations still lack crucial features in order to serve as a general data analysis tool. These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales. In this paper, we introduce a collection of definitions resulting in a framework that, on the one hand, entails and unifies existing network representations (e.g., network of networks and multilayer networks), and on the other hand, generalizes and extends them by incorporating the above features. To implement these features, we first specify the nodes and edges of a finite graph as sets of properties (which are permitted to be arbitrary mathematical objects). Second, the mathematical concept of partition lattices is transferred to the network theory in order to demonstrate how partitioning the node and edge set of a graph into supernodes and superedges allows us to aggregate, compute, and allocate information on and between arbitrary groups of nodes. The derived partition lattice of a graph, which we denote by deep graph, constitutes a concise, yet comprehensive representation that enables the expression and analysis of heterogeneous properties, relations, and interactions on all scales of a complex system in a self-contained manner. Furthermore, to be able to utilize existing network-based methods and models, we derive different representations of multilayer networks from our framework and demonstrate the advantages of our representation. On the basis of the formal framework described here, we provide a rich, fully scalable (and self-explanatory) software package that integrates into the PyData ecosystem and offers interfaces to popular network packages, making it a powerful, general-purpose data analysis toolkit. We exemplify an application of deep graphs using a real world dataset, comprising 16 years of satellite-derived global precipitation measurements. We deduce a deep graph representation of these measurements in order to track and investigate local formations of spatio-temporal clusters of extreme precipitation events.

Single or Dual Representations for Reading and Spelling?

ERIC Educational Resources Information Center

Holmes, Virginia M.; Babauta, Mariko L.

2005-01-01

Neuropsychological models postulate that the memory representation acquired for use in reading words is separate from the one acquired for use in spelling, while developmental models assume that the same representation is developed for access in both reading and spelling. The dual-representation model contends that there is often more precise…

Conversion from Tree to Graph Representation of Requirements

NASA Technical Reports Server (NTRS)

Mayank, Vimal; Everett, David Frank; Shmunis, Natalya; Austin, Mark

2009-01-01

A procedure and software to implement the procedure have been devised to enable conversion from a tree representation to a graph representation of the requirements governing the development and design of an engineering system. The need for this procedure and software and for other requirements-management tools arises as follows: In systems-engineering circles, it is well known that requirements- management capability improves the likelihood of success in the team-based development of complex systems involving multiple technological disciplines. It is especially desirable to be able to visualize (in order to identify and manage) requirements early in the system- design process, when errors can be corrected most easily and inexpensively.

Extraction of Graph Information Based on Image Contents and the Use of Ontology

ERIC Educational Resources Information Center

Kanjanawattana, Sarunya; Kimura, Masaomi

2016-01-01

A graph is an effective form of data representation used to summarize complex information. Explicit information such as the relationship between the X- and Y-axes can be easily extracted from a graph by applying human intelligence. However, implicit knowledge such as information obtained from other related concepts in an ontology also resides in…

EarthVision 2000: Examining Students' Representations of Complex Data Sets.

ERIC Educational Resources Information Center

Vellom, R. Paul; Pape, Stephen J.

2000-01-01

Examines pencil-and-paper graphs produced by students at the beginning of a 1-week summer teacher/student institute as well as computer-based graphs produced by those same students at the end of the institute. Initial problems with managing data sets and producing meaningful graphs disappeared quickly as students used the process of "building…

Metric learning with spectral graph convolutions on brain connectivity networks.

PubMed

Ktena, Sofia Ira; Parisot, Sarah; Ferrante, Enzo; Rajchl, Martin; Lee, Matthew; Glocker, Ben; Rueckert, Daniel

2018-04-01

Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. This is mainly due to the capability of revealing patterns related to brain development and disease, which were previously unknown. Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non-trivial. Most existing methods fail to accommodate the graph structure, discarding information that could be beneficial for further classification or regression analyses based on these similarities. We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN) in a supervised setting. The proposed framework takes into consideration the graph structure for the evaluation of similarity between a pair of graphs, by employing spectral graph convolutions that allow the generalisation of traditional convolutions to irregular graphs and operates in the graph spectral domain. We apply the proposed model on two datasets: the challenging ABIDE database, which comprises functional MRI data of 403 patients with autism spectrum disorder (ASD) and 468 healthy controls aggregated from multiple acquisition sites, and a set of 2500 subjects from UK Biobank. We demonstrate the performance of the method for the tasks of classification between matching and non-matching graphs, as well as individual subject classification and manifold learning, showing that it leads to significantly improved results compared to traditional methods. Copyright © 2017 Elsevier Inc. All rights reserved.

Molecular graph convolutions: moving beyond fingerprints.

PubMed

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-08-01

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph-atoms, bonds, distances, etc.-which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

GraQL: A Query Language for High-Performance Attributed Graph Databases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chavarría-Miranda, Daniel; Castellana, Vito G.; Morari, Alessandro

Graph databases have gained increasing interest in the last few years due to the emergence of data sources which are not easily analyzable in traditional relational models or for which a graph data model is the natural representation. In order to understand the design and implementation choices for an attributed graph database backend and query language, we have started to design our infrastructure for attributed graph databases. In this paper, we describe the design considerations of our in-memory attributed graph database system with a particular focus on the data definition and query language components.

Characterizing Interaction with Visual Mathematical Representations

ERIC Educational Resources Information Center

Sedig, Kamran; Sumner, Mark

2006-01-01

This paper presents a characterization of computer-based interactions by which learners can explore and investigate visual mathematical representations (VMRs). VMRs (e.g., geometric structures, graphs, and diagrams) refer to graphical representations that visually encode properties and relationships of mathematical structures and concepts.…

Graph Structured Program Evolution: Evolution of Loop Structures

NASA Astrophysics Data System (ADS)

Shirakawa, Shinichi; Nagao, Tomoharu

Recently, numerous automatic programming techniques have been developed and applied in various fields. A typical example is genetic programming (GP), and various extensions and representations of GP have been proposed thus far. Complex programs and hand-written programs, however, may contain several loops and handle multiple data types. In this chapter, we propose a new method called Graph Structured Program Evolution (GRAPE). The representation of GRAPE is a graph structure; therefore, it can represent branches and loops using this structure. Each programis constructed as an arbitrary directed graph of nodes and a data set. The GRAPE program handles multiple data types using the data set for each type, and the genotype of GRAPE takes the form of a linear string of integers. We apply GRAPE to three test problems, factorial, exponentiation, and list sorting, and demonstrate that the optimum solution in each problem is obtained by the GRAPE system.

Fingerprint recognition system by use of graph matching

NASA Astrophysics Data System (ADS)

Shen, Wei; Shen, Jun; Zheng, Huicheng

2001-09-01

Fingerprint recognition is an important subject in biometrics to identify or verify persons by physiological characteristics, and has found wide applications in different domains. In the present paper, we present a finger recognition system that combines singular points and structures. The principal steps of processing in our system are: preprocessing and ridge segmentation, singular point extraction and selection, graph representation, and finger recognition by graphs matching. Our fingerprint recognition system is implemented and tested for many fingerprint images and the experimental result are satisfactory. Different techniques are used in our system, such as fast calculation of orientation field, local fuzzy dynamical thresholding, algebraic analysis of connections and fingerprints representation and matching by graphs. Wed find that for fingerprint database that is not very large, the recognition rate is very high even without using a prior coarse category classification. This system works well for both one-to-few and one-to-many problems.

Examining How Activity Shapes Students' Interactions While Creating Representations in Early Elementary Science

ERIC Educational Resources Information Center

Danish, Joshua Adam; Saleh, Asmalina

2014-01-01

It is common practice in elementary science classrooms to have students create representations, such as drawings, as a way of exploring new content. While numerous studies suggest the benefits of representation in science, the majority focus on specific, canonical representations, such as graphs. Few offer insight or guidance regarding how…

Apparatuses and Methods for Producing Runtime Architectures of Computer Program Modules

NASA Technical Reports Server (NTRS)

Abi-Antoun, Marwan Elia (Inventor); Aldrich, Jonathan Erik (Inventor)

2013-01-01

Apparatuses and methods for producing run-time architectures of computer program modules. One embodiment includes creating an abstract graph from the computer program module and from containment information corresponding to the computer program module, wherein the abstract graph has nodes including types and objects, and wherein the abstract graph relates an object to a type, and wherein for a specific object the abstract graph relates the specific object to a type containing the specific object; and creating a runtime graph from the abstract graph, wherein the runtime graph is a representation of the true runtime object graph, wherein the runtime graph represents containment information such that, for a specific object, the runtime graph relates the specific object to another object that contains the specific object.

Group-sparse representation with dictionary learning for medical image denoising and fusion.

PubMed

Li, Shutao; Yin, Haitao; Fang, Leyuan

2012-12-01

Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

Representation of activity in images using geospatial temporal graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brost, Randolph; McLendon, III, William C.; Parekh, Ojas D.

Various technologies pertaining to modeling patterns of activity observed in remote sensing images using geospatial-temporal graphs are described herein. Graphs are constructed by representing objects in remote sensing images as nodes, and connecting nodes with undirected edges representing either distance or adjacency relationships between objects and directed edges representing changes in time. Activity patterns may be discerned from the graphs by coding nodes representing persistent objects like buildings differently from nodes representing ephemeral objects like vehicles, and examining the geospatial-temporal relationships of ephemeral nodes within the graph.

Reliability models for dataflow computer systems

NASA Technical Reports Server (NTRS)

Kavi, K. M.; Buckles, B. P.

1985-01-01

The demands for concurrent operation within a computer system and the representation of parallelism in programming languages have yielded a new form of program representation known as data flow (DENN 74, DENN 75, TREL 82a). A new model based on data flow principles for parallel computations and parallel computer systems is presented. Necessary conditions for liveness and deadlock freeness in data flow graphs are derived. The data flow graph is used as a model to represent asynchronous concurrent computer architectures including data flow computers.

Semantic graphs and associative memories

NASA Astrophysics Data System (ADS)

Pomi, Andrés; Mizraji, Eduardo

2004-12-01

Graphs have been increasingly utilized in the characterization of complex networks from diverse origins, including different kinds of semantic networks. Human memories are associative and are known to support complex semantic nets; these nets are represented by graphs. However, it is not known how the brain can sustain these semantic graphs. The vision of cognitive brain activities, shown by modern functional imaging techniques, assigns renewed value to classical distributed associative memory models. Here we show that these neural network models, also known as correlation matrix memories, naturally support a graph representation of the stored semantic structure. We demonstrate that the adjacency matrix of this graph of associations is just the memory coded with the standard basis of the concept vector space, and that the spectrum of the graph is a code invariant of the memory. As long as the assumptions of the model remain valid this result provides a practical method to predict and modify the evolution of the cognitive dynamics. Also, it could provide us with a way to comprehend how individual brains that map the external reality, almost surely with different particular vector representations, are nevertheless able to communicate and share a common knowledge of the world. We finish presenting adaptive association graphs, an extension of the model that makes use of the tensor product, which provides a solution to the known problem of branching in semantic nets.

Inventing Graphing: Meta-Representational Expertise in Children.

ERIC Educational Resources Information Center

DiSessa, Andra A.; And Others

1991-01-01

A cooperative activity involving eight sixth grade students over five days focused on inventing static representations of motion. In generating, critiquing and refining numerous representations, strong metarepresentational competence was found. An intricate blend of the children's conceptual and interactional skills, their interest in and sense of…

Multiple Representations and Connections with the Sierpinski Triangle

ERIC Educational Resources Information Center

Kirwan, J. Vince; Tobias, Jennifer M.

2014-01-01

To understand multiple representations in algebra, students must be able to describe relationships through a variety of formats, such as graphs, tables, pictures, and equations. NCTM indicates that varied representations are "essential elements in supporting students' understanding of mathematical concepts and relationships" (NCTM…

Representations and Rafts

ERIC Educational Resources Information Center

Hartweg, Kimberly Sipes

2011-01-01

To build on prior knowledge and mathematical understanding, middle school students need to be given the opportunity to make connections among a variety of representations. Graphs, tables, algebraic formulas, and models are just a few examples of representations that can help students explore quantitative relationships. As a mathematics educator,…

Dynamic graph of an oxy-fuel combustion system using autocatalytic set model

NASA Astrophysics Data System (ADS)

Harish, Noor Ainy; Bakar, Sumarni Abu

2017-08-01

Evaporation process is one of the main processes besides combustion process in an oxy-combustion boiler system. An Autocatalytic Set (ASC) Model has successfully applied in developing graphical representation of the chemical reactions that occurs in the evaporation process in the system. Seventeen variables identified in the process are represented as nodes and the catalytic relationships are represented as edges in the graph. In addition, in this paper graph dynamics of ACS is further investigated. By using Dynamic Autocatalytic Set Graph Algorithm (DAGA), the adjacency matrix for each of the graphs and its relations to Perron-Frobenius Theorem is investigated. The dynamic graph obtained is further investigated where the connection of the graph to fuzzy graph Type 1 is established.

Molecular graph convolutions: moving beyond fingerprints

NASA Astrophysics Data System (ADS)

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-08-01

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph—atoms, bonds, distances, etc.—which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

Dual nozzle aerodynamic and cooling analysis study. [dual throat and dual expander nozzles

NASA Technical Reports Server (NTRS)

Meagher, G. M.

1980-01-01

Geometric, aerodynamic flow field, performance prediction, and heat transfer analyses are considered for two advanced chamber nozzle concepts applicable to Earth-to-orbit engine systems. Topics covered include improvements to the dual throat aerodynamic and performance prediction program; geometric and flow field analyses of the dual expander concept; heat transfer analysis of both concepts, and engineering analysis of data from the NASA/MSFC hot-fire testing of a dual throat thruster model thrust chamber assembly. Preliminary results obtained are presented in graphs.

The effect of Gestalt laws of perceptual organization on the comprehension of three-variable bar and line graphs.

PubMed

Ali, Nadia; Peebles, David

2013-02-01

We report three experiments investigating the ability of undergraduate college students to comprehend 2 x 2 "interaction" graphs from two-way factorial research designs. Factorial research designs are an invaluable research tool widely used in all branches of the natural and social sciences, and the teaching of such designs lies at the core of many college curricula. Such data can be represented in bar or line graph form. Previous studies have shown, however, that people interpret these two graphical forms differently. In Experiment 1, participants were required to interpret interaction data in either bar or line graphs while thinking aloud. Verbal protocol analysis revealed that line graph users were significantly more likely to misinterpret the data or fail to interpret the graph altogether. The patterns of errors line graph users made were interpreted as arising from the operation of Gestalt principles of perceptual organization, and this interpretation was used to develop two modified versions of the line graph, which were then tested in two further experiments. One of the modifications resulted in a significant improvement in performance. Results of the three experiments support the proposed explanation and demonstrate the effects (both positive and negative) of Gestalt principles of perceptual organization on graph comprehension. We propose that our new design provides a more balanced representation of the data than the standard line graph for nonexpert users to comprehend the full range of relationships in two-way factorial research designs and may therefore be considered a more appropriate representation for use in educational and other nonexpert contexts.

Toward Topology Dualism: Improving the Accuracy of AS Annotations for Routers

NASA Astrophysics Data System (ADS)

Huffaker, Bradley; Dhamdhere, Amogh; Fomenkov, Marina; Claffy, Kc

To describe, analyze, and model the topological and structural characteristics of the Internet, researchers use Internet maps constructed at the router or autonomous system (AS) level. Although progress has been made on each front individually, a dual graph representing connectivity of routers with AS labels remains an elusive goal. We take steps toward merging the router-level and AS-level views of the Internet. We start from a collection of traces, i.e. sequences of IP addresses obtained with large-scale traceroute measurements from a distributed set of vantage points. We use state-of-the-art alias resolution techniques to identify interfaces belonging to the same router. We develop novel heuristics to assign routers to ASes, producing an AS-router dual graph. We validate our router assignment heuristics using data provided by tier-1 and tier-2 ISPs and five research networks, and show that we successfully assign 80% of routers with interfaces from multiple ASes to the correct AS. When we include routers with interfaces from a single AS, the accuracy drops to 71%, due to the 24% of total inferred routers for which our measurement or alias resolution fails to find an interface belonging to the correct AS. We use our dual graph construct to estimate economic properties of the AS-router dual graph, such as the number of internal and border routers owned by different types of ASes. We also demonstrate how our techniques can improve IP-AS mapping, including resolving up to 62% of false loops we observed in AS paths derived from traceroutes.

L1 and L2 Picture Naming in Mandarin-English Bilinguals: A Test of Bilingual Dual Coding Theory

ERIC Educational Resources Information Center

Jared, Debra; Poh, Rebecca Pei Yun; Paivio, Allan

2013-01-01

This study examined the nature of bilinguals' conceptual representations and the links from these representations to words in L1 and L2. Specifically, we tested an assumption of the Bilingual Dual Coding Theory that conceptual representations include image representations, and that learning two languages in separate contexts can result in…

Do Knowledge-Component Models Need to Incorporate Representational Competencies?

ERIC Educational Resources Information Center

Rau, Martina Angela

2017-01-01

Traditional knowledge-component models describe students' content knowledge (e.g., their ability to carry out problem-solving procedures or their ability to reason about a concept). In many STEM domains, instruction uses multiple visual representations such as graphs, figures, and diagrams. The use of visual representations implies a…

Teacher's Representational Fluency in a Context of Technology Use

ERIC Educational Resources Information Center

Rocha, Helena

2016-01-01

This study focuses on teacher's Knowledge for Teaching Mathematics with Technology (KTMT), paying a special attention to teacher's representational fluency. It intends to characterize how the teacher uses and integrates the different representations provided by the graphing calculator on the process of teaching and learning functions at the high…

Graphing Powers and Roots of Complex Numbers.

ERIC Educational Resources Information Center

Embse, Charles Vonder

1993-01-01

Using De Moivre's theorem and a parametric graphing utility, examines powers and roots of complex numbers and allows students to establish connections between the visual and numerical representations of complex numbers. Provides a program to numerically verify the roots of complex numbers. (MDH)

Model validation of simple-graph representations of metabolism

PubMed Central

Holme, Petter

2009-01-01

The large-scale properties of chemical reaction systems, such as metabolism, can be studied with graph-based methods. To do this, one needs to reduce the information, lists of chemical reactions, available in databases. Even for the simplest type of graph representation, this reduction can be done in several ways. We investigate different simple network representations by testing how well they encode information about one biologically important network structure—network modularity (the propensity for edges to be clustered into dense groups that are sparsely connected between each other). To achieve this goal, we design a model of reaction systems where network modularity can be controlled and measure how well the reduction to simple graphs captures the modular structure of the model reaction system. We find that the network types that best capture the modular structure of the reaction system are substrate–product networks (where substrates are linked to products of a reaction) and substance networks (with edges between all substances participating in a reaction). Furthermore, we argue that the proposed model for reaction systems with tunable clustering is a general framework for studies of how reaction systems are affected by modularity. To this end, we investigate statistical properties of the model and find, among other things, that it recreates correlations between degree and mass of the molecules. PMID:19158012

Visuospatial referents facilitate the learning and transfer of mathematical operations: extending the role of the angular gyrus.

PubMed

Pyke, Aryn; Betts, Shawn; Fincham, Jon M; Anderson, John R

2015-03-01

Different external representations for learning and solving mathematical operations may affect learning and transfer. To explore the effects of learning representations, learners were each introduced to two new operations (b↑n and b↓n) via either formulas or graphical representations. Both groups became adept at solving regular (trained) problems. During transfer, no external formulas or graphs were present; however, graph learners' knowledge could allow them to mentally associate problem expressions with visuospatial referents. The angular gyrus (AG) has recently been hypothesized to map problems to mental referents (e.g., symbolic answers; Grabner, Ansari, Koschutnig, Reishofer, & Ebner Human Brain Mapping, 34, 1013-1024, 2013), and we sought to test this hypothesis for visuospatial referents. To determine whether the AG and other math (horizontal intraparietal sulcus) and visuospatial (fusiform and posterior superior parietal lobule [PSPL]) regions were implicated in processing visuospatial mental referents, we included two types of transfer problems, computational and relational, which differed in referential load (one graph vs. two). During solving, the activations in AG, PSPL, and fusiform reflected the referential load manipulation among graph but not formula learners. Furthermore, the AG was more active among graph learners overall, which is consistent with its hypothesized referential role. Behavioral performance was comparable across the groups on computational transfer problems, which could be solved in a way that incorporated learners' respective procedures for regular problems. However, graph learners were more successful on relational transfer problems, which assessed their understanding of the relations between pairs of similar problems within and across operations. On such problems, their behavioral performance correlated with activation in the AG, fusiform, and a relational processing region (BA 10).

Image processing meta-algorithm development via genetic manipulation of existing algorithm graphs

NASA Astrophysics Data System (ADS)

Schalkoff, Robert J.; Shaaban, Khaled M.

1999-07-01

Automatic algorithm generation for image processing applications is not a new idea, however previous work is either restricted to morphological operates or impractical. In this paper, we show recent research result in the development and use of meta-algorithms, i.e. algorithms which lead to new algorithms. Although the concept is generally applicable, the application domain in this work is restricted to image processing. The meta-algorithm concept described in this paper is based upon out work in dynamic algorithm. The paper first present the concept of dynamic algorithms which, on the basis of training and archived algorithmic experience embedded in an algorithm graph (AG), dynamically adjust the sequence of operations applied to the input image data. Each node in the tree-based representation of a dynamic algorithm with out degree greater than 2 is a decision node. At these nodes, the algorithm examines the input data and determines which path will most likely achieve the desired results. This is currently done using nearest-neighbor classification. The details of this implementation are shown. The constrained perturbation of existing algorithm graphs, coupled with a suitable search strategy, is one mechanism to achieve meta-algorithm an doffers rich potential for the discovery of new algorithms. In our work, a meta-algorithm autonomously generates new dynamic algorithm graphs via genetic recombination of existing algorithm graphs. The AG representation is well suited to this genetic-like perturbation, using a commonly- employed technique in artificial neural network synthesis, namely the blueprint representation of graphs. A number of exam. One of the principal limitations of our current approach is the need for significant human input in the learning phase. Efforts to overcome this limitation are discussed. Future research directions are indicated.

LDRD final report :

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brost, Randolph C.; McLendon, William Clarence,

2013-01-01

Modeling geospatial information with semantic graphs enables search for sites of interest based on relationships between features, without requiring strong a priori models of feature shape or other intrinsic properties. Geospatial semantic graphs can be constructed from raw sensor data with suitable preprocessing to obtain a discretized representation. This report describes initial work toward extending geospatial semantic graphs to include temporal information, and initial results applying semantic graph techniques to SAR image data. We describe an efficient graph structure that includes geospatial and temporal information, which is designed to support simultaneous spatial and temporal search queries. We also report amore » preliminary implementation of feature recognition, semantic graph modeling, and graph search based on input SAR data. The report concludes with lessons learned and suggestions for future improvements.« less

Molecular graph convolutions: moving beyond fingerprints

PubMed Central

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-01-01

Molecular “fingerprints” encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph—atoms, bonds, distances, etc.—which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement. PMID:27558503

Neuro-symbolic representation learning on biological knowledge graphs.

PubMed

Alshahrani, Mona; Khan, Mohammad Asif; Maddouri, Omar; Kinjo, Akira R; Queralt-Rosinach, Núria; Hoehndorf, Robert

2017-09-01

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics. https://github.com/bio-ontology-research-group/walking-rdf-and-owl. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

A framework for graph-based synthesis, analysis, and visualization of HPC cluster job data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayo, Jackson R.; Kegelmeyer, W. Philip, Jr.; Wong, Matthew H.

The monitoring and system analysis of high performance computing (HPC) clusters is of increasing importance to the HPC community. Analysis of HPC job data can be used to characterize system usage and diagnose and examine failure modes and their effects. This analysis is not straightforward, however, due to the complex relationships that exist between jobs. These relationships are based on a number of factors, including shared compute nodes between jobs, proximity of jobs in time, etc. Graph-based techniques represent an approach that is particularly well suited to this problem, and provide an effective technique for discovering important relationships in jobmore » queuing and execution data. The efficacy of these techniques is rooted in the use of a semantic graph as a knowledge representation tool. In a semantic graph job data, represented in a combination of numerical and textual forms, can be flexibly processed into edges, with corresponding weights, expressing relationships between jobs, nodes, users, and other relevant entities. This graph-based representation permits formal manipulation by a number of analysis algorithms. This report presents a methodology and software implementation that leverages semantic graph-based techniques for the system-level monitoring and analysis of HPC clusters based on job queuing and execution data. Ontology development and graph synthesis is discussed with respect to the domain of HPC job data. The framework developed automates the synthesis of graphs from a database of job information. It also provides a front end, enabling visualization of the synthesized graphs. Additionally, an analysis engine is incorporated that provides performance analysis, graph-based clustering, and failure prediction capabilities for HPC systems.« less

The Significance of Rising Sea Levels.

ERIC Educational Resources Information Center

Conway, Gregory J.

1989-01-01

Describes an activity in which students graph changes in tides and ocean levels over a period in order to obtain a visual representation of the changes taking place and their effects upon the Earth. Provides questions for students to answer after construction of the graphs. (RT)

Network analysis for the visualization and analysis of qualitative data.

PubMed

Pokorny, Jennifer J; Norman, Alex; Zanesco, Anthony P; Bauer-Wu, Susan; Sahdra, Baljinder K; Saron, Clifford D

2018-03-01

We present a novel manner in which to visualize the coding of qualitative data that enables representation and analysis of connections between codes using graph theory and network analysis. Network graphs are created from codes applied to a transcript or audio file using the code names and their chronological location. The resulting network is a representation of the coding data that characterizes the interrelations of codes. This approach enables quantification of qualitative codes using network analysis and facilitates examination of associations of network indices with other quantitative variables using common statistical procedures. Here, as a proof of concept, we applied this method to a set of interview transcripts that had been coded in 2 different ways and the resultant network graphs were examined. The creation of network graphs allows researchers an opportunity to view and share their qualitative data in an innovative way that may provide new insights and enhance transparency of the analytical process by which they reach their conclusions. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks.

PubMed

Rutishauser, Ueli; Slotine, Jean-Jacques; Douglas, Rodney J

2018-05-01

Finding actions that satisfy the constraints imposed by both external inputs and internal representations is central to decision making. We demonstrate that some important classes of constraint satisfaction problems (CSPs) can be solved by networks composed of homogeneous cooperative-competitive modules that have connectivity similar to motifs observed in the superficial layers of neocortex. The winner-take-all modules are sparsely coupled by programming neurons that embed the constraints onto the otherwise homogeneous modular computational substrate. We show rules that embed any instance of the CSP's planar four-color graph coloring, maximum independent set, and sudoku on this substrate and provide mathematical proofs that guarantee these graph coloring problems will convergence to a solution. The network is composed of nonsaturating linear threshold neurons. Their lack of right saturation allows the overall network to explore the problem space driven through the unstable dynamics generated by recurrent excitation. The direction of exploration is steered by the constraint neurons. While many problems can be solved using only linear inhibitory constraints, network performance on hard problems benefits significantly when these negative constraints are implemented by nonlinear multiplicative inhibition. Overall, our results demonstrate the importance of instability rather than stability in network computation and offer insight into the computational role of dual inhibitory mechanisms in neural circuits.

Students' meaning making in science: solving energy resource problems in virtual worlds combined with spreadsheets to develop graphs

NASA Astrophysics Data System (ADS)

Krange, Ingeborg; Arnseth, Hans Christian

2012-09-01

The aim of this study is to scrutinize the characteristics of conceptual meaning making when students engage with virtual worlds in combination with a spreadsheet with the aim to develop graphs. We study how these tools and the representations they contain or enable students to construct serve to influence their understanding of energy resource consumption. The data were gathered in 1st grade upper-secondary science classes and they constitute the basis for the interaction analysis of students' meaning making with representations. Our analyses demonstrate the difficulties involved in developing students' orientation toward more conceptual orientations to representations of the knowledge domain. Virtual worlds do not in themselves represent a solution to this problem.

Persistent identifiers for web service requests relying on a provenance ontology design pattern

NASA Astrophysics Data System (ADS)

Car, Nicholas; Wang, Jingbo; Wyborn, Lesley; Si, Wei

2016-04-01

Delivering provenance information for datasets produced from static inputs is relatively straightforward: we represent the processing actions and data flow using provenance ontologies and link to stored copies of the inputs stored in repositories. If appropriate detail is given, the provenance information can then describe what actions have occurred (transparency) and enable reproducibility. When web service-generated data is used by a process to create a dataset instead of a static inputs, we need to use sophisticated provenance representations of the web service request as we can no longer just link to data stored in a repository. A graph-based provenance representation, such as the W3C's PROV standard, can be used to model the web service request as a single conceptual dataset and also as a small workflow with a number of components within the same provenance report. This dual representation does more than just allow simplified or detailed views of a dataset's production to be used where appropriate. It also allow persistent identifiers to be assigned to instances of a web service requests, thus enabling one form of dynamic data citation, and for those identifiers to resolve to whatever level of detail implementers think appropriate in order for that web service request to be reproduced. In this presentation we detail our reasoning in representing web service requests as small workflows. In outline, this stems from the idea that web service requests are perdurant things and in order to most easily persist knowledge of them for provenance, we should represent them as a nexus of relationships between endurant things, such as datasets and knowledge of particular system types, as these endurant things are far easier to persist. We also describe the ontology design pattern that we use to represent workflows in general and how we apply it to different types of web service requests. We give examples of specific web service requests instances that were made by systems at Australia's National Computing Infrastructure and show how one can 'click' through provenance interfaces to see the dual representations of the requests using provenance management tooling we have built.

Research-Based Worksheets on Using Multiple Representations in Science Classrooms

ERIC Educational Resources Information Center

Hill, Matthew; Sharma, Manjula

2015-01-01

The ability to represent the world like a scientist is difficult to teach; it is more than simply knowing the representations (e.g., graphs, words, equations and diagrams). For meaningful science learning to take place, consideration needs to be given to explicitly integrating representations into instructional methods, linked to the content, and…

A high-order staggered meshless method for elliptic problems

DOE PAGES

Trask, Nathaniel; Perego, Mauro; Bochev, Pavel Blagoveston

2017-03-21

Here, we present a new meshless method for scalar diffusion equations, which is motivated by their compatible discretizations on primal-dual grids. Unlike the latter though, our approach is truly meshless because it only requires the graph of nearby neighbor connectivity of the discretization points. This graph defines a local primal-dual grid complex with a virtual dual grid, in the sense that specification of the dual metric attributes is implicit in the method's construction. Our method combines a topological gradient operator on the local primal grid with a generalized moving least squares approximation of the divergence on the local dual grid. We show that the resulting approximation of the div-grad operator maintains polynomial reproduction to arbitrary orders and yields a meshless method, which attainsmore » $$O(h^{m})$$ convergence in both $L^2$- and $H^1$-norms, similar to mixed finite element methods. We demonstrate this convergence on curvilinear domains using manufactured solutions in two and three dimensions. Application of the new method to problems with discontinuous coefficients reveals solutions that are qualitatively similar to those of compatible mesh-based discretizations.« less

Data graphing methods, articles of manufacture, and computing devices

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wong, Pak Chung; Mackey, Patrick S.; Cook, Kristin A.

Data graphing methods, articles of manufacture, and computing devices are described. In one aspect, a method includes accessing a data set, displaying a graphical representation including data of the data set which is arranged according to a first of different hierarchical levels, wherein the first hierarchical level represents the data at a first of a plurality of different resolutions which respectively correspond to respective ones of the hierarchical levels, selecting a portion of the graphical representation wherein the data of the portion is arranged according to the first hierarchical level at the first resolution, modifying the graphical representation by arrangingmore » the data of the portion according to a second of the hierarchal levels at a second of the resolutions, and after the modifying, displaying the graphical representation wherein the data of the portion is arranged according to the second hierarchal level at the second resolution.« less

Probabilistic generation of random networks taking into account information on motifs occurrence.

PubMed

Bois, Frederic Y; Gayraud, Ghislaine

2015-01-01

Because of the huge number of graphs possible even with a small number of nodes, inference on network structure is known to be a challenging problem. Generating large random directed graphs with prescribed probabilities of occurrences of some meaningful patterns (motifs) is also difficult. We show how to generate such random graphs according to a formal probabilistic representation, using fast Markov chain Monte Carlo methods to sample them. As an illustration, we generate realistic graphs with several hundred nodes mimicking a gene transcription interaction network in Escherichia coli.

Probabilistic Generation of Random Networks Taking into Account Information on Motifs Occurrence

PubMed Central

Bois, Frederic Y.

2015-01-01

Abstract Because of the huge number of graphs possible even with a small number of nodes, inference on network structure is known to be a challenging problem. Generating large random directed graphs with prescribed probabilities of occurrences of some meaningful patterns (motifs) is also difficult. We show how to generate such random graphs according to a formal probabilistic representation, using fast Markov chain Monte Carlo methods to sample them. As an illustration, we generate realistic graphs with several hundred nodes mimicking a gene transcription interaction network in Escherichia coli. PMID:25493547

Novel Spectral Representations and Sparsity-Driven Algorithms for Shape Modeling and Analysis

NASA Astrophysics Data System (ADS)

Zhong, Ming

In this dissertation, we focus on extending classical spectral shape analysis by incorporating spectral graph wavelets and sparsity-seeking algorithms. Defined with the graph Laplacian eigenbasis, the spectral graph wavelets are localized both in the vertex domain and graph spectral domain, and thus are very effective in describing local geometry. With a rich dictionary of elementary vectors and forcing certain sparsity constraints, a real life signal can often be well approximated by a very sparse coefficient representation. The many successful applications of sparse signal representation in computer vision and image processing inspire us to explore the idea of employing sparse modeling techniques with dictionary of spectral basis to solve various shape modeling problems. Conventional spectral mesh compression uses the eigenfunctions of mesh Laplacian as shape bases, which are highly inefficient in representing local geometry. To ameliorate, we advocate an innovative approach to 3D mesh compression using spectral graph wavelets as dictionary to encode mesh geometry. The spectral graph wavelets are locally defined at individual vertices and can better capture local shape information than Laplacian eigenbasis. The multi-scale SGWs form a redundant dictionary as shape basis, so we formulate the compression of 3D shape as a sparse approximation problem that can be readily handled by greedy pursuit algorithms. Surface inpainting refers to the completion or recovery of missing shape geometry based on the shape information that is currently available. We devise a new surface inpainting algorithm founded upon the theory and techniques of sparse signal recovery. Instead of estimating the missing geometry directly, our novel method is to find this low-dimensional representation which describes the entire original shape. More specifically, we find that, for many shapes, the vertex coordinate function can be well approximated by a very sparse coefficient representation with respect to the dictionary comprising its Laplacian eigenbasis, and it is then possible to recover this sparse representation from partial measurements of the original shape. Taking advantage of the sparsity cue, we advocate a novel variational approach for surface inpainting, integrating data fidelity constraints on the shape domain with coefficient sparsity constraints on the transformed domain. Because of the powerful properties of Laplacian eigenbasis, the inpainting results of our method tend to be globally coherent with the remaining shape. Informative and discriminative feature descriptors are vital in qualitative and quantitative shape analysis for a large variety of graphics applications. We advocate novel strategies to define generalized, user-specified features on shapes. Our new region descriptors are primarily built upon the coefficients of spectral graph wavelets that are both multi-scale and multi-level in nature, consisting of both local and global information. Based on our novel spectral feature descriptor, we developed a user-specified feature detection framework and a tensor-based shape matching algorithm. Through various experiments, we demonstrate the competitive performance of our proposed methods and the great potential of spectral basis and sparsity-driven methods for shape modeling.

Enumerating Substituted Benzene Isomers of Tree-Like Chemical Graphs.

PubMed

Li, Jinghui; Nagamochi, Hiroshi; Akutsu, Tatsuya

2018-01-01

Enumeration of chemical structures is useful for drug design, which is one of the main targets of computational biology and bioinformatics. A chemical graph with no other cycles than benzene rings is called tree-like, and becomes a tree possibly with multiple edges if we contract each benzene ring into a single virtual atom of valence 6. All tree-like chemical graphs with a given tree representation are called the substituted benzene isomers of . When we replace each virtual atom in with a benzene ring to obtain a substituted benzene isomer, distinct isomers of are caused by the difference in arrangements of atom groups around a benzene ring. In this paper, we propose an efficient algorithm that enumerates all substituted benzene isomers of a given tree representation . Our algorithm first counts the number of all the isomers of the tree representation by a dynamic programming method. To enumerate all the isomers, for each , our algorithm then generates the th isomer by backtracking the counting phase of the dynamic programming. We also implemented our algorithm for computational experiments.

Graphing trillions of triangles.

PubMed

Burkhardt, Paul

2017-07-01

The increasing size of Big Data is often heralded but how data are transformed and represented is also profoundly important to knowledge discovery, and this is exemplified in Big Graph analytics. Much attention has been placed on the scale of the input graph but the product of a graph algorithm can be many times larger than the input. This is true for many graph problems, such as listing all triangles in a graph. Enabling scalable graph exploration for Big Graphs requires new approaches to algorithms, architectures, and visual analytics. A brief tutorial is given to aid the argument for thoughtful representation of data in the context of graph analysis. Then a new algebraic method to reduce the arithmetic operations in counting and listing triangles in graphs is introduced. Additionally, a scalable triangle listing algorithm in the MapReduce model will be presented followed by a description of the experiments with that algorithm that led to the current largest and fastest triangle listing benchmarks to date. Finally, a method for identifying triangles in new visual graph exploration technologies is proposed.

Examining Students' Reluctance to Use Graphs

ERIC Educational Resources Information Center

Dyke, Frances Van; White, Alexander

2004-01-01

An evaluation designed to test basic graphical-thinking skills to students entering calculus or applied calculus at American University was given to use the assessment to discover the underlying causes for student's inability to use graphs effectively. The study indicates that graphical representation is not emphasized properly in the curriculum…

Unsupervised Metric Fusion Over Multiview Data by Graph Random Walk-Based Cross-View Diffusion.

PubMed

Wang, Yang; Zhang, Wenjie; Wu, Lin; Lin, Xuemin; Zhao, Xiang

2017-01-01

Learning an ideal metric is crucial to many tasks in computer vision. Diverse feature representations may combat this problem from different aspects; as visual data objects described by multiple features can be decomposed into multiple views, thus often provide complementary information. In this paper, we propose a cross-view fusion algorithm that leads to a similarity metric for multiview data by systematically fusing multiple similarity measures. Unlike existing paradigms, we focus on learning distance measure by exploiting a graph structure of data samples, where an input similarity matrix can be improved through a propagation of graph random walk. In particular, we construct multiple graphs with each one corresponding to an individual view, and a cross-view fusion approach based on graph random walk is presented to derive an optimal distance measure by fusing multiple metrics. Our method is scalable to a large amount of data by enforcing sparsity through an anchor graph representation. To adaptively control the effects of different views, we dynamically learn view-specific coefficients, which are leveraged into graph random walk to balance multiviews. However, such a strategy may lead to an over-smooth similarity metric where affinities between dissimilar samples may be enlarged by excessively conducting cross-view fusion. Thus, we figure out a heuristic approach to controlling the iteration number in the fusion process in order to avoid over smoothness. Extensive experiments conducted on real-world data sets validate the effectiveness and efficiency of our approach.

Multimedia and Understanding: Expert and Novice Responses To Different Representations of Chemical Phenomena.

ERIC Educational Resources Information Center

Kozma, Robert B.; Russell, Joel

1997-01-01

Examines how professional chemists and undergraduate chemistry students respond to chemistry-related video segments, graphs, animations, and equations. Discusses the role that surface features of representations play in the understanding of chemistry. Contains 36 references. (DDR)

Structural Features of Algebraic Quantum Notations

ERIC Educational Resources Information Center

Gire, Elizabeth; Price, Edward

2015-01-01

The formalism of quantum mechanics includes a rich collection of representations for describing quantum systems, including functions, graphs, matrices, histograms of probabilities, and Dirac notation. The varied features of these representations affect how computations are performed. For example, identifying probabilities of measurement outcomes…

[A retrieval method of drug molecules based on graph collapsing].

PubMed

Qu, J W; Lv, X Q; Liu, Z M; Liao, Y; Sun, P H; Wang, B; Tang, Z

2018-04-18

To establish a compact and efficient hypergraph representation and a graph-similarity-based retrieval method of molecules to achieve effective and efficient medicine information retrieval. Chemical structural formula (CSF) was a primary search target as a unique and precise identifier for each compound at the molecular level in the research field of medicine information retrieval. To retrieve medicine information effectively and efficiently, a complete workflow of the graph-based CSF retrieval system was introduced. This system accepted the photos taken from smartphones and the sketches drawn on tablet personal computers as CSF inputs, and formalized the CSFs with the corresponding graphs. Then this paper proposed a compact and efficient hypergraph representation for molecules on the basis of analyzing factors that directly affected the efficiency of graph matching. According to the characteristics of CSFs, a hierarchical collapsing method combining graph isomorphism and frequent subgraph mining was adopted. There was yet a fundamental challenge, subgraph overlapping during the collapsing procedure, which hindered the method from establishing the correct compact hypergraph of an original CSF graph. Therefore, a graph-isomorphism-based algorithm was proposed to select dominant acyclic subgraphs on the basis of overlapping analysis. Finally, the spatial similarity among graphical CSFs was evaluated by multi-dimensional measures of similarity. To evaluate the performance of the proposed method, the proposed system was firstly compared with Wikipedia Chemical Structure Explorer (WCSE), the state-of-the-art system that allowed CSF similarity searching within Wikipedia molecules dataset, on retrieval accuracy. The system achieved higher values on mean average precision, discounted cumulative gain, rank-biased precision, and expected reciprocal rank than WCSE from the top-2 to the top-10 retrieved results. Specifically, the system achieved 10%, 1.41, 6.42%, and 1.32% higher than WCSE on these metrics for top-10 retrieval results, respectively. Moreover, several retrieval cases were presented to intuitively compare with WCSE. The results of the above comparative study demonstrated that the proposed method outperformed the existing method with regard to accuracy and effectiveness. This paper proposes a graph-similarity-based retrieval approach for medicine information. To obtain satisfactory retrieval results, an isomorphism-based algorithm is proposed for dominant subgraph selection based on the subgraph overlapping analysis, as well as an effective and efficient hypergraph representation of molecules. Experiment results demonstrate the effectiveness of the proposed approach.

Integral representations on supermanifolds: super Hodge duals, PCOs and Liouville forms

NASA Astrophysics Data System (ADS)

Castellani, Leonardo; Catenacci, Roberto; Grassi, Pietro Antonio

2017-01-01

We present a few types of integral transforms and integral representations that are very useful for extending to supergeometry many familiar concepts of differential geometry. Among them we discuss the construction of the super Hodge dual, the integral representation of picture changing operators of string theories and the construction of the super-Liouville form of a symplectic supermanifold.

Graphs for information security control in software defined networks

NASA Astrophysics Data System (ADS)

Grusho, Alexander A.; Abaev, Pavel O.; Shorgin, Sergey Ya.; Timonina, Elena E.

2017-07-01

Information security control in software defined networks (SDN) is connected with execution of the security policy rules regulating information accesses and protection against distribution of the malicious code and harmful influences. The paper offers a representation of a security policy in the form of hierarchical structure which in case of distribution of resources for the solution of tasks defines graphs of admissible interactions in a networks. These graphs define commutation tables of switches via the SDN controller.

Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs

PubMed Central

Gómez-Adorno, Helena; Sidorov, Grigori; Pinto, David; Vilariño, Darnes; Gelbukh, Alexander

2016-01-01

We apply the integrated syntactic graph feature extraction methodology to the task of automatic authorship detection. This graph-based representation allows integrating different levels of language description into a single structure. We extract textual patterns based on features obtained from shortest path walks over integrated syntactic graphs and apply them to determine the authors of documents. On average, our method outperforms the state of the art approaches and gives consistently high results across different corpora, unlike existing methods. Our results show that our textual patterns are useful for the task of authorship attribution. PMID:27589740

Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning

PubMed Central

Wang, Zhengxia; Zhu, Xiaofeng; Adeli, Ehsan; Zhu, Yingying; Nie, Feiping; Munsell, Brent

2018-01-01

Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In particular, conventional GTL approaches first construct a fixed inter-subject relation graph that is based on similarities in voxel intensity values in the feature domain, which can then be used to propagate the known phenotype data (i.e., clinical scores and labels) from the training data to the testing data in the label domain. However, this type of graph is exclusively learned in the feature domain, and primarily due to outliers in the observed features, may not be optimal for label propagation in the label domain. To address this limitation, a progressive GTL (pGTL) method is proposed that gradually finds an intrinsic data representation that more accurately aligns imaging features with the phenotype data. In general, optimal feature-to-phenotype alignment is achieved using an iterative approach that: (1) refines inter-subject relationships observed in the feature domain by using the learned intrinsic data representation in the label domain, (2) updates the intrinsic data representation from the refined inter-subject relationships, and (3) verifies the intrinsic data representation on the training data to guarantee an optimal classification when applied to testing data. Additionally, the iterative approach is extended to multi-modal imaging data to further improve pGTL classification accuracy. Using Alzheimer’s disease and Parkinson’s disease study data, the classification accuracy of the proposed pGTL method is compared to several state-of-the-art classification methods, and the results show pGTL can more accurately identify subjects, even at different progression stages, in these two study data sets. PMID:28551556

Structural Representations in Knowledge Acquisition.

ERIC Educational Resources Information Center

Gonzalvo, Pilar; And Others

1994-01-01

Multidimensional scaling (MDS) and Pathfinder techniques for assessing changes in the structural representation of a knowledge domain were studied with relatedness ratings collected from 72 Spanish college students. Comparison of student and expert similarity measures indicate that MDS and graph theoretic approaches are valid techniques. (SLD)

On the Misinterpretation of Histograms and Box Plots

ERIC Educational Resources Information Center

Lem, Stephanie; Onghena, Patrick; Verschaffel, Lieven; Van Dooren, Wim

2013-01-01

Recent studies have shown that the interpretation of graphs is not always easy for students. In order to reason properly about distributions of data, however, one needs to be able to interpret graphical representations of these distributions correctly. In this study, we used Tversky's principles for the design of graphs to explain how 125…

The Effect of Emergent Features on Judgments of Quantity in Configural and Separable Displays

ERIC Educational Resources Information Center

Peebles, David

2008-01-01

Two experiments investigated effects of emergent features on perceptual judgments of comparative magnitude in three diagrammatic representations: kiviat charts, bar graphs, and line graphs. Experiment 1 required participants to compare individual values; whereas in Experiment 2 participants had to integrate several values to produce a global…

BeeSign: A Computationally-Mediated Intervention to Examine K-1 Students' Representational Activities in the Context of Teaching Complex Systems Concepts

ERIC Educational Resources Information Center

Danish, Joshua Adam

2009-01-01

Representations such as drawings, graphs, and computer simulations, are central to learning and doing science. Furthermore, ongoing success in science learning requires students to build on the representations and associated practices that they are presumed to have learned throughout their schooling career. Without these practices, students have…

Analysis of Student Understanding of Science Concepts Including Mathematical Representations: Ph Values and the Relative Differences of pH Values

ERIC Educational Resources Information Center

Park, Eun-Jung; Choi, Kyunghee

2013-01-01

In general, mathematical representations such as formulae, numbers, and graphs are the inseparable components in science used to better describe or explain scientific phenomena or knowledge. Regardless of their necessity and benefit, science seems to be difficult for some students, as a result of the mathematical representations and problem…

BioJS DAGViewer: A reusable JavaScript component for displaying directed graphs

PubMed Central

Micklem, Gos

2014-01-01

Summary: The DAGViewer BioJS component is a reusable JavaScript component made available as part of the BioJS project and intended to be used to display graphs of structured data, with a particular emphasis on Directed Acyclic Graphs (DAGs). It enables users to embed representations of graphs of data, such as ontologies or phylogenetic trees, in hyper-text documents (HTML). This component is generic, since it is capable (given the appropriate configuration) of displaying any kind of data that is organised as a graph. The features of this component which are useful for examining and filtering large and complex graphs are described. Availability: http://github.com/alexkalderimis/dag-viewer-biojs; http://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.8303. PMID:24627804

Operator based integration of information in multimodal radiological search mission with applications to anomaly detection

NASA Astrophysics Data System (ADS)

Benedetto, J.; Cloninger, A.; Czaja, W.; Doster, T.; Kochersberger, K.; Manning, B.; McCullough, T.; McLane, M.

2014-05-01

Successful performance of radiological search mission is dependent on effective utilization of mixture of signals. Examples of modalities include, e.g., EO imagery and gamma radiation data, or radiation data collected during multiple events. In addition, elevation data or spatial proximity can be used to enhance the performance of acquisition systems. State of the art techniques in processing and exploitation of complex information manifolds rely on diffusion operators. Our approach involves machine learning techniques based on analysis of joint data- dependent graphs and their associated diffusion kernels. Then, the significant eigenvectors of the derived fused graph Laplace and Schroedinger operators form the new representation, which provides integrated features from the heterogeneous input data. The families of data-dependent Laplace and Schroedinger operators on joint data graphs, shall be integrated by means of appropriately designed fusion metrics. These fused representations are used for target and anomaly detection.

Graph Representations of Flow and Transport in Fracture Networks using Machine Learning

NASA Astrophysics Data System (ADS)

Srinivasan, G.; Viswanathan, H. S.; Karra, S.; O'Malley, D.; Godinez, H. C.; Hagberg, A.; Osthus, D.; Mohd-Yusof, J.

2017-12-01

Flow and transport of fluids through fractured systems is governed by the properties and interactions at the micro-scale. Retaining information about the micro-structure such as fracture length, orientation, aperture and connectivity in mesh-based computational models results in solving for millions to billions of degrees of freedom and quickly renders the problem computationally intractable. Our approach depicts fracture networks graphically, by mapping fractures to nodes and intersections to edges, thereby greatly reducing computational burden. Additionally, we use machine learning techniques to build simulators on the graph representation, trained on data from the mesh-based high fidelity simulations to speed up computation by orders of magnitude. We demonstrate our methodology on ensembles of discrete fracture networks, dividing up the data into training and validation sets. Our machine learned graph-based solvers result in over 3 orders of magnitude speedup without any significant sacrifice in accuracy.

L1-norm locally linear representation regularization multi-source adaptation learning.

PubMed

Tao, Jianwen; Wen, Shiting; Hu, Wenjun

2015-09-01

In most supervised domain adaptation learning (DAL) tasks, one has access only to a small number of labeled examples from target domain. Therefore the success of supervised DAL in this "small sample" regime needs the effective utilization of the large amounts of unlabeled data to extract information that is useful for generalization. Toward this end, we here use the geometric intuition of manifold assumption to extend the established frameworks in existing model-based DAL methods for function learning by incorporating additional information about the target geometric structure of the marginal distribution. We would like to ensure that the solution is smooth with respect to both the ambient space and the target marginal distribution. In doing this, we propose a novel L1-norm locally linear representation regularization multi-source adaptation learning framework which exploits the geometry of the probability distribution, which has two techniques. Firstly, an L1-norm locally linear representation method is presented for robust graph construction by replacing the L2-norm reconstruction measure in LLE with L1-norm one, which is termed as L1-LLR for short. Secondly, considering the robust graph regularization, we replace traditional graph Laplacian regularization with our new L1-LLR graph Laplacian regularization and therefore construct new graph-based semi-supervised learning framework with multi-source adaptation constraint, which is coined as L1-MSAL method. Moreover, to deal with the nonlinear learning problem, we also generalize the L1-MSAL method by mapping the input data points from the input space to a high-dimensional reproducing kernel Hilbert space (RKHS) via a nonlinear mapping. Promising experimental results have been obtained on several real-world datasets such as face, visual video and object. Copyright © 2015 Elsevier Ltd. All rights reserved.

Graphing trillions of triangles

PubMed Central

Burkhardt, Paul

2016-01-01

The increasing size of Big Data is often heralded but how data are transformed and represented is also profoundly important to knowledge discovery, and this is exemplified in Big Graph analytics. Much attention has been placed on the scale of the input graph but the product of a graph algorithm can be many times larger than the input. This is true for many graph problems, such as listing all triangles in a graph. Enabling scalable graph exploration for Big Graphs requires new approaches to algorithms, architectures, and visual analytics. A brief tutorial is given to aid the argument for thoughtful representation of data in the context of graph analysis. Then a new algebraic method to reduce the arithmetic operations in counting and listing triangles in graphs is introduced. Additionally, a scalable triangle listing algorithm in the MapReduce model will be presented followed by a description of the experiments with that algorithm that led to the current largest and fastest triangle listing benchmarks to date. Finally, a method for identifying triangles in new visual graph exploration technologies is proposed. PMID:28690426

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics.

PubMed

Sharma, Harshita; Alekseychuk, Alexander; Leskovsky, Peter; Hellwich, Olaf; Anand, R S; Zerbe, Norman; Hufnagl, Peter

2012-10-04

Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923.

Local adjacency metric dimension of sun graph and stacked book graph

NASA Astrophysics Data System (ADS)

Yulisda Badri, Alifiah; Darmaji

2018-03-01

A graph is a mathematical system consisting of a non-empty set of nodes and a set of empty sides. One of the topics to be studied in graph theory is the metric dimension. Application in the metric dimension is the navigation robot system on a path. Robot moves from one vertex to another vertex in the field by minimizing the errors that occur in translating the instructions (code) obtained from the vertices of that location. To move the robot must give different instructions (code). In order for the robot to move efficiently, the robot must be fast to translate the code of the nodes of the location it passes. so that the location vertex has a minimum distance. However, if the robot must move with the vertex location on a very large field, so the robot can not detect because the distance is too far.[6] In this case, the robot can determine its position by utilizing location vertices based on adjacency. The problem is to find the minimum cardinality of the required location vertex, and where to put, so that the robot can determine its location. The solution to this problem is the dimension of adjacency metric and adjacency metric bases. Rodrguez-Velzquez and Fernau combine the adjacency metric dimensions with local metric dimensions, thus becoming the local adjacency metric dimension. In the local adjacency metric dimension each vertex in the graph may have the same adjacency representation as the terms of the vertices. To obtain the local metric dimension of values in the graph of the Sun and the stacked book graph is used the construction method by considering the representation of each adjacent vertex of the graph.

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics

PubMed Central

2012-01-01

Background Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. Methods The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. Results The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. Conclusion The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. Virtual Slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923. PMID:23035717

Dynamic Uncertain Causality Graph for Knowledge Representation and Probabilistic Reasoning: Directed Cyclic Graph and Joint Probability Distribution.

PubMed

Zhang, Qin

2015-07-01

Probabilistic graphical models (PGMs) such as Bayesian network (BN) have been widely applied in uncertain causality representation and probabilistic reasoning. Dynamic uncertain causality graph (DUCG) is a newly presented model of PGMs, which can be applied to fault diagnosis of large and complex industrial systems, disease diagnosis, and so on. The basic methodology of DUCG has been previously presented, in which only the directed acyclic graph (DAG) was addressed. However, the mathematical meaning of DUCG was not discussed. In this paper, the DUCG with directed cyclic graphs (DCGs) is addressed. In contrast, BN does not allow DCGs, as otherwise the conditional independence will not be satisfied. The inference algorithm for the DUCG with DCGs is presented, which not only extends the capabilities of DUCG from DAGs to DCGs but also enables users to decompose a large and complex DUCG into a set of small, simple sub-DUCGs, so that a large and complex knowledge base can be easily constructed, understood, and maintained. The basic mathematical definition of a complete DUCG with or without DCGs is proved to be a joint probability distribution (JPD) over a set of random variables. The incomplete DUCG as a part of a complete DUCG may represent a part of JPD. Examples are provided to illustrate the methodology.

A searching and reporting system for relational databases using a graph-based metadata representation.

PubMed

Hewitt, Robin; Gobbi, Alberto; Lee, Man-Ling

2005-01-01

Relational databases are the current standard for storing and retrieving data in the pharmaceutical and biotech industries. However, retrieving data from a relational database requires specialized knowledge of the database schema and of the SQL query language. At Anadys, we have developed an easy-to-use system for searching and reporting data in a relational database to support our drug discovery project teams. This system is fast and flexible and allows users to access all data without having to write SQL queries. This paper presents the hierarchical, graph-based metadata representation and SQL-construction methods that, together, are the basis of this system's capabilities.

Detection of dual-band infrared small target based on joint dynamic sparse representation

NASA Astrophysics Data System (ADS)

Zhou, Jinwei; Li, Jicheng; Shi, Zhiguang; Lu, Xiaowei; Ren, Dongwei

2015-10-01

Infrared small target detection is a crucial and yet still is a difficult issue in aeronautic and astronautic applications. Sparse representation is an important mathematic tool and has been used extensively in image processing in recent years. Joint sparse representation is applied in dual-band infrared dim target detection in this paper. Firstly, according to the characters of dim targets in dual-band infrared images, 2-dimension Gaussian intensity model was used to construct target dictionary, then the dictionary was classified into different sub-classes according to different positions of Gaussian function's center point in image block; The fact that dual-band small targets detection can use the same dictionary and the sparsity doesn't lie in atom-level but in sub-class level was utilized, hence the detection of targets in dual-band infrared images was converted to be a joint dynamic sparse representation problem. And the dynamic active sets were used to describe the sparse constraint of coefficients. Two modified sparsity concentration index (SCI) criteria was proposed to evaluate whether targets exist in the images. In experiments, it shows that the proposed algorithm can achieve better detecting performance and dual-band detection is much more robust to noise compared with single-band detection. Moreover, the proposed method can be expanded to multi-spectrum small target detection.

Graphical Representations of Electronic Search Patterns.

ERIC Educational Resources Information Center

Lin, Xia; And Others

1991-01-01

Discussion of search behavior in electronic environments focuses on the development of GRIP (Graphic Representor of Interaction Patterns), a graphing tool based on HyperCard that produces graphic representations of search patterns. Search state spaces are explained, and forms of data available from electronic searches are described. (34…

Investigating Absolute Value: A Real World Application

ERIC Educational Resources Information Center

Kidd, Margaret; Pagni, David

2009-01-01

Making connections between various representations is important in mathematics. In this article, the authors discuss the numeric, algebraic, and graphical representations of sums of absolute values of linear functions. The initial explanations are accessible to all students who have experience graphing and who understand that absolute value simply…

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

PubMed

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Graph Theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanfilippo, Antonio P.

2005-12-27

Graph theory is a branch of discrete combinatorial mathematics that studies the properties of graphs. The theory was pioneered by the Swiss mathematician Leonhard Euler in the 18th century, commenced its formal development during the second half of the 19th century, and has witnessed substantial growth during the last seventy years, with applications in areas as diverse as engineering, computer science, physics, sociology, chemistry and biology. Graph theory has also had a strong impact in computational linguistics by providing the foundations for the theory of features structures that has emerged as one of the most widely used frameworks for themore » representation of grammar formalisms.« less

Bond graph modelling of multibody dynamics and its symbolic scheme

NASA Astrophysics Data System (ADS)

Kawase, Takehiko; Yoshimura, Hiroaki

A bond graph method of modeling multibody dynamics is demonstrated. Specifically, a symbolic generation scheme which fully utilizes the bond graph information is presented. It is also demonstrated that structural understanding and representation in bond graph theory is quite powerful for the modeling of such large scale systems, and that the nonenergic multiport of junction structure, which is a multiport expression of the system structure, plays an important role, as first suggested by Paynter. The principal part of the proposed symbolic scheme, that is, the elimination of excess variables, is done through tearing and interconnection in the sense of Kron using newly defined causal and causal coefficient arrays.

A Novel Graph Constructor for Semisupervised Discriminant Analysis: Combined Low-Rank and k-Nearest Neighbor Graph

PubMed Central

Pan, Yongke; Niu, Wenjia

2017-01-01

Semisupervised Discriminant Analysis (SDA) is a semisupervised dimensionality reduction algorithm, which can easily resolve the out-of-sample problem. Relative works usually focus on the geometric relationships of data points, which are not obvious, to enhance the performance of SDA. Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods. In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and k-nearest neighbor (LRKNN) graph. In our LRKNN graph, we map the data to the LR feature space and then the kNN is adopted to satisfy the algorithmic requirements of SDA. Since the low-rank representation can capture the global structure and the k-nearest neighbor algorithm can maximally preserve the local geometrical structure of the data, the LRKNN graph can significantly improve the performance of SDA. Extensive experiments on several real-world databases show that the proposed LRKNN graph is an efficient graph constructor, which can largely outperform other commonly used baselines. PMID:28316616

Graph Mining Meets the Semantic Web

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Sangkeun; Sukumar, Sreenivas R; Lim, Seung-Hwan

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today, data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. We address that need through implementation of three popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, and PageRank). We implement these algorithms as SPARQL queries, wrapped within Python scripts. We evaluatemore » the performance of our implementation on 6 real world data sets and show graph mining algorithms (that have a linear-algebra formulation) can indeed be unleashed on data represented as RDF graphs using the SPARQL query interface.« less

On a programming language for graph algorithms

NASA Technical Reports Server (NTRS)

Rheinboldt, W. C.; Basili, V. R.; Mesztenyi, C. K.

1971-01-01

An algorithmic language, GRAAL, is presented for describing and implementing graph algorithms of the type primarily arising in applications. The language is based on a set algebraic model of graph theory which defines the graph structure in terms of morphisms between certain set algebraic structures over the node set and arc set. GRAAL is modular in the sense that the user specifies which of these mappings are available with any graph. This allows flexibility in the selection of the storage representation for different graph structures. In line with its set theoretic foundation, the language introduces sets as a basic data type and provides for the efficient execution of all set and graph operators. At present, GRAAL is defined as an extension of ALGOL 60 (revised) and its formal description is given as a supplement to the syntactic and semantic definition of ALGOL. Several typical graph algorithms are written in GRAAL to illustrate various features of the language and to show its applicability.

Identifying Understudied Nuclear Reactions by Text-mining the EXFOR Experimental Nuclear Reaction Library

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hirdt, J.A.; Brown, D.A., E-mail: dbrown@bnl.gov

The EXFOR library contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We text-mined the REACTION and MONITOR fields of the ENTRYs in the EXFOR library in order to identify understudied reactions and quantities. Using the results of the text-mining, we created an undirected graph from the EXFOR datasets with each graph node representing a single reaction and quantity and graph links representing the various types of connections between these reactions and quantities. This graph is an abstract representation of the connections in EXFOR, similar to graphs of socialmore » networks, authorship networks, etc. We use various graph theoretical tools to identify important yet understudied reactions and quantities in EXFOR. Although we identified a few cross sections relevant for shielding applications and isotope production, mostly we identified charged particle fluence monitor cross sections. As a side effect of this work, we learn that our abstract graph is typical of other real-world graphs.« less

Identifying Understudied Nuclear Reactions by Text-mining the EXFOR Experimental Nuclear Reaction Library

NASA Astrophysics Data System (ADS)

Hirdt, J. A.; Brown, D. A.

2016-01-01

The EXFOR library contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We text-mined the REACTION and MONITOR fields of the ENTRYs in the EXFOR library in order to identify understudied reactions and quantities. Using the results of the text-mining, we created an undirected graph from the EXFOR datasets with each graph node representing a single reaction and quantity and graph links representing the various types of connections between these reactions and quantities. This graph is an abstract representation of the connections in EXFOR, similar to graphs of social networks, authorship networks, etc. We use various graph theoretical tools to identify important yet understudied reactions and quantities in EXFOR. Although we identified a few cross sections relevant for shielding applications and isotope production, mostly we identified charged particle fluence monitor cross sections. As a side effect of this work, we learn that our abstract graph is typical of other real-world graphs.

The Graphic Representation of Structure in Similarity/Dissimilarity Matrices: Alternative Methods.

ERIC Educational Resources Information Center

Rudnitsky, Alan N.

Three approaches to the graphic representation of similarity and dissimilarity matrices are compared and contrasted. Specifically, Kruskal's multidimensional scaling, Johnson's hierarchical clustering, and Waern's graphing techniques are employed to depict, in two dimensions, data representing the structure of a set of botanical concepts. Each of…

Processes and Reasoning in Representations of Linear Functions

ERIC Educational Resources Information Center

Adu-Gyamfi, Kwaku; Bossé, Michael J.

2014-01-01

This study examined student actions, interpretations, and language in respect to questions raised regarding tabular, graphical, and algebraic representations in the context of functions. The purpose was to investigate students' interpretations and specific ways of working within table, graph, and the algebraic on notions fundamental to a…

Helping Students-Connect Functions and Their Representations

ERIC Educational Resources Information Center

Moore-Russo, Deborah; Golzy, John B.

2005-01-01

The description about the changed instruction to encourage student exploration of the graphical and then the algebraic representations of functions is presented, which enables the students to understand how the graph, equation, and table of a function are related. The activity addresses both the Learning Principle and the Connection standard and…

The Design of Lessons Using Mathematics Analysis Software to Support Multiple Representations in Secondary School Mathematics

ERIC Educational Resources Information Center

Pierce, Robyn; Stacey, Kaye; Wander, Roger; Ball, Lynda

2011-01-01

Current technologies incorporating sophisticated mathematical analysis software (calculation, graphing, dynamic geometry, tables, and more) provide easy access to multiple representations of mathematical problems. Realising the affordances of such technology for students' learning requires carefully designed lessons. This paper reports on design…

Alternative Representations for Algebraic Problem Solving: When Are Graphs Better than Equations?

ERIC Educational Resources Information Center

Mielicki, Marta K.; Wiley, Jennifer

2016-01-01

Successful algebraic problem solving entails adaptability of solution methods using different representations. Prior research has suggested that students are more likely to prefer symbolic solution methods (equations) over graphical ones, even when graphical methods should be more efficient. However, this research has not tested how representation…

Simple graph models of information spread in finite populations

PubMed Central

Voorhees, Burton; Ryder, Bergerud

2015-01-01

We consider several classes of simple graphs as potential models for information diffusion in a structured population. These include biases cycles, dual circular flows, partial bipartite graphs and what we call ‘single-link’ graphs. In addition to fixation probabilities, we study structure parameters for these graphs, including eigenvalues of the Laplacian, conductances, communicability and expected hitting times. In several cases, values of these parameters are related, most strongly so for partial bipartite graphs. A measure of directional bias in cycles and circular flows arises from the non-zero eigenvalues of the antisymmetric part of the Laplacian and another measure is found for cycles as the value of the transition probability for which hitting times going in either direction of the cycle are equal. A generalization of circular flow graphs is used to illustrate the possibility of tuning edge weights to match pre-specified values for graph parameters; in particular, we show that generalizations of circular flows can be tuned to have fixation probabilities equal to the Moran probability for a complete graph by tuning vertex temperature profiles. Finally, single-link graphs are introduced as an example of a graph involving a bottleneck in the connection between two components and these are compared to the partial bipartite graphs. PMID:26064661

Reflecting on Graphs: Attributes of Graph Choice and Construction Practices in Biology

PubMed Central

Angra, Aakanksha; Gardner, Stephanie M.

2017-01-01

Undergraduate biology education reform aims to engage students in scientific practices such as experimental design, experimentation, and data analysis and communication. Graphs are ubiquitous in the biological sciences, and creating effective graphical representations involves quantitative and disciplinary concepts and skills. Past studies document student difficulties with graphing within the contexts of classroom or national assessments without evaluating student reasoning. Operating under the metarepresentational competence framework, we conducted think-aloud interviews to reveal differences in reasoning and graph quality between undergraduate biology students, graduate students, and professors in a pen-and-paper graphing task. All professors planned and thought about data before graph construction. When reflecting on their graphs, professors and graduate students focused on the function of graphs and experimental design, while most undergraduate students relied on intuition and data provided in the task. Most undergraduate students meticulously plotted all data with scaled axes, while professors and some graduate students transformed the data, aligned the graph with the research question, and reflected on statistics and sample size. Differences in reasoning and approaches taken in graph choice and construction corroborate and extend previous findings and provide rich targets for undergraduate and graduate instruction. PMID:28821538

Chemical Understanding and Graphing Skills in an Honors Case-Based Computerized Chemistry Laboratory Environment: The Value of Bidirectional Visual and Textual Representations

ERIC Educational Resources Information Center

Dori, Yehudit J.; Sasson, Irit

2008-01-01

The case-based computerized laboratory (CCL) is a chemistry learning environment that integrates computerized experiments with emphasis on scientific inquiry and comprehension of case studies. The research objective was to investigate chemical understanding and graphing skills of high school honors students via bidirectional visual and textual…

Metaphors in Mathematics Classrooms: Analyzing the Dynamic Process of Teaching and Learning of Graph Functions

ERIC Educational Resources Information Center

Font, Vicenc; Bolite, Janete; Acevedo, Jorge

2010-01-01

This article presents an analysis of a phenomenon that was observed within the dynamic processes of teaching and learning to read and elaborate Cartesian graphs for functions at high-school level. Two questions were considered during this investigation: What types of metaphors does the teacher use to explain the graphic representation of functions…

The Role of Microcomputer-Based Laboratories in Learning To Make Graphs of Distance and Velocity.

ERIC Educational Resources Information Center

Brasell, Heather

Two questions about the effects of microcomputer-based laboratory (MBL) activities on graphing skills were addressed in this study: (1) the extent to which activities help students link their concrete experiences with motion with graphic representations of these experiences; and (2) the degree of importance of the real-time aspect of the MBL in…

The Roles of Visualization and Symbolism in the Potential and Actual Infinity of the Limit Process

ERIC Educational Resources Information Center

Kidron, Ivy; Tall, David

2015-01-01

A teaching experiment-using Mathematica to investigate the convergence of sequence of functions visually as a sequence of objects (graphs) converging onto a fixed object (the graph of the limit function)-is here used to analyze how the approach can support the dynamic blending of visual and symbolic representations that has the potential to lead…

Temporally Scalable Visual SLAM using a Reduced Pose Graph

DTIC Science & Technology

2012-05-25

m b r i d g e , m a 0 213 9 u s a — w w w. c s a i l . m i t . e d u MIT-CSAIL-TR-2012-013 May 25, 2012 Temporally Scalable Visual SLAM using a...00-00-2012 4. TITLE AND SUBTITLE Temporally Scalable Visual SLAM using a Reduced Pose Graph 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...demonstrate a system for temporally scalable visual SLAM using a reduced pose graph representation. Unlike previous visual SLAM approaches that use

Are C-reps contextual representations? A reply to Brewin and Burgess.

PubMed

Pearson, David G

2014-03-01

Brewin and Burgess (2013) argue that our recent papers investigating the role of contextual representations in intrusive memories do not pose a challenge to dual-representation theory as originally claimed (Pearson, 2012; Pearson, Ross, & Webster, 2012). Here I point out that their alternative explanation for our results can be rejected using data already published in both papers. I also argue that their definition of what constitutes a contextual representation renders their revised dual-representation theory incompatible with experimental results that have previously been argued in the literature to support it. Valuable though their contribution is, it does not impact on our main conclusion that abstract contextual representations serve to increase intrusive memories for traumatic material. Copyright © 2013 Elsevier Ltd. All rights reserved.

Conclusiveness of natural languages and recognition of images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wojcik, Z.M.

1983-01-01

The conclusiveness is investigated using recognition processes and one-one correspondence between expressions of a natural language and graphs representing events. The graphs, as conceived in psycholinguistics, are obtained as a result of perception processes. It is possible to generate and process the graphs automatically, using computers and then to convert the resulting graphs into expressions of a natural language. Correctness and conclusiveness of the graphs and sentences are investigated using the fundamental condition for events representation processes. Some consequences of the conclusiveness are discussed, e.g. undecidability of arithmetic, human brain assymetry, correctness of statistical calculations and operations research. It ismore » suggested that the group theory should be imposed on mathematical models of any real system. Proof of the fundamental condition is also presented. 14 references.« less

The partition dimension of cycle books graph

NASA Astrophysics Data System (ADS)

Santoso, Jaya; Darmaji

2018-03-01

Let G be a nontrivial and connected graph with vertex set V(G), edge set E(G) and S ⊆ V(G) with v ∈ V(G), the distance between v and S is d(v,S) = min{d(v,x)|x ∈ S}. For an ordered partition ∏ = {S 1, S 2, S 3,…, Sk } of V(G), the representation of v with respect to ∏ is defined by r(v|∏) = (d(v, S 1), d(v, S 2),…, d(v, Sk )). The partition ∏ is called a resolving partition of G if all representations of vertices are distinct. The partition dimension pd(G) is the smallest integer k such that G has a resolving partition set with k members. In this research, we will determine the partition dimension of Cycle Books {B}{Cr,m}. Cycle books graph {B}{Cr,m} is a graph consisting of m copies cycle Cr with the common path P 2. It is shown that the partition dimension of cycle books graph, pd({B}{C3,m}) is 3 for m = 2, 3, and m for m ≥ 4. pd({B}{C4,m}) is 3 + 2k for m = 3k + 2, 4 + 2(k ‑ 1) for m = 3k + 1, and 3 + 2(k ‑ 1) for m = 3k. pd({B}{C5,m}) is m + 1.

Data mining the EXFOR database

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, David A.; Hirdt, John; Herman, Michal

2013-12-13

The EXFOR database contains the largest collection of experimental nuclear reaction data available as well as this data's bibliographic information and experimental details. We created an undirected graph from the EXFOR datasets with graph nodes representing single observables and graph links representing the connections of various types between these observables. This graph is an abstract representation of the connections in EXFOR, similar to graphs of social networks, authorship networks, etc. Analysing this abstract graph, we are able to address very specific questions such as 1) what observables are being used as reference measurements by the experimental community? 2) are thesemore » observables given the attention needed by various standards organisations? 3) are there classes of observables that are not connected to these reference measurements? In addressing these questions, we propose several (mostly cross section) observables that should be evaluated and made into reaction reference standards.« less

EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

DOE Office of Scientific and Technical Information (OSTI.GOV)

2015-01-16

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graph mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution,more » diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'« less

Disease management research using event graphs.

PubMed

Allore, H G; Schruben, L W

2000-08-01

Event Graphs, conditional representations of stochastic relationships between discrete events, simulate disease dynamics. In this paper, we demonstrate how Event Graphs, at an appropriate abstraction level, also extend and organize scientific knowledge about diseases. They can identify promising treatment strategies and directions for further research and provide enough detail for testing combinations of new medicines and interventions. Event Graphs can be enriched to incorporate and validate data and test new theories to reflect an expanding dynamic scientific knowledge base and establish performance criteria for the economic viability of new treatments. To illustrate, an Event Graph is developed for mastitis, a costly dairy cattle disease, for which extensive scientific literature exists. With only a modest amount of imagination, the methodology presented here can be seen to apply modeling to any disease, human, plant, or animal. The Event Graph simulation presented here is currently being used in research and in a new veterinary epidemiology course. Copyright 2000 Academic Press.

Representing k-graphs as Matrix Algebras

NASA Astrophysics Data System (ADS)

Rosjanuardi, R.

2018-05-01

For any commutative unital ring R and finitely aligned k-graph Λ with |Λ| < ∞ without cycles, we can realise Kumjian-Pask algebra KP R (Λ) as a direct sum of of matrix algebra over some vertices v with properties ν = νΛ, i.e: ⊕ νΛ=ν M |Λv|(R). When there is only a single vertex ν ∈ Λ° such that ν = νΛ, we can realise the Kumjian-Pask algebra as the matrix algebra M |ΛV|(R). Hence the matrix algebra M |vΛ|(R) can be regarded as a representation of the k-graph Λ. In this talk we will figure out the relation between finitely aligned k-graph and matrix algebra.

Geospatial-temporal semantic graph representations of trajectories from remote sensing and geolocation data

DOEpatents

Perkins, David Nikolaus; Brost, Randolph; Ray, Lawrence P.

2017-08-08

Various technologies for facilitating analysis of large remote sensing and geolocation datasets to identify features of interest are described herein. A search query can be submitted to a computing system that executes searches over a geospatial temporal semantic (GTS) graph to identify features of interest. The GTS graph comprises nodes corresponding to objects described in the remote sensing and geolocation datasets, and edges that indicate geospatial or temporal relationships between pairs of nodes in the nodes. Trajectory information is encoded in the GTS graph by the inclusion of movable nodes to facilitate searches for features of interest in the datasets relative to moving objects such as vehicles.

Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Utilization of Statistical Data and Domain Knowledge in Complex Cases.

PubMed

Zhang, Qin; Yao, Quanying

2018-05-01

The dynamic uncertain causality graph (DUCG) is a newly presented framework for uncertain causality representation and probabilistic reasoning. It has been successfully applied to online fault diagnoses of large, complex industrial systems, and decease diagnoses. This paper extends the DUCG to model more complex cases than what could be previously modeled, e.g., the case in which statistical data are in different groups with or without overlap, and some domain knowledge and actions (new variables with uncertain causalities) are introduced. In other words, this paper proposes to use -mode, -mode, and -mode of the DUCG to model such complex cases and then transform them into either the standard -mode or the standard -mode. In the former situation, if no directed cyclic graph is involved, the transformed result is simply a Bayesian network (BN), and existing inference methods for BNs can be applied. In the latter situation, an inference method based on the DUCG is proposed. Examples are provided to illustrate the methodology.

Integration of heterogeneous data for classification in hyperspectral satellite imagery

NASA Astrophysics Data System (ADS)

Benedetto, J.; Czaja, W.; Dobrosotskaya, J.; Doster, T.; Duke, K.; Gillis, D.

2012-06-01

As new remote sensing modalities emerge, it becomes increasingly important to nd more suitable algorithms for fusion and integration of dierent data types for the purposes of target/anomaly detection and classication. Typical techniques that deal with this problem are based on performing detection/classication/segmentation separately in chosen modalities, and then integrating the resulting outcomes into a more complete picture. In this paper we provide a broad analysis of a new approach, based on creating fused representations of the multi- modal data, which then can be subjected to analysis by means of the state-of-the-art classiers or detectors. In this scenario we shall consider the hyperspectral imagery combined with spatial information. Our approach involves machine learning techniques based on analysis of joint data-dependent graphs and their associated diusion kernels. Then, the signicant eigenvectors of the derived fused graph Laplace operator form the new representation, which provides integrated features from the heterogeneous input data. We compare these fused approaches with analysis of integrated outputs of spatial and spectral graph methods.

Towards a Script-Based Representation Language for Educational Films.

ERIC Educational Resources Information Center

Parkes, Alan P.

1987-01-01

Discusses aspects of the syntax and semantics of film, and presents a scenario for the use of film by intelligent computer assisted instruction (ICAI) systems. An outline of a representation language for educational films on videodisc is presented, and an appendix provides conceptual graphs that explain notations used in examples. (Author/LRW)

External Visual Representations in Science Learning: The Case of Relations among System Components

ERIC Educational Resources Information Center

Eilam, Billie; Poyas, Yael

2010-01-01

How do external visual representations (e.g., graph, diagram) promote or constrain students' ability to identify system components and their interrelations, to reinforce a systemic view through the application of the STS approach? University students (N = 150) received information cards describing cellphones' communication system and its subsystem…

Toward a Social Practice Perspective on the Work of Reading Inscriptions in Science Texts

ERIC Educational Resources Information Center

Pozzer-Ardenghi, Lilian; Roth, Wolff-Michael

2010-01-01

In the social studies of science, visuals and graphical representations are theorized by means of the concept of inscription, a term that denotes all representations other than text inscribed in some medium including graphs, tables, photographs, and equations. Inscriptions constitute an intrinsic and integral part of scientific practice; their…

Representational Practices in Extra-Curricular Science Inquiry Projects: A Study with Asian Primary Pupils

ERIC Educational Resources Information Center

So, Winnie W. M.

2016-01-01

Data collection, organization, and analysis are indispensable means of seeking solutions during the process of inquiry. Representations (called inscriptions by some educators) including graphs, tables, photographs, and equations are powerful ways of arranging and displaying data in visual form. This study aims to analyze quantitatively the use of…

Visual graph query formulation and exploration: a new perspective on information retrieval at the edge

NASA Astrophysics Data System (ADS)

Kase, Sue E.; Vanni, Michelle; Knight, Joanne A.; Su, Yu; Yan, Xifeng

2016-05-01

Within operational environments decisions must be made quickly based on the information available. Identifying an appropriate knowledge base and accurately formulating a search query are critical tasks for decision-making effectiveness in dynamic situations. The spreading of graph data management tools to access large graph databases is a rapidly emerging research area of potential benefit to the intelligence community. A graph representation provides a natural way of modeling data in a wide variety of domains. Graph structures use nodes, edges, and properties to represent and store data. This research investigates the advantages of information search by graph query initiated by the analyst and interactively refined within the contextual dimensions of the answer space toward a solution. The paper introduces SLQ, a user-friendly graph querying system enabling the visual formulation of schemaless and structureless graph queries. SLQ is demonstrated with an intelligence analyst information search scenario focused on identifying individuals responsible for manufacturing a mosquito-hosted deadly virus. The scenario highlights the interactive construction of graph queries without prior training in complex query languages or graph databases, intuitive navigation through the problem space, and visualization of results in graphical format.

Cell nuclei attributed relational graphs for efficient representation and classification of gastric cancer in digital histopathology

NASA Astrophysics Data System (ADS)

Sharma, Harshita; Zerbe, Norman; Heim, Daniel; Wienert, Stephan; Lohmann, Sebastian; Hellwich, Olaf; Hufnagl, Peter

2016-03-01

This paper describes a novel graph-based method for efficient representation and subsequent classification in histological whole slide images of gastric cancer. Her2/neu immunohistochemically stained and haematoxylin and eosin stained histological sections of gastric carcinoma are digitized. Immunohistochemical staining is used in practice by pathologists to determine extent of malignancy, however, it is laborious to visually discriminate the corresponding malignancy levels in the more commonly used haematoxylin and eosin stain, and this study attempts to solve this problem using a computer-based method. Cell nuclei are first isolated at high magnification using an automatic cell nuclei segmentation strategy, followed by construction of cell nuclei attributed relational graphs of the tissue regions. These graphs represent tissue architecture comprehensively, as they contain information about cell nuclei morphology as vertex attributes, along with knowledge of neighborhood in the form of edge linking and edge attributes. Global graph characteristics are derived and ensemble learning is used to discriminate between three types of malignancy levels, namely, non-tumor, Her2/neu positive tumor and Her2/neu negative tumor. Performance is compared with state of the art methods including four texture feature groups (Haralick, Gabor, Local Binary Patterns and Varma Zisserman features), color and intensity features, and Voronoi diagram and Delaunay triangulation. Texture, color and intensity information is also combined with graph-based knowledge, followed by correlation analysis. Quantitative assessment is performed using two cross validation strategies. On investigating the experimental results, it can be concluded that the proposed method provides a promising way for computer-based analysis of histopathological images of gastric cancer.

Multi-representation ability of students on the problem solving physics

NASA Astrophysics Data System (ADS)

Theasy, Y.; Wiyanto; Sujarwata

2018-03-01

Accuracy in representing knowledge possessed by students will show how the level of student understanding. The multi-representation ability of students on the problem solving of physics has been done through qualitative method of grounded theory model and implemented on physics education student of Unnes academic year 2016/2017. Multiforms of representation used are verbal (V), images/diagrams (D), graph (G), and mathematically (M). High and low category students have an accurate use of graphical representation (G) of 83% and 77.78%, and medium category has accurate use of image representation (D) equal to 66%.

Students' Meaning Making in Science: Solving Energy Resource Problems in Virtual Worlds Combined with Spreadsheets to Develop Graphs

ERIC Educational Resources Information Center

Krange, Ingeborg; Arnseth, Hans Christian

2012-01-01

The aim of this study is to scrutinize the characteristics of conceptual meaning making when students engage with virtual worlds in combination with a spreadsheet with the aim to develop graphs. We study how these tools and the representations they contain or enable students to construct serve to influence their understanding of energy resource…

Dual coding: a cognitive model for psychoanalytic research.

PubMed

Bucci, W

1985-01-01

Four theories of mental representation derived from current experimental work in cognitive psychology have been discussed in relation to psychoanalytic theory. These are: verbal mediation theory, in which language determines or mediates thought; perceptual dominance theory, in which imagistic structures are dominant; common code or propositional models, in which all information, perceptual or linguistic, is represented in an abstract, amodal code; and dual coding, in which nonverbal and verbal information are each encoded, in symbolic form, in separate systems specialized for such representation, and connected by a complex system of referential relations. The weight of current empirical evidence supports the dual code theory. However, psychoanalysis has implicitly accepted a mixed model-perceptual dominance theory applying to unconscious representation, and verbal mediation characterizing mature conscious waking thought. The characterization of psychoanalysis, by Schafer, Spence, and others, as a domain in which reality is constructed rather than discovered, reflects the application of this incomplete mixed model. The representations of experience in the patient's mind are seen as without structure of their own, needing to be organized by words, thus vulnerable to distortion or dissolution by the language of the analyst or the patient himself. In these terms, hypothesis testing becomes a meaningless pursuit; the propositions of the theory are no longer falsifiable; the analyst is always more or less "right." This paper suggests that the integrated dual code formulation provides a more coherent theoretical framework for psychoanalysis than the mixed model, with important implications for theory and technique. In terms of dual coding, the problem is not that the nonverbal representations are vulnerable to distortion by words, but that the words that pass back and forth between analyst and patient will not affect the nonverbal schemata at all. Using the dual code formulation, and applying an investigative methodology derived from experimental cognitive psychology, a new approach to the verification of interpretations is possible. Some constructions of a patient's story may be seen as more accurate than others, by virtue of their linkage to stored perceptual representations in long-term memory. We can demonstrate that such linking has occurred in functional or operational terms--through evaluating the representation of imagistic content in the patient's speech.

On the dual equivalence of the self-dual and topologically massive /B∧F models coupled to dynamical fermionic matter

NASA Astrophysics Data System (ADS)

Menezes, R.; Nascimento, J. R. S.; Ribeiro, R. F.; Wotzasek, C.

2002-06-01

We study the equivalence between the /B∧F self-dual (SDB∧F) and the /B∧F topologically massive (TMB∧F) models including the coupling to dynamical, U(1) charged fermionic matter. This is done through an iterative procedure of gauge embedding that produces the dual mapping. In the interactive cases, the minimal coupling adopted for both vector and tensor fields in the self-dual representation is transformed into a non-minimal magnetic like coupling in the topologically massive representation but with the currents swapped. It is known that to establish this equivalence a current-current interaction term is needed to render the matter sector unchanged. We show that both terms arise naturally from the embedding procedure.

Effective numerical method of spectral analysis of quantum graphs

NASA Astrophysics Data System (ADS)

Barrera-Figueroa, Víctor; Rabinovich, Vladimir S.

2017-05-01

We present in the paper an effective numerical method for the determination of the spectra of periodic metric graphs equipped by Schrödinger operators with real-valued periodic electric potentials as Hamiltonians and with Kirchhoff and Neumann conditions at the vertices. Our method is based on the spectral parameter power series method, which leads to a series representation of the dispersion equation, which is suitable for both analytical and numerical calculations. Several important examples demonstrate the effectiveness of our method for some periodic graphs of interest that possess potentials usually found in quantum mechanics.

One-dimensional swarm algorithm packaging

NASA Astrophysics Data System (ADS)

Lebedev, Boris K.; Lebedev, Oleg B.; Lebedeva, Ekaterina O.

2018-05-01

The paper considers an algorithm for solving the problem of onedimensional packaging based on the adaptive behavior model of an ant colony. The key role in the development of the ant algorithm is the choice of representation (interpretation) of the solution. The structure of the solution search graph, the procedure for finding solutions on the graph, the methods of deposition and evaporation of pheromone are described. Unlike the canonical paradigm of an ant algorithm, an ant on the solution search graph generates sets of elements distributed across blocks. Experimental studies were conducted on IBM PC. Compared with the existing algorithms, the results are improved.

Graph C ∗-algebras and Z2-quotients of quantum spheres

NASA Astrophysics Data System (ADS)

Hajac, Piotr M.; Matthes, Rainer; Szymański, Wojciech

2003-06-01

We consider two Z2-actions on the Podleś generic quantum spheres. They yield, as noncommutative quotient spaces, the Klimek-Lesmewski q-disc and the quantum real projective space, respectively. The C ∗-algebas of all these quantum spaces are described as graph C ∗-algebras. The K-groups of the thus presented C ∗-algebras are then easily determined from the general theory of graph C ∗-algebas. For the quantum real projective space, we also recall the classification of the classes of irreducible ∗-representations of its algebra and give a linear basis for this algebra.

Reading Mathematics Representations: An Eye-Tracking Study

ERIC Educational Resources Information Center

Andrá, Chiara; Lindström, Paulina; Arzarello, Ferdinando; Holmqvist, Kenneth; Robutti, Ornella; Sabena, Cristina

2015-01-01

We use eye tracking as a method to examine how different mathematical representations of the same mathematical object are attended to by students. The results of this study show that there is a meaningful difference in the eye movements between formulas and graphs. This difference can be understood in terms of the cultural and social shaping of…

Novice Interpretations of Visual Representations of Geosciences Data

NASA Astrophysics Data System (ADS)

Burkemper, L. K.; Arthurs, L.

2013-12-01

Past cognition research of individual's perception and comprehension of bar and line graphs are substantive enough that they have resulted in the generation of graph design principles and graph comprehension theories; however, gaps remain in our understanding of how people process visual representations of data, especially of geologic and atmospheric data. This pilot project serves to build on others' prior research and begin filling the existing gaps. The primary objectives of this pilot project include: (i) design a novel data collection protocol based on a combination of paper-based surveys, think-aloud interviews, and eye-tracking tasks to investigate student data handling skills of simple to complex visual representations of geologic and atmospheric data, (ii) demonstrate that the protocol yields results that shed light on student data handling skills, and (iii) generate preliminary findings upon which tentative but perhaps helpful recommendations on how to more effectively present these data to the non-scientist community and teach essential data handling skills. An effective protocol for the combined use of paper-based surveys, think-aloud interviews, and computer-based eye-tracking tasks for investigating cognitive processes involved in perceiving, comprehending, and interpreting visual representations of geologic and atmospheric data is instrumental to future research in this area. The outcomes of this pilot study provide the foundation upon which future more in depth and scaled up investigations can build. Furthermore, findings of this pilot project are sufficient for making, at least, tentative recommendations that can help inform (i) the design of physical attributes of visual representations of data, especially more complex representations, that may aid in improving students' data handling skills and (ii) instructional approaches that have the potential to aid students in more effectively handling visual representations of geologic and atmospheric data that they might encounter in a course, television news, newspapers and magazines, and websites. Such recommendations would also be the potential subject of future investigations and have the potential to impact the design features when data is presented to the public and instructional strategies not only in geoscience courses but also other science, technology, engineering, and mathematics (STEM) courses.

Mathematization of experience in a grade 8 open-inquiry environment: An introduction to the representational practices of science

NASA Astrophysics Data System (ADS)

Roth, Wolff-Michael; Bowen, G. Michael

The purpose of this classroom study was to investigate the use of mathematical representations in three Grade 8 general science classes that engaged in a 10-week open inquiry about the correlations between biological and physical variables in the environment. A constructivist perspective was used to design the study and to assemble the data sources. These data sources included videotapes of students in their work, audiotapes of teacher-student interactions and teacher interviews, the transcripts of these tapes, the students' field notebooks, field reports, special problem assignments, examinations, and the teachers' curriculum guides, field notes, and reflective journal. An interpretive method was used to construct assertions and the supporting data. In the setting provided, students increasingly used mathematical representations such as graphs and data tables to support their claims in a convincing manner; the use of abstract equations and percent calculations did not change over the course of the study. Representations such as graphs, maps, averages, and equations were not only useful as inscriptions (representations in some permanent medium, usually paper), but also as conscription devices in the construction of, and through which, students engaged each other to collaboratively construct meaning. This study demonstrates the use of representations as conscription devices, and illustrates how the use and understanding of inscriptions changes over time. Understanding representations as inscription and conscription devices focuses on the social aspects of knowing, which has important implications for teachers' conceptualization of learning and their organization of science classrooms.

Strings in bubbling geometries and dual Wilson loop correlators

NASA Astrophysics Data System (ADS)

Aguilera-Damia, Jeremías; Correa, Diego H.; Fucito, Francesco; Giraldo-Rivera, Victor I.; Morales, Jose F.; Pando Zayas, Leopoldo A.

2017-12-01

We consider a fundamental string in a bubbling geometry of arbitrary genus dual to a half-supersymmetric Wilson loop in a general large representation R of the SU( N) gauge group in N=4 Supersymmetric Yang-Mills. We demonstrate, under some mild conditions, that the minimum value of the string classical action for a bubbling geometry of arbitrary genus precisely matches the correlator of a Wilson loop in the fundamental representation and one in a general large representation. We work out the case in which the large representation is given by a rectangular Young tableau, corresponding to a genus one bubbling geometry, explicitly. We also present explicit results in the field theory for a correlator of two Wilson loops: a large one in an arbitrary representation and a "small" one in the fundamental, totally symmetric or totally antisymmetric representation.

Dual PECCS: a cognitive system for conceptual representation and categorization

NASA Astrophysics Data System (ADS)

Lieto, Antonio; Radicioni, Daniele P.; Rho, Valentina

2017-03-01

In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual structures represented as heterogeneous proxytypes. Dual-PECCS has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies, and its output has been compared to human responses. The obtained results suggest that our approach can be beneficial to improve the representational and reasoning conceptual capabilities of standard cognitive artificial systems, and - in addition - that it may be plausibly applied to different general computational models of cognition. The current version of the system, in fact, extends our previous work, in that Dual- PECCS is now integrated and tested into two cognitive architectures, ACT-R and CLARION, implementing different assumptions on the underlying invariant structures governing human cognition. Such integration allowed us to extend our previous evaluation.

A new network representation of the metabolism to detect chemical transformation modules.

PubMed

Sorokina, Maria; Medigue, Claudine; Vallenet, David

2015-11-14

Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS). RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation. This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.

What Students Choose to Do and Have to Say about Use of Multiple Representations in College Algebra

ERIC Educational Resources Information Center

Herman, Marlena

2007-01-01

This report summarizes findings on strategies chosen by students (n=38) when solving algebra problems related to various functions with the freedom to use a TI-83 graphing calculator, influences on student problem-solving strategy choices, student ability to approach algebra problems with use of multiple representations, and student beliefs on how…

Attributing Meanings to Representations of Data: The Case of Statistical Process Control

ERIC Educational Resources Information Center

Hoyles, Celia; Bakker, Arthur; Kent, Phillip; Noss, Richard

2007-01-01

This article is concerned with the meanings that employees in industry attribute to representations of data and the contingencies of these meanings in context. Our primary concern is to more precisely characterize how the context of the industrial process is constitutive of the meaning of graphs of data derived from this process. We draw on data…

Network representation of protein interactions: Theory of graph description and analysis.

PubMed

Kurzbach, Dennis

2016-09-01

A methodological framework is presented for the graph theoretical interpretation of NMR data of protein interactions. The proposed analysis generalizes the idea of network representations of protein structures by expanding it to protein interactions. This approach is based on regularization of residue-resolved NMR relaxation times and chemical shift data and subsequent construction of an adjacency matrix that represents the underlying protein interaction as a graph or network. The network nodes represent protein residues. Two nodes are connected if two residues are functionally correlated during the protein interaction event. The analysis of the resulting network enables the quantification of the importance of each amino acid of a protein for its interactions. Furthermore, the determination of the pattern of correlations between residues yields insights into the functional architecture of an interaction. This is of special interest for intrinsically disordered proteins, since the structural (three-dimensional) architecture of these proteins and their complexes is difficult to determine. The power of the proposed methodology is demonstrated at the example of the interaction between the intrinsically disordered protein osteopontin and its natural ligand heparin. © 2016 The Protein Society.

A Development Testbed for ALPS-Based Systems

DTIC Science & Technology

1988-10-01

alloted to tile application because of size or power constraints). Given an underlying support ALPS architecture such as the d-ALPS architecture, a...resource on which it is assigned at runtime. A second representation problem is that most graph analysis algorithms treat either graphs with weighted links...subtask) associated with it but is treated like other links. In d-ALPS, as a priority precedence link, it would cause the binding of a pro- cessor: as a

A Scalable Distributed Syntactic, Semantic, and Lexical Language Model

DTIC Science & Technology

2012-09-01

Here pa(τ) denotes the set of parent states of τ. If the recursive factorization refers to a graph , then we have a Bayesian network (Lauritzen 1996...Broadly speaking, however, the recursive factorization can refer to a representation more complicated than a graph with a fixed set of nodes and edges...factored language (FL) model (Bilmes and Kirchhoff 2003) is close to the smoothing technique we propose here, the major difference is that FL

Discrete Methods and their Applications

DTIC Science & Technology

1993-02-03

problem of finding all near-optimal solutions to a linear program. In paper [18], we give a brief and elementary proof of a result of Hoffman [1952) about...relies only on linear programming duality; second, we obtain geometric and algebraic representations of the bounds that are determined explicitly in...same. We have studied the problem of finding the minimum n such that a given unit interval graph is an n--graph. A linear time algorithm to compute

Symposium on Chemical Applications of Topology and Graph Theory, April 18-22, 1983.

DTIC Science & Technology

1983-04-01

illustrated by application to the Lotka - Volterra oscillator. ELECTRICAL NETWORK REPRESENTATION OF n-DIMENSIONAL CHEMICAL MANIFOLDS L. Peusner P.O. Box 380...like molecules and others; the original formulas by Cayley were extended by Polya in a general enumeration theorem, simplified by Otter, and also studied...Gutman, leading to a joint paper which generalized it, using line graphs. Finally, electroneqativity consider- ations tell the strength of a chemical

Review on Graph Clustering and Subgraph Similarity Based Analysis of Neurological Disorders

PubMed Central

Thomas, Jaya; Seo, Dongmin; Sael, Lee

2016-01-01

How can complex relationships among molecular or clinico-pathological entities of neurological disorders be represented and analyzed? Graphs seem to be the current answer to the question no matter the type of information: molecular data, brain images or neural signals. We review a wide spectrum of graph representation and graph analysis methods and their application in the study of both the genomic level and the phenotypic level of the neurological disorder. We find numerous research works that create, process and analyze graphs formed from one or a few data types to gain an understanding of specific aspects of the neurological disorders. Furthermore, with the increasing number of data of various types becoming available for neurological disorders, we find that integrative analysis approaches that combine several types of data are being recognized as a way to gain a global understanding of the diseases. Although there are still not many integrative analyses of graphs due to the complexity in analysis, multi-layer graph analysis is a promising framework that can incorporate various data types. We describe and discuss the benefits of the multi-layer graph framework for studies of neurological disease. PMID:27258269

Review on Graph Clustering and Subgraph Similarity Based Analysis of Neurological Disorders.

PubMed

Thomas, Jaya; Seo, Dongmin; Sael, Lee

2016-06-01

How can complex relationships among molecular or clinico-pathological entities of neurological disorders be represented and analyzed? Graphs seem to be the current answer to the question no matter the type of information: molecular data, brain images or neural signals. We review a wide spectrum of graph representation and graph analysis methods and their application in the study of both the genomic level and the phenotypic level of the neurological disorder. We find numerous research works that create, process and analyze graphs formed from one or a few data types to gain an understanding of specific aspects of the neurological disorders. Furthermore, with the increasing number of data of various types becoming available for neurological disorders, we find that integrative analysis approaches that combine several types of data are being recognized as a way to gain a global understanding of the diseases. Although there are still not many integrative analyses of graphs due to the complexity in analysis, multi-layer graph analysis is a promising framework that can incorporate various data types. We describe and discuss the benefits of the multi-layer graph framework for studies of neurological disease.

SimGraph: A Flight Simulation Data Visualization Workstation

NASA Technical Reports Server (NTRS)

Kaplan, Joseph A.; Kenney, Patrick S.

1997-01-01

Today's modern flight simulation research produces vast amounts of time sensitive data, making a qualitative analysis of the data difficult while it remains in a numerical representation. Therefore, a method of merging related data together and presenting it to the user in a more comprehensible format is necessary. Simulation Graphics (SimGraph) is an object-oriented data visualization software package that presents simulation data in animated graphical displays for easy interpretation. Data produced from a flight simulation is presented by SimGraph in several different formats, including: 3-Dimensional Views, Cockpit Control Views, Heads-Up Displays, Strip Charts, and Status Indicators. SimGraph can accommodate the addition of new graphical displays to allow the software to be customized to each user s particular environment. A new display can be developed and added to SimGraph without having to design a new application, allowing the graphics programmer to focus on the development of the graphical display. The SimGraph framework can be reused for a wide variety of visualization tasks. Although it was created for the flight simulation facilities at NASA Langley Research Center, SimGraph can be reconfigured to almost any data visualization environment. This paper describes the capabilities and operations of SimGraph.

Integrated VR platform for 3D and image-based models: a step toward interactive image-based virtual environments

NASA Astrophysics Data System (ADS)

Yoon, Jayoung; Kim, Gerard J.

2003-04-01

Traditionally, three dimension models have been used for building virtual worlds, and a data structure called the "scene graph" is often employed to organize these 3D objects in the virtual space. On the other hand, image-based rendering has recently been suggested as a probable alternative VR platform for its photo-realism, however, due to limited interactivity, it has only been used for simple navigation systems. To combine the merits of these two approaches to object/scene representations, this paper proposes for a scene graph structure in which both 3D models and various image-based scenes/objects can be defined, traversed, and rendered together. In fact, as suggested by Shade et al., these different representations can be used as different LOD's for a given object. For instance, an object might be rendered using a 3D model at close range, a billboard at an intermediate range, and as part of an environment map at far range. The ultimate objective of this mixed platform is to breath more interactivity into the image based rendered VE's by employing 3D models as well. There are several technical challenges in devising such a platform: designing scene graph nodes for various types of image based techniques, establishing criteria for LOD/representation selection, handling their transitions, implementing appropriate interaction schemes, and correctly rendering the overall scene. Currently, we have extended the scene graph structure of the Sense8's WorldToolKit, to accommodate new node types for environment maps billboards, moving textures and sprites, "Tour-into-the-Picture" structure, and view interpolated objects. As for choosing the right LOD level, the usual viewing distance and image space criteria are used, however, the switching between the image and 3D model occurs at a distance from the user where the user starts to perceive the object's internal depth. Also, during interaction, regardless of the viewing distance, a 3D representation would be used, it if exists. Before rendering, objects are conservatively culled from the view frustum using the representation with the largest volume. Finally, we carried out experiments to verify the theoretical derivation of the switching rule and obtained positive results.

The Ponzano-Regge Model and Parametric Representation

NASA Astrophysics Data System (ADS)

Li, Dan

2014-04-01

We give a parametric representation of the effective noncommutative field theory derived from a -deformation of the Ponzano-Regge model and define a generalized Kirchhoff polynomial with -correction terms, obtained in a -linear approximation. We then consider the corresponding graph hypersurfaces and the question of how the presence of the correction term affects their motivic nature. We look in particular at the tetrahedron graph, which is the basic case of relevance to quantum gravity. With the help of computer calculations, we verify that the number of points over finite fields of the corresponding hypersurface does not fit polynomials with integer coefficients, hence the hypersurface of the tetrahedron is not polynomially countable. This shows that the correction term can change significantly the motivic properties of the hypersurfaces, with respect to the classical case.

Strings in bubbling geometries and dual Wilson loop correlators

DOE PAGES

Aguilera-Damia, Jeremias; Correa, Diego H.; Fucito, Francesco; ...

2017-12-20

We consider a fundamental string in a bubbling geometry of arbitrary genus dual to a half-supersymmetric Wilson loop in a general large representation R of the SU(N) gauge group in N = 4 Supersymmetric Yang-Mills. We demonstrate, under some mild conditions, that the minimum value of the string classical action for a bubbling geometry of arbitrary genus precisely matches the correlator of a Wilson loop in the fundamental representation and one in a general large representation. We work out the case in which the large representation is given by a rectangular Young tableau, corresponding to a genus one bubbling geometry,more » explicitly. Lastly, we also present explicit results in the field theory for a correlator of two Wilson loops: a large one in an arbitrary representation and a “small” one in the fundamental, totally symmetric or totally antisymmetric representation.« less

Strings in bubbling geometries and dual Wilson loop correlators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aguilera-Damia, Jeremias; Correa, Diego H.; Fucito, Francesco

We consider a fundamental string in a bubbling geometry of arbitrary genus dual to a half-supersymmetric Wilson loop in a general large representation R of the SU(N) gauge group in N = 4 Supersymmetric Yang-Mills. We demonstrate, under some mild conditions, that the minimum value of the string classical action for a bubbling geometry of arbitrary genus precisely matches the correlator of a Wilson loop in the fundamental representation and one in a general large representation. We work out the case in which the large representation is given by a rectangular Young tableau, corresponding to a genus one bubbling geometry,more » explicitly. Lastly, we also present explicit results in the field theory for a correlator of two Wilson loops: a large one in an arbitrary representation and a “small” one in the fundamental, totally symmetric or totally antisymmetric representation.« less

Graph Analytics for Signature Discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hogan, Emilie A.; Johnson, John R.; Halappanavar, Mahantesh

2013-06-01

Within large amounts of seemingly unstructured data it can be diffcult to find signatures of events. In our work we transform unstructured data into a graph representation. By doing this we expose underlying structure in the data and can take advantage of existing graph analytics capabilities, as well as develop new capabilities. Currently we focus on applications in cybersecurity and communication domains. Within cybersecurity we aim to find signatures for perpetrators using the pass-the-hash attack, and in communications we look for emails or phone calls going up or down a chain of command. In both of these areas, and inmore » many others, the signature we look for is a path with certain temporal properties. In this paper we discuss our methodology for finding these temporal paths within large graphs.« less

A graph-theoretical representation of multiphoton resonance processes in superconducting quantum circuits

DOE PAGES

Jooya, Hossein Z.; Reihani, Kamran; Chu, Shih-I

2016-11-21

We propose a graph-theoretical formalism to study generic circuit quantum electrodynamics systems consisting of a two level qubit coupled with a single-mode resonator in arbitrary coupling strength regimes beyond rotating-wave approximation. We define colored-weighted graphs, and introduce different products between them to investigate the dynamics of superconducting qubits in transverse, longitudinal, and bidirectional coupling schemes. In conclusion, the intuitive and predictive picture provided by this method, and the simplicity of the mathematical construction, are demonstrated with some numerical studies of the multiphoton resonance processes and quantum interference phenomena for the superconducting qubit systems driven by intense ac fields.

Application of dynamic uncertain causality graph in spacecraft fault diagnosis: Logic cycle

NASA Astrophysics Data System (ADS)

Yao, Quanying; Zhang, Qin; Liu, Peng; Yang, Ping; Zhu, Ma; Wang, Xiaochen

2017-04-01

Intelligent diagnosis system are applied to fault diagnosis in spacecraft. Dynamic Uncertain Causality Graph (DUCG) is a new probability graphic model with many advantages. In the knowledge expression of spacecraft fault diagnosis, feedback among variables is frequently encountered, which may cause directed cyclic graphs (DCGs). Probabilistic graphical models (PGMs) such as bayesian network (BN) have been widely applied in uncertain causality representation and probabilistic reasoning, but BN does not allow DCGs. In this paper, DUGG is applied to fault diagnosis in spacecraft: introducing the inference algorithm for the DUCG to deal with feedback. Now, DUCG has been tested in 16 typical faults with 100% diagnosis accuracy.

Edge length dynamics on graphs with applications to p-adic AdS/CFT

DOE PAGES

Gubser, Steven S.; Heydeman, Matthew; Jepsen, Christian; ...

2017-06-30

We formulate a Euclidean theory of edge length dynamics based on a notion of Ricci curvature on graphs with variable edge lengths. In order to write an explicit form for the discrete analog of the Einstein-Hilbert action, we require that the graph should either be a tree or that all its cycles should be sufficiently long. The infinite regular tree with all edge lengths equal is an example of a graph with constant negative curvature, providing a connection with p-adic AdS/CFT, where such a tree takes the place of anti-de Sitter space. Here, we compute simple correlators of the operatormore » holographically dual to edge length fluctuations. This operator has dimension equal to the dimension of the boundary, and it has some features in common with the stress tensor.« less

Edge length dynamics on graphs with applications to p-adic AdS/CFT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gubser, Steven S.; Heydeman, Matthew; Jepsen, Christian

We formulate a Euclidean theory of edge length dynamics based on a notion of Ricci curvature on graphs with variable edge lengths. In order to write an explicit form for the discrete analog of the Einstein-Hilbert action, we require that the graph should either be a tree or that all its cycles should be sufficiently long. The infinite regular tree with all edge lengths equal is an example of a graph with constant negative curvature, providing a connection with p-adic AdS/CFT, where such a tree takes the place of anti-de Sitter space. Here, we compute simple correlators of the operatormore » holographically dual to edge length fluctuations. This operator has dimension equal to the dimension of the boundary, and it has some features in common with the stress tensor.« less

Application of Theodorsen's Theory to Propeller Design

NASA Technical Reports Server (NTRS)

Crigler, John L

1948-01-01

A theoretical analysis is presented for obtaining by use of Theodorsen's propeller theory the load distribution along a propeller radius to give the optimum propeller efficiency for any design condition.The efficiencies realized by designing for the optimum load distribution are given in graphs, and the optimum efficiency for any design condition may be read directly from the graph without any laborious calculations. Examples are included to illustrate the method of obtaining the optimum load distributions for both single-rotating and dual-rotating propellers.

Application of Theodorsen's theory to propeller design

NASA Technical Reports Server (NTRS)

Crigler, John L

1949-01-01

A theoretical analysis is presented for obtaining, by use of Theodorsen's propeller theory, the load distribution along a propeller radius to give the optimum propeller efficiency for any design condition. The efficiencies realized by designing for the optimum load distribution are given in graphs, and the optimum efficiency for any design condition may be read directly from the graph without any laborious calculations. Examples are included to illustrate the method of obtaining the optimum load distributions for both single-rotating and dual-rotating propellers.

Motion sensors in mathematics teaching: learning tools for understanding general math concepts?

NASA Astrophysics Data System (ADS)

Urban-Woldron, Hildegard

2015-05-01

Incorporating technology tools into the mathematics classroom adds a new dimension to the teaching of mathematics concepts and establishes a whole new approach to mathematics learning. In particular, gathering data in a hands-on and real-time method helps classrooms coming alive. The focus of this paper is on bringing forward important mathematics concepts such as functions and rate of change with the motion detector. Findings from the author's studies suggest that the motion detector can be introduced from a very early age and used to enliven classes at any level. Using real-world data to present the main functions invites an experimental approach to mathematics and encourages students to engage actively in their learning. By emphasizing learning experiences with computer-based motion detectors and aiming to involve students in mathematical representations of real-world phenomena, six learning activities, which were developed in previous research studies, will be presented. Students use motion sensors to collect physical data that are graphed in real time and then manipulate and analyse them. Because data are presented in an immediately understandable graphical form, students are allowed to take an active role in their learning by constructing mathematical knowledge from observation of the physical world. By utilizing a predict-observe-explain format, students learn about slope, determining slope and distance vs. time graphs through motion-filled activities. Furthermore, exploring the meaning of slope, viewed as the rate of change, students acquire competencies for reading, understanding and interpreting kinematics graphs involving a multitude of mathematical representations. Consequently, the students are empowered to efficiently move among tabular, graphical and symbolic representation to analyse patterns and discover the relationships between different representations of motion. In fact, there is a need for further research to explore how mathematics teachers can integrate motion sensors into their classrooms.

Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.

PubMed

Crichton, Gamal; Guo, Yufan; Pyysalo, Sampo; Korhonen, Anna

2018-05-21

Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Preliminary works were done on this and report promising results. However they did not use realistic settings like time-slicing, evaluate performances with comprehensive metrics or explain when or why neural network methods outperform. We investigated how inputs from four node representation algorithms affect performance of a neural link predictor on random- and time-sliced biomedical graphs of real-world sizes (∼ 6 million edges) containing information relevant to DTI, PPI and LBD. We compared the performance of the neural link predictor to those of established baselines and report performance across five metrics. In random- and time-sliced experiments when the neural network methods were able to learn good node representations and there was a negligible amount of disconnected nodes, those approaches outperformed the baselines. In the smallest graph (∼ 15,000 edges) and in larger graphs with approximately 14% disconnected nodes, baselines such as Common Neighbours proved a justifiable choice for link prediction. At low recall levels (∼ 0.3) the approaches were mostly equal, but at higher recall levels across all nodes and average performance at individual nodes, neural network approaches were superior. Analysis showed that neural network methods performed well on links between nodes with no previous common neighbours; potentially the most interesting links. Additionally, while neural network methods benefit from large amounts of data, they require considerable amounts of computational resources to utilise them. Our results indicate that when there is enough data for the neural network methods to use and there are a negligible amount of disconnected nodes, those approaches outperform the baselines. At low recall levels the approaches are mostly equal but at higher recall levels and average performance at individual nodes, neural network approaches are superior. Performance at nodes without common neighbours which indicate more unexpected and perhaps more useful links account for this.

A topo-graph model for indistinct target boundary definition from anatomical images.

PubMed

Cui, Hui; Wang, Xiuying; Zhou, Jianlong; Gong, Guanzhong; Eberl, Stefan; Yin, Yong; Wang, Lisheng; Feng, Dagan; Fulham, Michael

2018-06-01

It can be challenging to delineate the target object in anatomical imaging when the object boundaries are difficult to discern due to the low contrast or overlapping intensity distributions from adjacent tissues. We propose a topo-graph model to address this issue. The first step is to extract a topographic representation that reflects multiple levels of topographic information in an input image. We then define two types of node connections - nesting branches (NBs) and geodesic edges (GEs). NBs connect nodes corresponding to initial topographic regions and GEs link the nodes at a detailed level. The weights for NBs are defined to measure the similarity of regional appearance, and weights for GEs are defined with geodesic and local constraints. NBs contribute to the separation of topographic regions and the GEs assist the delineation of uncertain boundaries. Final segmentation is achieved by calculating the relevance of the unlabeled nodes to the labels by the optimization of a graph-based energy function. We test our model on 47 low contrast CT studies of patients with non-small cell lung cancer (NSCLC), 10 contrast-enhanced CT liver cases and 50 breast and abdominal ultrasound images. The validation criteria are the Dice's similarity coefficient and the Hausdorff distance. Student's t-test show that our model outperformed the graph models with pixel-only, pixel and regional, neighboring and radial connections (p-values <0.05). Our findings show that the topographic representation and topo-graph model provides improved delineation and separation of objects from adjacent tissues compared to the tested models. Copyright © 2018 Elsevier B.V. All rights reserved.

Reservoir simulation with MUFITS code: Extension for double porosity reservoirs and flows in horizontal wells

NASA Astrophysics Data System (ADS)

Afanasyev, Andrey

2017-04-01

Numerical modelling of multiphase flows in porous medium is necessary in many applications concerning subsurface utilization. An incomplete list of those applications includes oil and gas fields exploration, underground carbon dioxide storage and geothermal energy production. The numerical simulations are conducted using complicated computer programs called reservoir simulators. A robust simulator should include a wide range of modelling options covering various exploration techniques, rock and fluid properties, and geological settings. In this work we present a recent development of new options in MUFITS code [1]. The first option concerns modelling of multiphase flows in double-porosity double-permeability reservoirs. We describe internal representation of reservoir models in MUFITS, which are constructed as a 3D graph of grid blocks, pipe segments, interfaces, etc. In case of double porosity reservoir, two linked nodes of the graph correspond to a grid cell. We simulate the 6th SPE comparative problem [2] and a five-spot geothermal production problem to validate the option. The second option concerns modelling of flows in porous medium coupled with flows in horizontal wells that are represented in the 3D graph as a sequence of pipe segments linked with pipe junctions. The well completions link the pipe segments with reservoir. The hydraulics in the wellbore, i.e. the frictional pressure drop, is calculated in accordance with Haaland's formula. We validate the option against the 7th SPE comparative problem [3]. We acknowledge financial support by the Russian Foundation for Basic Research (project No RFBR-15-31-20585). References [1] Afanasyev, A. MUFITS Reservoir Simulation Software (www.mufits.imec.msu.ru). [2] Firoozabadi A. et al. Sixth SPE Comparative Solution Project: Dual-Porosity Simulators // J. Petrol. Tech. 1990. V.42. N.6. P.710-715. [3] Nghiem L., et al. Seventh SPE Comparative Solution Project: Modelling of Horizontal Wells in Reservoir Simulation // SPE Symp. Res. Sim., 1991. DOI: 10.2118/21221-MS.

2D-dynamic representation of DNA sequences as a graphical tool in bioinformatics

NASA Astrophysics Data System (ADS)

Bielińska-Wa̧Ż, D.; Wa̧Ż, P.

2016-10-01

2D-dynamic representation of DNA sequences is briefly reviewed. Some new examples of 2D-dynamic graphs which are the graphical tool of the method are shown. Using the examples of the complete genome sequences of the Zika virus it is shown that the present method can be applied for the study of the evolution of viral genomes.

Effect of the Presence of External Representations on Accuracy and Reaction Time in Solving Mathematical Double-Choice Problems by Students of Different Levels of Instruction

ERIC Educational Resources Information Center

Leikin, Roza; Leikin, Mark; Waisman, Ilana; Shaul, Shelley

2013-01-01

This study explores the effects of the "presence of external representations of a mathematical object" (ERs) on problem solving performance associated with short double-choice problems. The problems were borrowed from secondary school algebra and geometry, and the ERs were either formulas, graphs of functions, or drawings of geometric…

Children's Learning from Touch Screens: A Dual Representation Perspective.

PubMed

Sheehan, Kelly J; Uttal, David H

2016-01-01

Parents and educators often expect that children will learn from touch screen devices, such as during joint e-book reading. Therefore an essential question is whether young children understand that the touch screen can be a symbolic medium - that entities represented on the touch screen can refer to entities in the real world. Research on symbolic development suggests that symbolic understanding requires that children develop dual representational abilities, meaning children need to appreciate that a symbol is an object in itself (i.e., picture of a dog) while also being a representation of something else (i.e., the real dog). Drawing on classic research on symbols and new research on children's learning from touch screens, we offer the perspective that children's ability to learn from the touch screen as a symbolic medium depends on the effect of interactivity on children's developing dual representational abilities. Although previous research on dual representation suggests the interactive nature of the touch screen might make it difficult for young children to use as a symbolic medium, the unique interactive affordances may help alleviate this difficulty. More research needs to investigate how the interactivity of the touch screen affects children's ability to connect the symbols on the screen to the real world. Given the interactive nature of the touch screen, researchers and educators should consider both the affordances of the touch screen as well as young children's cognitive abilities when assessing whether young children can learn from it as a symbolic medium.

Calculating massive 3-loop graphs for operator matrix elements by the method of hyperlogarithms

NASA Astrophysics Data System (ADS)

Ablinger, Jakob; Blümlein, Johannes; Raab, Clemens; Schneider, Carsten; Wißbrock, Fabian

2014-08-01

We calculate convergent 3-loop Feynman diagrams containing a single massive loop equipped with twist τ=2 local operator insertions corresponding to spin N. They contribute to the massive operator matrix elements in QCD describing the massive Wilson coefficients for deep-inelastic scattering at large virtualities. Diagrams of this kind can be computed using an extended version of the method of hyperlogarithms, originally being designed for massless Feynman diagrams without operators. The method is applied to Benz- and V-type graphs, belonging to the genuine 3-loop topologies. In case of the V-type graphs with five massive propagators, new types of nested sums and iterated integrals emerge. The sums are given in terms of finite binomially and inverse binomially weighted generalized cyclotomic sums, while the 1-dimensionally iterated integrals are based on a set of ∼30 square-root valued letters. We also derive the asymptotic representations of the nested sums and present the solution for N∈C. Integrals with a power-like divergence in N-space ∝aN,a∈R,a>1, for large values of N emerge. They still possess a representation in x-space, which is given in terms of root-valued iterated integrals in the present case. The method of hyperlogarithms is also used to calculate higher moments for crossed box graphs with different operator insertions.

Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structure-activity relationships.

PubMed

Ivanciuc, Ovidiu

2013-06-01

Chemical and molecular graphs have fundamental applications in chemoinformatics, quantitative structureproperty relationships (QSPR), quantitative structure-activity relationships (QSAR), virtual screening of chemical libraries, and computational drug design. Chemoinformatics applications of graphs include chemical structure representation and coding, database search and retrieval, and physicochemical property prediction. QSPR, QSAR and virtual screening are based on the structure-property principle, which states that the physicochemical and biological properties of chemical compounds can be predicted from their chemical structure. Such structure-property correlations are usually developed from topological indices and fingerprints computed from the molecular graph and from molecular descriptors computed from the three-dimensional chemical structure. We present here a selection of the most important graph descriptors and topological indices, including molecular matrices, graph spectra, spectral moments, graph polynomials, and vertex topological indices. These graph descriptors are used to define several topological indices based on molecular connectivity, graph distance, reciprocal distance, distance-degree, distance-valency, spectra, polynomials, and information theory concepts. The molecular descriptors and topological indices can be developed with a more general approach, based on molecular graph operators, which define a family of graph indices related by a common formula. Graph descriptors and topological indices for molecules containing heteroatoms and multiple bonds are computed with weighting schemes based on atomic properties, such as the atomic number, covalent radius, or electronegativity. The correlation in QSPR and QSAR models can be improved by optimizing some parameters in the formula of topological indices, as demonstrated for structural descriptors based on atomic connectivity and graph distance.

Clique-Based Neural Associative Memories with Local Coding and Precoding.

PubMed

Mofrad, Asieh Abolpour; Parker, Matthew G; Ferdosi, Zahra; Tadayon, Mohammad H

2016-08-01

Techniques from coding theory are able to improve the efficiency of neuroinspired and neural associative memories by forcing some construction and constraints on the network. In this letter, the approach is to embed coding techniques into neural associative memory in order to increase their performance in the presence of partial erasures. The motivation comes from recent work by Gripon, Berrou, and coauthors, which revisited Willshaw networks and presented a neural network with interacting neurons that partitioned into clusters. The model introduced stores patterns as small-size cliques that can be retrieved in spite of partial error. We focus on improving the success of retrieval by applying two techniques: doing a local coding in each cluster and then applying a precoding step. We use a slightly different decoding scheme, which is appropriate for partial erasures and converges faster. Although the ideas of local coding and precoding are not new, the way we apply them is different. Simulations show an increase in the pattern retrieval capacity for both techniques. Moreover, we use self-dual additive codes over field [Formula: see text], which have very interesting properties and a simple-graph representation.

Image Formation in Lenses and Mirrors, a Complete Representation

ERIC Educational Resources Information Center

Bartlett, Albert A.

1976-01-01

Provides tables and graphs that give a complete and simple picture of the relationships of image distance, object distance, and magnification in all formations of images by simple lenses and mirrors. (CP)

Constructing graph models for software system development and analysis

NASA Astrophysics Data System (ADS)

Pogrebnoy, Andrey V.

2017-01-01

We propose a concept for creating the instrumentation for functional and structural decisions rationale during the software system (SS) development. We propose to develop SS simultaneously on two models - functional (FM) and structural (SM). FM is a source code of the SS. Adequate representation of the FM in the form of a graph model (GM) is made automatically and called SM. The problem of creating and visualizing GM is considered from the point of applying it as a uniform platform for the adequate representation of the SS source code. We propose three levels of GM detailing: GM1 - for visual analysis of the source code and for SS version control, GM2 - for resources optimization and analysis of connections between SS components, GM3 - for analysis of the SS functioning in dynamics. The paper includes examples of constructing all levels of GM.

Thermodynamic characterization of networks using graph polynomials

NASA Astrophysics Data System (ADS)

Ye, Cheng; Comin, César H.; Peron, Thomas K. DM.; Silva, Filipi N.; Rodrigues, Francisco A.; Costa, Luciano da F.; Torsello, Andrea; Hancock, Edwin R.

2015-09-01

In this paper, we present a method for characterizing the evolution of time-varying complex networks by adopting a thermodynamic representation of network structure computed from a polynomial (or algebraic) characterization of graph structure. Commencing from a representation of graph structure based on a characteristic polynomial computed from the normalized Laplacian matrix, we show how the polynomial is linked to the Boltzmann partition function of a network. This allows us to compute a number of thermodynamic quantities for the network, including the average energy and entropy. Assuming that the system does not change volume, we can also compute the temperature, defined as the rate of change of entropy with energy. All three thermodynamic variables can be approximated using low-order Taylor series that can be computed using the traces of powers of the Laplacian matrix, avoiding explicit computation of the normalized Laplacian spectrum. These polynomial approximations allow a smoothed representation of the evolution of networks to be constructed in the thermodynamic space spanned by entropy, energy, and temperature. We show how these thermodynamic variables can be computed in terms of simple network characteristics, e.g., the total number of nodes and node degree statistics for nodes connected by edges. We apply the resulting thermodynamic characterization to real-world time-varying networks representing complex systems in the financial and biological domains. The study demonstrates that the method provides an efficient tool for detecting abrupt changes and characterizing different stages in network evolution.

Robust fault diagnosis of physical systems in operation. Ph.D. Thesis - Rutgers - The State Univ.

NASA Technical Reports Server (NTRS)

Abbott, Kathy Hamilton

1991-01-01

Ideas are presented and demonstrated for improved robustness in diagnostic problem solving of complex physical systems in operation, or operative diagnosis. The first idea is that graceful degradation can be viewed as reasoning at higher levels of abstraction whenever the more detailed levels proved to be incomplete or inadequate. A form of abstraction is defined that applies this view to the problem of diagnosis. In this form of abstraction, named status abstraction, two levels are defined. The lower level of abstraction corresponds to the level of detail at which most current knowledge-based diagnosis systems reason. At the higher level, a graph representation is presented that describes the real-world physical system. An incremental, constructive approach to manipulating this graph representation is demonstrated that supports certain characteristics of operative diagnosis. The suitability of this constructive approach is shown for diagnosing fault propagation behavior over time, and for sometimes diagnosing systems with feedback. A way is shown to represent different semantics in the same type of graph representation to characterize different types of fault propagation behavior. An approach is demonstrated that threats these different behaviors as different fault classes, and the approach moves to other classes when previous classes fail to generate suitable hypotheses. These ideas are implemented in a computer program named Draphys (Diagnostic Reasoning About Physical Systems) and demonstrated for the domain of inflight aircraft subsystems, specifically a propulsion system (containing two turbofan systems and a fuel system) and hydraulic subsystem.

A signal-flow-graph approach to on-line gradient calculation.

PubMed

Campolucci, P; Uncini, A; Piazza, F

2000-08-01

A large class of nonlinear dynamic adaptive systems such as dynamic recurrent neural networks can be effectively represented by signal flow graphs (SFGs). By this method, complex systems are described as a general connection of many simple components, each of them implementing a simple one-input, one-output transformation, as in an electrical circuit. Even if graph representations are popular in the neural network community, they are often used for qualitative description rather than for rigorous representation and computational purposes. In this article, a method for both on-line and batch-backward gradient computation of a system output or cost function with respect to system parameters is derived by the SFG representation theory and its known properties. The system can be any causal, in general nonlinear and time-variant, dynamic system represented by an SFG, in particular any feedforward, time-delay, or recurrent neural network. In this work, we use discrete-time notation, but the same theory holds for the continuous-time case. The gradient is obtained in a straightforward way by the analysis of two SFGs, the original one and its adjoint (obtained from the first by simple transformations), without the complex chain rule expansions of derivatives usually employed. This method can be used for sensitivity analysis and for learning both off-line and on-line. On-line learning is particularly important since it is required by many real applications, such as digital signal processing, system identification and control, channel equalization, and predistortion.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Minghai; Duan, Mojie; Fan, Jue

The thermodynamics and kinetics of protein folding and protein conformational changes are governed by the underlying free energy landscape. However, the multidimensional nature of the free energy landscape makes it difficult to describe. We propose to use a weighted-graph approach to depict the free energy landscape with the nodes on the graph representing the conformational states and the edge weights reflecting the free energy barriers between the states. Our graph is constructed from a molecular dynamics trajectory and does not involve projecting the multi-dimensional free energy landscape onto a low-dimensional space defined by a few order parameters. The calculation ofmore » free energy barriers was based on transition-path theory using the MSMBuilder2 package. We compare our graph with the widely used transition disconnectivity graph (TRDG) which is constructed from the same trajectory and show that our approach gives more accurate description of the free energy landscape than the TRDG approach even though the latter can be organized into a simple tree representation. The weighted-graph is a general approach and can be used on any complex system.« less

Network Reliability: The effect of local network structure on diffusive processes

PubMed Central

Youssef, Mina; Khorramzadeh, Yasamin; Eubank, Stephen

2014-01-01

This paper re-introduces the network reliability polynomial – introduced by Moore and Shannon in 1956 – for studying the effect of network structure on the spread of diseases. We exhibit a representation of the polynomial that is well-suited for estimation by distributed simulation. We describe a collection of graphs derived from Erdős-Rényi and scale-free-like random graphs in which we have manipulated assortativity-by-degree and the number of triangles. We evaluate the network reliability for all these graphs under a reliability rule that is related to the expected size of a connected component. Through these extensive simulations, we show that for positively or neutrally assortative graphs, swapping edges to increase the number of triangles does not increase the network reliability. Also, positively assortative graphs are more reliable than neutral or disassortative graphs with the same number of edges. Moreover, we show the combined effect of both assortativity-by-degree and the presence of triangles on the critical point and the size of the smallest subgraph that is reliable. PMID:24329321

High-order graph matching based feature selection for Alzheimer's disease identification.

PubMed

Liu, Feng; Suk, Heung-Il; Wee, Chong-Yaw; Chen, Huafu; Shen, Dinggang

2013-01-01

One of the main limitations of l1-norm feature selection is that it focuses on estimating the target vector for each sample individually without considering relations with other samples. However, it's believed that the geometrical relation among target vectors in the training set may provide useful information, and it would be natural to expect that the predicted vectors have similar geometric relations as the target vectors. To overcome these limitations, we formulate this as a graph-matching feature selection problem between a predicted graph and a target graph. In the predicted graph a node is represented by predicted vector that may describe regional gray matter volume or cortical thickness features, and in the target graph a node is represented by target vector that include class label and clinical scores. In particular, we devise new regularization terms in sparse representation to impose high-order graph matching between the target vectors and the predicted ones. Finally, the selected regional gray matter volume and cortical thickness features are fused in kernel space for classification. Using the ADNI dataset, we evaluate the effectiveness of the proposed method and obtain the accuracies of 92.17% and 81.57% in AD and MCI classification, respectively.

Coordinates and intervals in graph-based reference genomes.

PubMed

Rand, Knut D; Grytten, Ivar; Nederbragt, Alexander J; Storvik, Geir O; Glad, Ingrid K; Sandve, Geir K

2017-05-18

It has been proposed that future reference genomes should be graph structures in order to better represent the sequence diversity present in a species. However, there is currently no standard method to represent genomic intervals, such as the positions of genes or transcription factor binding sites, on graph-based reference genomes. We formalize offset-based coordinate systems on graph-based reference genomes and introduce methods for representing intervals on these reference structures. We show the advantage of our methods by representing genes on a graph-based representation of the newest assembly of the human genome (GRCh38) and its alternative loci for regions that are highly variable. More complex reference genomes, containing alternative loci, require methods to represent genomic data on these structures. Our proposed notation for genomic intervals makes it possible to fully utilize the alternative loci of the GRCh38 assembly and potential future graph-based reference genomes. We have made a Python package for representing such intervals on offset-based coordinate systems, available at https://github.com/uio-cels/offsetbasedgraph . An interactive web-tool using this Python package to visualize genes on a graph created from GRCh38 is available at https://github.com/uio-cels/genomicgraphcoords .

Graph pyramids as models of human problem solving

NASA Astrophysics Data System (ADS)

Pizlo, Zygmunt; Li, Zheng

2004-05-01

Prior theories have assumed that human problem solving involves estimating distances among states and performing search through the problem space. The role of mental representation in those theories was minimal. Results of our recent experiments suggest that humans are able to solve some difficult problems quickly and accurately. Specifically, in solving these problems humans do not seem to rely on distances or on search. It is quite clear that producing good solutions without performing search requires a very effective mental representation. In this paper we concentrate on studying the nature of this representation. Our theory takes the form of a graph pyramid. To verify the psychological plausibility of this theory we tested subjects in a Euclidean Traveling Salesman Problem in the presence of obstacles. The role of the number and size of obstacles was tested for problems with 6-50 cities. We analyzed the effect of experimental conditions on solution time per city and on solution error. The main result is that time per city is systematically affected only by the size of obstacles, but not by their number, or by the number of cities.

Higher-order thinking skill problem on data representation in primary school: A case study

NASA Astrophysics Data System (ADS)

Putri, R. I. I.; Zulkardi, Z.

2018-01-01

This article aimed at reporting research result on a case study of a lesson using a HOTS problem. The task was about data representation using baby growth context. The study used a design research method consisting of three stages: preparing for an experiment, experiment in the classroom (pilot and teaching), and retrospective analysis. Participants were sixth grade students who were learning data representations in a Primary School in Palembang Indonesia. A set of instructional activities were designed using Indonesian version of Realistic Mathematics Education (PMRI) approach. The result showed that students were able to solve the problem and present their solution in front of the classroom. The conclusion indicated that that HOTS problem using the growth of a child as the context could lead students to use their mathematical thinking. During the learning activities along with teacher orchestra’s guidance, and discussion, students were able to solve the problem using line graph although some of them used a bar graph. In the future, teachers are necessary to focus on the role of real-world figure in mathematics learning.

Artificial intelligence approach to planning the robotic assembly of large tetrahedral truss structures

NASA Technical Reports Server (NTRS)

Homemdemello, Luiz S.

1992-01-01

An assembly planner for tetrahedral truss structures is presented. To overcome the difficulties due to the large number of parts, the planner exploits the simplicity and uniformity of the shapes of the parts and the regularity of their interconnection. The planning automation is based on the computational formalism known as production system. The global data base consists of a hexagonal grid representation of the truss structure. This representation captures the regularity of tetrahedral truss structures and their multiple hierarchies. It maps into quadratic grids and can be implemented in a computer by using a two-dimensional array data structure. By maintaining the multiple hierarchies explicitly in the model, the choice of a particular hierarchy is only made when needed, thus allowing a more informed decision. Furthermore, testing the preconditions of the production rules is simple because the patterned way in which the struts are interconnected is incorporated into the topology of the hexagonal grid. A directed graph representation of assembly sequences allows the use of both graph search and backtracking control strategies.

Acute effects of alcohol on intrusive memory development and viewpoint dependence in spatial memory support a dual representation model.

PubMed

Bisby, James A; King, John A; Brewin, Chris R; Burgess, Neil; Curran, H Valerie

2010-08-01

A dual representation model of intrusive memory proposes that personally experienced events give rise to two types of representation: an image-based, egocentric representation based on sensory-perceptual features; and a more abstract, allocentric representation that incorporates spatiotemporal context. The model proposes that intrusions reflect involuntary reactivation of egocentric representations in the absence of a corresponding allocentric representation. We tested the model by investigating the effect of alcohol on intrusive memories and, concurrently, on egocentric and allocentric spatial memory. With a double-blind independent group design participants were administered alcohol (.4 or .8 g/kg) or placebo. A virtual environment was used to present objects and test recognition memory from the same viewpoint as presentation (tapping egocentric memory) or a shifted viewpoint (tapping allocentric memory). Participants were also exposed to a trauma video and required to detail intrusive memories for 7 days, after which explicit memory was assessed. There was a selective impairment of shifted-view recognition after the low dose of alcohol, whereas the high dose induced a global impairment in same-view and shifted-view conditions. Alcohol showed a dose-dependent inverted "U"-shaped effect on intrusions, with only the low dose increasing the number of intrusions, replicating previous work. When same-view recognition was intact, decrements in shifted-view recognition were associated with increases in intrusions. The differential effect of alcohol on intrusive memories and on same/shifted-view recognition support a dual representation model in which intrusions might reflect an imbalance between two types of memory representation. These findings highlight important clinical implications, given alcohol's involvement in real-life trauma. Copyright 2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

AGM: A DSL for mobile cloud computing based on directed graph

NASA Astrophysics Data System (ADS)

Tanković, Nikola; Grbac, Tihana Galinac

2016-06-01

This paper summarizes a novel approach for consuming a domain specific language (DSL) by transforming it to a directed graph representation persisted by a graph database. Using such specialized database enables advanced navigation trough the stored model exposing only relevant subsets of meta-data to different involved services and components. We applied this approach in a mobile cloud computing system and used it to model several mobile applications in retail, supply chain management and merchandising domain. These application are distributed in a Software-as-a-Service (SaaS) fashion and used by thousands of customers in Croatia. We report on lessons learned and propose further research on this topic.

Learning locality preserving graph from data.

PubMed

Zhang, Yan-Ming; Huang, Kaizhu; Hou, Xinwen; Liu, Cheng-Lin

2014-11-01

Machine learning based on graph representation, or manifold learning, has attracted great interest in recent years. As the discrete approximation of data manifold, the graph plays a crucial role in these kinds of learning approaches. In this paper, we propose a novel learning method for graph construction, which is distinct from previous methods in that it solves an optimization problem with the aim of directly preserving the local information of the original data set. We show that the proposed objective has close connections with the popular Laplacian Eigenmap problem, and is hence well justified. The optimization turns out to be a quadratic programming problem with n(n-1)/2 variables (n is the number of data points). Exploiting the sparsity of the graph, we further propose a more efficient cutting plane algorithm to solve the problem, making the method better scalable in practice. In the context of clustering and semi-supervised learning, we demonstrated the advantages of our proposed method by experiments.

The signed permutation group on Feynman graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Purkart, Julian, E-mail: purkart@physik.hu-berlin.de

2016-08-15

The Feynman rules assign to every graph an integral which can be written as a function of a scaling parameter L. Assuming L for the process under consideration is very small, so that contributions to the renormalization group are small, we can expand the integral and only consider the lowest orders in the scaling. The aim of this article is to determine specific combinations of graphs in a scalar quantum field theory that lead to a remarkable simplification of the first non-trivial term in the perturbation series. It will be seen that the result is independent of the renormalization schememore » and the scattering angles. To achieve that goal we will utilize the parametric representation of scalar Feynman integrals as well as the Hopf algebraic structure of the Feynman graphs under consideration. Moreover, we will present a formula which reduces the effort of determining the first-order term in the perturbation series for the specific combination of graphs to a minimum.« less

Superselection Structure of Massive Quantum Field Theories in 1+1 Dimensions

NASA Astrophysics Data System (ADS)

Müger, Michael

We show that a large class of massive quantum field theories in 1+1 dimensions, characterized by Haag duality and the split property for wedges, does not admit locally generated superselection sectors in the sense of Doplicher, Haag and Roberts. Thereby the extension of DHR theory to 1+1 dimensions due to Fredenhagen, Rehren and Schroer is vacuous for such theories. Even charged representations which are localizable only in wedge regions are ruled out. Furthermore, Haag duality holds in all locally normal representations. These results are applied to the theory of soliton sectors. Furthermore, the extension of localized representations of a non-Haag dual net to the dual net is reconsidered. It must be emphasized that these statements do not apply to massless theories since they do not satisfy the above split property. In particular, it is known that positive energy representations of conformally invariant theories are DHR representations.

Graph Unification and Tangram Hypothesis Explanation Representation (GATHER) and System and Component Modeling Framework (SCMF)

DTIC Science & Technology

2008-08-01

services, DIDS and DMS, are deployable on the TanGrid system and are accessible via two APIs, a Java client and a servlet based interface. Additionally...but required the user to instantiate an IGraph object with several Java Maps containing the nodes, node attributes, edge types, and the connections...restrictions imposed by the bulk ingest process. Finally, once the bulk ingest process was available in the GraphUnification Java Archives (JAR), DC was

RelFinder: Revealing Relationships in RDF Knowledge Bases

NASA Astrophysics Data System (ADS)

Heim, Philipp; Hellmann, Sebastian; Lehmann, Jens; Lohmann, Steffen; Stegemann, Timo

The Semantic Web has recently seen a rise of large knowledge bases (such as DBpedia) that are freely accessible via SPARQL endpoints. The structured representation of the contained information opens up new possibilities in the way it can be accessed and queried. In this paper, we present an approach that extracts a graph covering relationships between two objects of interest. We show an interactive visualization of this graph that supports the systematic analysis of the found relationships by providing highlighting, previewing, and filtering features.

GOGrapher: A Python library for GO graph representation and analysis.

PubMed

Muller, Brian; Richards, Adam J; Jin, Bo; Lu, Xinghua

2009-07-07

The Gene Ontology is the most commonly used controlled vocabulary for annotating proteins. The concepts in the ontology are organized as a directed acyclic graph, in which a node corresponds to a biological concept and a directed edge denotes the parent-child semantic relationship between a pair of terms. A large number of protein annotations further create links between proteins and their functional annotations, reflecting the contemporary knowledge about proteins and their functional relationships. This leads to a complex graph consisting of interleaved biological concepts and their associated proteins. What is needed is a simple, open source library that provides tools to not only create and view the Gene Ontology graph, but to analyze and manipulate it as well. Here we describe the development and use of GOGrapher, a Python library that can be used for the creation, analysis, manipulation, and visualization of Gene Ontology related graphs. An object-oriented approach was adopted to organize the hierarchy of the graphs types and associated classes. An Application Programming Interface is provided through which different types of graphs can be pragmatically created, manipulated, and visualized. GOGrapher has been successfully utilized in multiple research projects, e.g., a graph-based multi-label text classifier for protein annotation. The GOGrapher project provides a reusable programming library designed for the manipulation and analysis of Gene Ontology graphs. The library is freely available for the scientific community to use and improve.

Dual algebraic formulation of differential GPS

NASA Astrophysics Data System (ADS)

Lannes, A.; Dur, S.

2003-05-01

A new approach to differential GPS is presented. The corresponding theoretical framework calls on elementary concepts of algebraic graph theory. The notion of double difference, which is related to that of closure in the sense of Kirchhoff, is revisited in this context. The Moore-Penrose pseudo-inverse of the closure operator plays a key role in the corresponding dual formulation. This approach, which is very attractive from a conceptual point of view, sheds a new light on the Teunissen formulation.

Reticulate classification of mosaic microbial genomes using NeAT website.

PubMed

Lima-Mendez, Gipsi

2012-01-01

The tree of life is the classical representation of the evolutionary relationships between existent species. A tree is appropriate to display the divergence of species through mutation, i.e., by vertical descent. However, lateral gene transfer (LGT) is excluded from such representations. When LGT contribution to genome evolution cannot be neglected (e.g., for prokaryotes and mobile genetic elements), the tree becomes misleading. Networks appear as an intuitive way to represent both vertical and horizontal relationships, while overlapping groups within such graphs are more suitable for their classification. Here, we describe a method to represent both vertical and horizontal relationships. We start with a set of genomes whose coded proteins have been grouped into families based on sequence similarity. Next, all pairs of genomes are compared, counting the number of proteins classified into the same family. From this comparison, we derive a weighted graph where genomes with a significant number of similar proteins are linked. Finally, we apply a two-step clustering of this graph to produce a classification where nodes can be assigned to multiple clusters. The procedure can be performed using the Network Analysis Tools (NeAT) website.

Effects of direct instruction of visual literacy skills on science achievement when integrated into inquiry learning

NASA Astrophysics Data System (ADS)

Galyas, Lesley Crowell

Understanding of visual representations is a pivotal skill necessary in science. These visual, verbal, and numeric representations are the crux of science discourses "by scientists, with students and the general public" (Pauwels, 2006, p.viii). Those who lack the understanding of these representations see it as a foreign language, one that they have never been taught to interpret. Roth, Bowen and Masciotra (2002) assert that students lack the necessary preparation to interpret scientific representational practices thoughtfully and skillfully and are not equipped to decipher the combinations of "divergent representational systems (graphs, images, equations) in a meaningful and edifying whole" (Pauwels, 2006, p.x). Several studies confirm that when students are unable to retrieve and apply knowledge, they will have difficulties with problem solving, critical thinking, and learning new material; moreover this has been demonstrated among all ability levels (O'Reilly & McNamara, 2007). The purpose of this mixed method case study was to explore the use of deliberate instruction of visual literacy skills embedded within inquiry science learning, utilizing the TLC method, for middle school students in a single classroom. Pre- and post-testing, teacher interviews and classroom observations were utilized. The study had three phases pre-implementation, implementation of TLC, and post implementation. The analysis was based on the Embedded Experimental Model. "This model is defined by having qualitative data embedded within an experimental design" (Creswell, 2007, Loc 806 of 3545). The 7th grade science classes studied are dual language immersion with 93% Hispanic and 100% economically disadvantaged students. These classes were taught by a single teacher where native Spanish speakers were taught in Spanish and English speakers were taught in English. The data for final test scores for students taught in English (English speakers, and EL exited) resulted in t (21)=5.42, * p≤.05; and for students taught in Spanish (EL) resulted in t (43)=10.29, *p≤.05. For these findings the following conclusions were made: Evidence from the t-test analysis suggests that increasing the focus of visual literacy skills increases student achievement on pre- and post-tests with both native English speakers as well as English learners.

An approach to multiscale modelling with graph grammars.

PubMed

Ong, Yongzhi; Streit, Katarína; Henke, Michael; Kurth, Winfried

2014-09-01

Functional-structural plant models (FSPMs) simulate biological processes at different spatial scales. Methods exist for multiscale data representation and modification, but the advantages of using multiple scales in the dynamic aspects of FSPMs remain unclear. Results from multiscale models in various other areas of science that share fundamental modelling issues with FSPMs suggest that potential advantages do exist, and this study therefore aims to introduce an approach to multiscale modelling in FSPMs. A three-part graph data structure and grammar is revisited, and presented with a conceptual framework for multiscale modelling. The framework is used for identifying roles, categorizing and describing scale-to-scale interactions, thus allowing alternative approaches to model development as opposed to correlation-based modelling at a single scale. Reverse information flow (from macro- to micro-scale) is catered for in the framework. The methods are implemented within the programming language XL. Three example models are implemented using the proposed multiscale graph model and framework. The first illustrates the fundamental usage of the graph data structure and grammar, the second uses probabilistic modelling for organs at the fine scale in order to derive crown growth, and the third combines multiscale plant topology with ozone trends and metabolic network simulations in order to model juvenile beech stands under exposure to a toxic trace gas. The graph data structure supports data representation and grammar operations at multiple scales. The results demonstrate that multiscale modelling is a viable method in FSPM and an alternative to correlation-based modelling. Advantages and disadvantages of multiscale modelling are illustrated by comparisons with single-scale implementations, leading to motivations for further research in sensitivity analysis and run-time efficiency for these models.

An approach to multiscale modelling with graph grammars

PubMed Central

Ong, Yongzhi; Streit, Katarína; Henke, Michael; Kurth, Winfried

2014-01-01

Background and Aims Functional–structural plant models (FSPMs) simulate biological processes at different spatial scales. Methods exist for multiscale data representation and modification, but the advantages of using multiple scales in the dynamic aspects of FSPMs remain unclear. Results from multiscale models in various other areas of science that share fundamental modelling issues with FSPMs suggest that potential advantages do exist, and this study therefore aims to introduce an approach to multiscale modelling in FSPMs. Methods A three-part graph data structure and grammar is revisited, and presented with a conceptual framework for multiscale modelling. The framework is used for identifying roles, categorizing and describing scale-to-scale interactions, thus allowing alternative approaches to model development as opposed to correlation-based modelling at a single scale. Reverse information flow (from macro- to micro-scale) is catered for in the framework. The methods are implemented within the programming language XL. Key Results Three example models are implemented using the proposed multiscale graph model and framework. The first illustrates the fundamental usage of the graph data structure and grammar, the second uses probabilistic modelling for organs at the fine scale in order to derive crown growth, and the third combines multiscale plant topology with ozone trends and metabolic network simulations in order to model juvenile beech stands under exposure to a toxic trace gas. Conclusions The graph data structure supports data representation and grammar operations at multiple scales. The results demonstrate that multiscale modelling is a viable method in FSPM and an alternative to correlation-based modelling. Advantages and disadvantages of multiscale modelling are illustrated by comparisons with single-scale implementations, leading to motivations for further research in sensitivity analysis and run-time efficiency for these models. PMID:25134929

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

NASA Astrophysics Data System (ADS)

Hyman, J.; Hagberg, A.; Srinivasan, G.; Mohd-Yusof, J.; Viswanathan, H. S.

2017-12-01

We present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths. First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. Accurate estimates of first passage times are obtained with an order of magnitude reduction of CPU time and mesh size using the proposed method.

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

NASA Astrophysics Data System (ADS)

Hyman, Jeffrey D.; Hagberg, Aric; Srinivasan, Gowri; Mohd-Yusof, Jamaludin; Viswanathan, Hari

2017-07-01

We present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths. First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. Accurate estimates of first passage times are obtained with an order of magnitude reduction of CPU time and mesh size using the proposed method.

Simulation of 'hitch-hiking' genealogies.

PubMed

Slade, P F

2001-01-01

An ancestral influence graph is derived, an analogue of the coalescent and a composite of Griffiths' (1991) two-locus ancestral graph and Krone and Neuhauser's (1997) ancestral selection graph. This generalizes their use of branching-coalescing random graphs so as to incorporate both selection and recombination into gene genealogies. Qualitative understanding of a 'hitch-hiking' effect on genealogies is pursued via diagrammatic representation of the genealogical process in a two-locus, two-allele haploid model. Extending the simulation technique of Griffiths and Tavare (1996), computational estimation of expected times to the most recent common ancestor of samples of n genes under recombination and selection in two-locus, two-allele haploid and diploid models are presented. Such times are conditional on sample configuration. Monte Carlo simulations show that 'hitch-hiking' is a subtle effect that alters the conditional expected depth of the genealogy at the linked neutral locus depending on a mutation-selection-recombination balance.

Principal curve detection in complicated graph images

NASA Astrophysics Data System (ADS)

Liu, Yuncai; Huang, Thomas S.

2001-09-01

Finding principal curves in an image is an important low level processing in computer vision and pattern recognition. Principal curves are those curves in an image that represent boundaries or contours of objects of interest. In general, a principal curve should be smooth with certain length constraint and allow either smooth or sharp turning. In this paper, we present a method that can efficiently detect principal curves in complicated map images. For a given feature image, obtained from edge detection of an intensity image or thinning operation of a pictorial map image, the feature image is first converted to a graph representation. In graph image domain, the operation of principal curve detection is performed to identify useful image features. The shortest path and directional deviation schemes are used in our algorithm os principal verve detection, which is proven to be very efficient working with real graph images.

Graph Design via Convex Optimization: Online and Distributed Perspectives

NASA Astrophysics Data System (ADS)

Meng, De

Network and graph have long been natural abstraction of relations in a variety of applications, e.g. transportation, power system, social network, communication, electrical circuit, etc. As a large number of computation and optimization problems are naturally defined on graphs, graph structures not only enable important properties of these problems, but also leads to highly efficient distributed and online algorithms. For example, graph separability enables the parallelism for computation and operation as well as limits the size of local problems. More interestingly, graphs can be defined and constructed in order to take best advantage of those problem properties. This dissertation focuses on graph structure and design in newly proposed optimization problems, which establish a bridge between graph properties and optimization problem properties. We first study a new optimization problem called Geodesic Distance Maximization Problem (GDMP). Given a graph with fixed edge weights, finding the shortest path, also known as the geodesic, between two nodes is a well-studied network flow problem. We introduce the Geodesic Distance Maximization Problem (GDMP): the problem of finding the edge weights that maximize the length of the geodesic subject to convex constraints on the weights. We show that GDMP is a convex optimization problem for a wide class of flow costs, and provide a physical interpretation using the dual. We present applications of the GDMP in various fields, including optical lens design, network interdiction, and resource allocation in the control of forest fires. We develop an Alternating Direction Method of Multipliers (ADMM) by exploiting specific problem structures to solve large-scale GDMP, and demonstrate its effectiveness in numerical examples. We then turn our attention to distributed optimization on graph with only local communication. Distributed optimization arises in a variety of applications, e.g. distributed tracking and localization, estimation problems in sensor networks, multi-agent coordination. Distributed optimization aims to optimize a global objective function formed by summation of coupled local functions over a graph via only local communication and computation. We developed a weighted proximal ADMM for distributed optimization using graph structure. This fully distributed, single-loop algorithm allows simultaneous updates and can be viewed as a generalization of existing algorithms. More importantly, we achieve faster convergence by jointly designing graph weights and algorithm parameters. Finally, we propose a new problem on networks called Online Network Formation Problem: starting with a base graph and a set of candidate edges, at each round of the game, player one first chooses a candidate edge and reveals it to player two, then player two decides whether to accept it; player two can only accept limited number of edges and make online decisions with the goal to achieve the best properties of the synthesized network. The network properties considered include the number of spanning trees, algebraic connectivity and total effective resistance. These network formation games arise in a variety of cooperative multiagent systems. We propose a primal-dual algorithm framework for the general online network formation game, and analyze the algorithm performance by the competitive ratio and regret.

Layer-by-layer assembly of graphene oxide on thermosensitive liposomes for photo-chemotherapy.

PubMed

Hashemi, Mohadeseh; Omidi, Meisam; Muralidharan, Bharadwaj; Tayebi, Lobat; Herpin, Matthew J; Mohagheghi, Mohammad Ali; Mohammadi, Javad; Smyth, Hugh D C; Milner, Thomas E

2018-01-01

Stimuli responsive polyelectrolyte nanoparticles have been developed for chemo-photothermal destruction of breast cancer cells. This novel system, called layer by layer Lipo-graph (LBL Lipo-graph), is composed of alternate layers of graphene oxide (GO) and graphene oxide conjugated poly (l-lysine) (GO-PLL) deposited on cationic liposomes encapsulating doxorubicin. Various concentrations of GO and GO-PLL were examined and the optimal LBL Lipo-graph was found to have a particle size of 267.9 ± 13 nm, zeta potential of +43.9 ± 6.9 mV and encapsulation efficiency of 86.4 ± 4.7%. The morphology of LBL Lipo-graph was examined by cryogenic-transmission electron microscopy (Cryo-TEM), atomic force microcopy (AFM) and scanning electron microscopy (SEM). The buildup of LBL Lipo-graph was confirmed via ultraviolet-visible (UV-Vis) spectrophotometry, thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) analysis. Infra-red (IR) response suggests that four layers are sufficient to induce a gel-to-liquid phase transition in response to near infra-red (NIR) laser irradiation. Light-matter interaction of LBL Lipo-graph was studied by calculating the absorption cross section in the frequency domain by utilizing Fourier analysis. Drug release assay indicates that the LBL Lipo-graph releases much faster in an acidic environment than a liposome control. A cytotoxicity assay was conducted to prove the efficacy of LBL Lipo-graph to destroy MD-MB-231 cells in response to NIR laser emission. Also, image stream flow cytometry and two photon microcopy provide supportive data for the potential application of LBL Lipo-graph for photothermal therapy. Study results suggest the novel dual-sensitive nanoparticles allow intracellular doxorubin delivery and respond to either acidic environments or NIR excitation. Stimuli sensitive hybrid nanoparticles have been synthesized using a layer-by-layer technique and demonstrated for dual chemo-photothermal destruction of breast cancer cells. The hybrid nanoparticles are composed of alternating layers of graphene oxide and graphene oxide conjugated poly-l-lysine coating the surface of a thermosensitive cationic liposome containing doxorubicin as a core. Data suggests that the hybrid nanoparticles may offer many advantages for chemo-photothermal therapy. Advantages include a decrease of the initial burst release which may result in the reduction in systemic toxicity, increase in pH responsivity around the tumor environment and improved NIR light absorption. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Inferring ontology graph structures using OWL reasoning.

PubMed

Rodríguez-García, Miguel Ángel; Hoehndorf, Robert

2018-01-05

Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge. We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph . Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

Reflecting on Graphs: Attributes of Graph Choice and Construction Practices in Biology.

PubMed

Angra, Aakanksha; Gardner, Stephanie M

2017-01-01

Undergraduate biology education reform aims to engage students in scientific practices such as experimental design, experimentation, and data analysis and communication. Graphs are ubiquitous in the biological sciences, and creating effective graphical representations involves quantitative and disciplinary concepts and skills. Past studies document student difficulties with graphing within the contexts of classroom or national assessments without evaluating student reasoning. Operating under the metarepresentational competence framework, we conducted think-aloud interviews to reveal differences in reasoning and graph quality between undergraduate biology students, graduate students, and professors in a pen-and-paper graphing task. All professors planned and thought about data before graph construction. When reflecting on their graphs, professors and graduate students focused on the function of graphs and experimental design, while most undergraduate students relied on intuition and data provided in the task. Most undergraduate students meticulously plotted all data with scaled axes, while professors and some graduate students transformed the data, aligned the graph with the research question, and reflected on statistics and sample size. Differences in reasoning and approaches taken in graph choice and construction corroborate and extend previous findings and provide rich targets for undergraduate and graduate instruction. © 2017 A. Angra and S. M. Gardner. CBE—Life Sciences Education © 2017 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Graphic Strategies for Analyzing and Interpreting Curricular Mapping Data

PubMed Central

Leonard, Sean T.

2010-01-01

Objective To describe curricular mapping strategies used in analyzing and interpreting curricular mapping data and present findings on how these strategies were used to facilitate curricular development. Design Nova Southeastern University's doctor of pharmacy curriculum was mapped to the college's educational outcomes. The mapping process included development of educational outcomes followed by analysis of course material and semi-structured interviews with course faculty members. Data collected per course outcome included learning opportunities and assessment measures used. Assessment Nearly 1,000 variables and 10,000 discrete rows of curricular data were collected. Graphic representations of curricular data were created using bar charts and stacked area graphs relating the learning opportunities to the educational outcomes. Graphs were used in the curricular evaluation and development processes to facilitate the identification of curricular holes, sequencing misalignments, learning opportunities, and assessment measures. Conclusion Mapping strategies that use graphic representations of curricular data serve as effective diagnostic and curricular development tools. PMID:20798804

Graphic strategies for analyzing and interpreting curricular mapping data.

PubMed

Armayor, Graciela M; Leonard, Sean T

2010-06-15

To describe curricular mapping strategies used in analyzing and interpreting curricular mapping data and present findings on how these strategies were used to facilitate curricular development. Nova Southeastern University's doctor of pharmacy curriculum was mapped to the college's educational outcomes. The mapping process included development of educational outcomes followed by analysis of course material and semi-structured interviews with course faculty members. Data collected per course outcome included learning opportunities and assessment measures used. Nearly 1,000 variables and 10,000 discrete rows of curricular data were collected. Graphic representations of curricular data were created using bar charts and stacked area graphs relating the learning opportunities to the educational outcomes. Graphs were used in the curricular evaluation and development processes to facilitate the identification of curricular holes, sequencing misalignments, learning opportunities, and assessment measures. Mapping strategies that use graphic representations of curricular data serve as effective diagnostic and curricular development tools.

Some trees with partition dimension three

NASA Astrophysics Data System (ADS)

Fredlina, Ketut Queena; Baskoro, Edy Tri

2016-02-01

The concept of partition dimension of a graph was introduced by Chartrand, E. Salehi and P. Zhang (1998) [2]. Let G(V, E) be a connected graph. For S ⊆ V (G) and v ∈ V (G), define the distance d(v, S) from v to S is min{d(v, x)|x ∈ S}. Let Π be an ordered partition of V (G) and Π = {S1, S2, ..., Sk }. The representation r(v|Π) of vertex v with respect to Π is (d(v, S1), d(v, S2), ..., d(v, Sk)). If the representations of all vertices are distinct, then the partition Π is called a resolving partition of G. The partition dimension of G is the minimum k such that G has a resolving partition with k partition classes. In this paper, we characterize some classes of trees with partition dimension three, namely olive trees, weeds, and centipedes.

On the dual equivalence of the self-dual and topologically massive /p-form models

NASA Astrophysics Data System (ADS)

Menezes, R.; Nascimento, J. R. S.; Ribeiro, R. F.; Wotzasek, C.

2003-07-01

We study the duality symmetry in p-form models containing a generalized Bq∧Fp+1 term in spacetime manifolds of arbitrary dimensions. The equivalence between the Bq∧Fp+1 self-dual (SDB∧F) and the Bq∧Fp+1 topologically massive (TMB∧F) models is established using a gauge embedding procedure, including the minimal coupling to conserved charged matter current. The minimal coupling adopted for both tensor fields in the self-dual representation is transformed into a non-minimal magnetic like coupling in the topologically massive representation but with the currents swapped. It is known that to establish this equivalence a current-current interaction term is needed to render the matter sector unchanged. We show that both terms arise naturally from the embedding adopted. Comparison with Higgs/Julia-Toulouse duality is established.

RNA Graph Partitioning for the Discovery of RNA Modularity: A Novel Application of Graph Partition Algorithm to Biology

PubMed Central

Elmetwaly, Shereef; Schlick, Tamar

2014-01-01

Graph representations have been widely used to analyze and design various economic, social, military, political, and biological networks. In systems biology, networks of cells and organs are useful for understanding disease and medical treatments and, in structural biology, structures of molecules can be described, including RNA structures. In our RNA-As-Graphs (RAG) framework, we represent RNA structures as tree graphs by translating unpaired regions into vertices and helices into edges. Here we explore the modularity of RNA structures by applying graph partitioning known in graph theory to divide an RNA graph into subgraphs. To our knowledge, this is the first application of graph partitioning to biology, and the results suggest a systematic approach for modular design in general. The graph partitioning algorithms utilize mathematical properties of the Laplacian eigenvector (µ2) corresponding to the second eigenvalues (λ2) associated with the topology matrix defining the graph: λ2 describes the overall topology, and the sum of µ2′s components is zero. The three types of algorithms, termed median, sign, and gap cuts, divide a graph by determining nodes of cut by median, zero, and largest gap of µ2′s components, respectively. We apply these algorithms to 45 graphs corresponding to all solved RNA structures up through 11 vertices (∼220 nucleotides). While we observe that the median cut divides a graph into two similar-sized subgraphs, the sign and gap cuts partition a graph into two topologically-distinct subgraphs. We find that the gap cut produces the best biologically-relevant partitioning for RNA because it divides RNAs at less stable connections while maintaining junctions intact. The iterative gap cuts suggest basic modules and assembly protocols to design large RNA structures. Our graph substructuring thus suggests a systematic approach to explore the modularity of biological networks. In our applications to RNA structures, subgraphs also suggest design strategies for novel RNA motifs. PMID:25188578

Heuristic-driven graph wavelet modeling of complex terrain

NASA Astrophysics Data System (ADS)

Cioacǎ, Teodor; Dumitrescu, Bogdan; Stupariu, Mihai-Sorin; Pǎtru-Stupariu, Ileana; Nǎpǎrus, Magdalena; Stoicescu, Ioana; Peringer, Alexander; Buttler, Alexandre; Golay, François

2015-03-01

We present a novel method for building a multi-resolution representation of large digital surface models. The surface points coincide with the nodes of a planar graph which can be processed using a critically sampled, invertible lifting scheme. To drive the lazy wavelet node partitioning, we employ an attribute aware cost function based on the generalized quadric error metric. The resulting algorithm can be applied to multivariate data by storing additional attributes at the graph's nodes. We discuss how the cost computation mechanism can be coupled with the lifting scheme and examine the results by evaluating the root mean square error. The algorithm is experimentally tested using two multivariate LiDAR sets representing terrain surface and vegetation structure with different sampling densities.

Figure-ground segmentation based on class-independent shape priors

NASA Astrophysics Data System (ADS)

Li, Yang; Liu, Yang; Liu, Guojun; Guo, Maozu

2018-01-01

We propose a method to generate figure-ground segmentation by incorporating shape priors into the graph-cuts algorithm. Given an image, we first obtain a linear representation of an image and then apply directional chamfer matching to generate class-independent, nonparametric shape priors, which provide shape clues for the graph-cuts algorithm. We then enforce shape priors in a graph-cuts energy function to produce object segmentation. In contrast to previous segmentation methods, the proposed method shares shape knowledge for different semantic classes and does not require class-specific model training. Therefore, the approach obtains high-quality segmentation for objects. We experimentally validate that the proposed method outperforms previous approaches using the challenging PASCAL VOC 2010/2012 and Berkeley (BSD300) segmentation datasets.

A bio-behavioral model of addiction treatment: applying dual representation theory to craving management and relapse prevention.

PubMed

Matto, Holly

2005-01-01

A bio-behavioral approach to drug addiction treatment is outlined. The presented treatment model uses dual representation theory as a guiding framework for understanding the bio-behavioral processes activated during the application of expressive therapeutic methods. Specifically, the treatment model explains how visual processing techniques can supplement traditional relapse prevention therapy protocols, to help clients better manage cravings and control triggers in hard-to-treat populations such as chronic substance-dependent persons.

Design of a flexible component gathering algorithm for converting cell-based models to graph representations for use in evolutionary search

PubMed Central

2014-01-01

Background The ability of science to produce experimental data has outpaced the ability to effectively visualize and integrate the data into a conceptual framework that can further higher order understanding. Multidimensional and shape-based observational data of regenerative biology presents a particularly daunting challenge in this regard. Large amounts of data are available in regenerative biology, but little progress has been made in understanding how organisms such as planaria robustly achieve and maintain body form. An example of this kind of data can be found in a new repository (PlanformDB) that encodes descriptions of planaria experiments and morphological outcomes using a graph formalism. Results We are developing a model discovery framework that uses a cell-based modeling platform combined with evolutionary search to automatically search for and identify plausible mechanisms for the biological behavior described in PlanformDB. To automate the evolutionary search we developed a way to compare the output of the modeling platform to the morphological descriptions stored in PlanformDB. We used a flexible connected component algorithm to create a graph representation of the virtual worm from the robust, cell-based simulation data. These graphs can then be validated and compared with target data from PlanformDB using the well-known graph-edit distance calculation, which provides a quantitative metric of similarity between graphs. The graph edit distance calculation was integrated into a fitness function that was able to guide automated searches for unbiased models of planarian regeneration. We present a cell-based model of planarian that can regenerate anatomical regions following bisection of the organism, and show that the automated model discovery framework is capable of searching for and finding models of planarian regeneration that match experimental data stored in PlanformDB. Conclusion The work presented here, including our algorithm for converting cell-based models into graphs for comparison with data stored in an external data repository, has made feasible the automated development, training, and validation of computational models using morphology-based data. This work is part of an ongoing project to automate the search process, which will greatly expand our ability to identify, consider, and test biological mechanisms in the field of regenerative biology. PMID:24917489

eHUGS: Enhanced Hierarchical Unbiased Graph Shrinkage for Efficient Groupwise Registration

PubMed Central

Wu, Guorong; Peng, Xuewei; Ying, Shihui; Wang, Qian; Yap, Pew-Thian; Shen, Dan; Shen, Dinggang

2016-01-01

Effective and efficient spatial normalization of a large population of brain images is critical for many clinical and research studies, but it is technically very challenging. A commonly used approach is to choose a certain image as the template and then align all other images in the population to this template by applying pairwise registration. To avoid the potential bias induced by the inappropriate template selection, groupwise registration methods have been proposed to simultaneously register all images to a latent common space. However, current groupwise registration methods do not make full use of image distribution information for more accurate registration. In this paper, we present a novel groupwise registration method that harnesses the image distribution information by capturing the image distribution manifold using a hierarchical graph with its nodes representing the individual images. More specifically, a low-level graph describes the image distribution in each subgroup, and a high-level graph encodes the relationship between representative images of subgroups. Given the graph representation, we can register all images to the common space by dynamically shrinking the graph on the image manifold. The topology of the entire image distribution is always maintained during graph shrinkage. Evaluations on two datasets, one for 80 elderly individuals and one for 285 infants, indicate that our method can yield promising results. PMID:26800361

Graph Databases for Large-Scale Healthcare Systems: A Framework for Efficient Data Management and Data Services

DOE Office of Scientific and Technical Information (OSTI.GOV)

Park, Yubin; Shankar, Mallikarjun; Park, Byung H.

Designing a database system for both efficient data management and data services has been one of the enduring challenges in the healthcare domain. In many healthcare systems, data services and data management are often viewed as two orthogonal tasks; data services refer to retrieval and analytic queries such as search, joins, statistical data extraction, and simple data mining algorithms, while data management refers to building error-tolerant and non-redundant database systems. The gap between service and management has resulted in rigid database systems and schemas that do not support effective analytics. We compose a rich graph structure from an abstracted healthcaremore » RDBMS to illustrate how we can fill this gap in practice. We show how a healthcare graph can be automatically constructed from a normalized relational database using the proposed 3NF Equivalent Graph (3EG) transformation.We discuss a set of real world graph queries such as finding self-referrals, shared providers, and collaborative filtering, and evaluate their performance over a relational database and its 3EG-transformed graph. Experimental results show that the graph representation serves as multiple de-normalized tables, thus reducing complexity in a database and enhancing data accessibility of users. Based on this finding, we propose an ensemble framework of databases for healthcare applications.« less

Constant size descriptors for accurate machine learning models of molecular properties

NASA Astrophysics Data System (ADS)

Collins, Christopher R.; Gordon, Geoffrey J.; von Lilienfeld, O. Anatole; Yaron, David J.

2018-06-01

Two different classes of molecular representations for use in machine learning of thermodynamic and electronic properties are studied. The representations are evaluated by monitoring the performance of linear and kernel ridge regression models on well-studied data sets of small organic molecules. One class of representations studied here counts the occurrence of bonding patterns in the molecule. These require only the connectivity of atoms in the molecule as may be obtained from a line diagram or a SMILES string. The second class utilizes the three-dimensional structure of the molecule. These include the Coulomb matrix and Bag of Bonds, which list the inter-atomic distances present in the molecule, and Encoded Bonds, which encode such lists into a feature vector whose length is independent of molecular size. Encoded Bonds' features introduced here have the advantage of leading to models that may be trained on smaller molecules and then used successfully on larger molecules. A wide range of feature sets are constructed by selecting, at each rank, either a graph or geometry-based feature. Here, rank refers to the number of atoms involved in the feature, e.g., atom counts are rank 1, while Encoded Bonds are rank 2. For atomization energies in the QM7 data set, the best graph-based feature set gives a mean absolute error of 3.4 kcal/mol. Inclusion of 3D geometry substantially enhances the performance, with Encoded Bonds giving 2.4 kcal/mol, when used alone, and 1.19 kcal/mol, when combined with graph features.

Students' Use of Mathematical Representations in Problem Solving.

ERIC Educational Resources Information Center

Santos-Trigo, Manuel

2002-01-01

Documents the experiences of 25 first-year university students with regard to the kinds of tasks calculus instructors should design in order to engage students in mathematical practices that often require the use of a graphing calculator. (MM)

GOGrapher: A Python library for GO graph representation and analysis

PubMed Central

Muller, Brian; Richards, Adam J; Jin, Bo; Lu, Xinghua

2009-01-01

Background The Gene Ontology is the most commonly used controlled vocabulary for annotating proteins. The concepts in the ontology are organized as a directed acyclic graph, in which a node corresponds to a biological concept and a directed edge denotes the parent-child semantic relationship between a pair of terms. A large number of protein annotations further create links between proteins and their functional annotations, reflecting the contemporary knowledge about proteins and their functional relationships. This leads to a complex graph consisting of interleaved biological concepts and their associated proteins. What is needed is a simple, open source library that provides tools to not only create and view the Gene Ontology graph, but to analyze and manipulate it as well. Here we describe the development and use of GOGrapher, a Python library that can be used for the creation, analysis, manipulation, and visualization of Gene Ontology related graphs. Findings An object-oriented approach was adopted to organize the hierarchy of the graphs types and associated classes. An Application Programming Interface is provided through which different types of graphs can be pragmatically created, manipulated, and visualized. GOGrapher has been successfully utilized in multiple research projects, e.g., a graph-based multi-label text classifier for protein annotation. Conclusion The GOGrapher project provides a reusable programming library designed for the manipulation and analysis of Gene Ontology graphs. The library is freely available for the scientific community to use and improve. PMID:19583843

Graph-based geometric-iconic guide-wire tracking.

PubMed

Honnorat, Nicolas; Vaillant, Régis; Paragios, Nikos

2011-01-01

In this paper we introduce a novel hybrid graph-based approach for Guide-wire tracking. The image support is captured by steerable filters and improved through tensor voting. Then, a graphical model is considered that represents guide-wire extraction/tracking through a B-spline control-point model. Points with strong geometric interest (landmarks) are automatically determined and anchored to such a representation. Tracking is then performed through discrete MRFs that optimize the spatio-temporal positions of the control points while establishing landmark temporal correspondences. Promising results demonstrate the potentials of our method.

The Representation of Abstract Words: Why Emotion Matters

ERIC Educational Resources Information Center

Kousta, Stavroula-Thaleia; Vigliocco, Gabriella; Vinson, David P.; Andrews, Mark; Del Campo, Elena

2011-01-01

Although much is known about the representation and processing of concrete concepts, knowledge of what abstract semantics might be is severely limited. In this article we first address the adequacy of the 2 dominant accounts (dual coding theory and the context availability model) put forward in order to explain representation and processing…

Representation learning via Dual-Autoencoder for recommendation.

PubMed

Zhuang, Fuzhen; Zhang, Zhiqiang; Qian, Mingda; Shi, Chuan; Xie, Xing; He, Qing

2017-06-01

Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And many subsequent works consider external information, e.g., social relationships of users and items' attributions, to improve the recommendation performance under the matrix factorization framework. However, matrix factorization methods may not make full use of the limited information from rating or check-in matrices, and achieve unsatisfying results. Recently, deep learning has proven able to learn good representation in natural language processing, image classification, and so on. Along this line, we propose a new representation learning framework called Recommendation via Dual-Autoencoder (ReDa). In this framework, we simultaneously learn the new hidden representations of users and items using autoencoders, and minimize the deviations of training data by the learnt representations of users and items. Based on this framework, we develop a gradient descent method to learn hidden representations. Extensive experiments conducted on several real-world data sets demonstrate the effectiveness of our proposed method compared with state-of-the-art matrix factorization based methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spatial analysis of bus transport networks using network theory

NASA Astrophysics Data System (ADS)

Shanmukhappa, Tanuja; Ho, Ivan Wang-Hei; Tse, Chi Kong

2018-07-01

In this paper, we analyze the bus transport network (BTN) structure considering the spatial embedding of the network for three cities, namely, Hong Kong (HK), London (LD), and Bengaluru (BL). We propose a novel approach called supernode graph structuring for modeling the bus transport network. A static demand estimation procedure is proposed to assign the node weights by considering the points of interests (POIs) and the population distribution in the city over various localized zones. In addition, the end-to-end delay is proposed as a parameter to measure the topological efficiency of the bus networks instead of the shortest distance measure used in previous works. With the aid of supernode graph representation, important network parameters are analyzed for the directed, weighted and geo-referenced bus transport networks. It is observed that the supernode concept has significant advantage in analyzing the inherent topological behavior. For instance, the scale-free and small-world behavior becomes evident with supernode representation as compared to conventional or regular graph representation for the Hong Kong network. Significant improvement in clustering, reduction in path length, and increase in centrality values are observed in all the three networks with supernode representation. The correlation between topologically central nodes and the geographically central nodes reveals the interesting fact that the proposed static demand estimation method for assigning node weights aids in better identifying the geographically significant nodes in the network. The impact of these geographically significant nodes on the local traffic behavior is demonstrated by simulation using the SUMO (Simulation of Urban Mobility) tool which is also supported by real-world empirical data, and our results indicate that the traffic speed around a particular bus stop can reach a jammed state from a free flow state due to the presence of these geographically important nodes. A comparison of the simulation and the empirical data provides useful information on how bus operators can better plan their routes and deploy stops considering the geographically significant nodes.

Men's interpretations of graphical information in a videotape decision aid 1

PubMed Central

Pylar, Jan; Wills, Celia E.; Lillie, Janet; Rovner, David R.; Kelly‐Blake, Karen; Holmes‐Rovner, Margaret

2007-01-01

Abstract Objective  To examine men's interpretations of graphical information types viewed in a high‐quality, previously tested videotape decision aid (DA). Setting, participants, design  A community‐dwelling sample of men >50 years of age (N = 188) balanced by education (college/non‐college) and race (Black/White) were interviewed just following their viewing of a videotape DA. A descriptive study design was used to examine men's interpretations of a representative sample of the types of graphs that were shown in the benign prostatic hyperplasia videotape DA. Main variables studied  Men provided their interpretation of graphs information presented in three formats that varied in complexity: pictograph, line and horizontal bar graph. Audiotape transcripts of men's responses were coded for meaning and content‐related interpretation statements. Results  Men provided both meaning and content‐focused interpretations of the graphs. Accuracy of interpretation was lower than hypothesized on the basis of literature review (85.4% for pictograph, 65.7% for line graph, 47.8% for horizontal bar graph). Accuracy for pictograph and line graphs was associated with education level,  = 3.94, P = 0.047, and  = 7.55, P = 0.006, respectively. Accuracy was uncorrelated with men's reported liking of the graphs,  = 2.00, P = 0.441. Conclusion  While men generally liked the DA, accuracy of graphs interpretation was associated with format complexity and education level. Graphs are often recommended to improve comprehension of information in DAs. However, additional evaluation is needed in experimental and naturalistic observational settings to develop best practice standards for data representation. PMID:17524011

A Representation for Fermionic Correlation Functions

NASA Astrophysics Data System (ADS)

Feldman, Joel; Knörrer, Horst; Trubowitz, Eugene

Let dμS(a) be a Gaussian measure on the finitely generated Grassmann algebra A. Given an even W(a)∈A, we construct an operator R on A such that for all f(a)∈A. This representation of the Schwinger functional iteratively builds up Feynman graphs by successively appending lines farther and farther from f. It allows the Pauli exclusion principle to be implemented quantitatively by a simple application of Gram's inequality.

Graph theory enables drug repurposing--how a mathematical model can drive the discovery of hidden mechanisms of action.

PubMed

Gramatica, Ruggero; Di Matteo, T; Giorgetti, Stefano; Barbiani, Massimo; Bevec, Dorian; Aste, Tomaso

2014-01-01

We introduce a methodology to efficiently exploit natural-language expressed biomedical knowledge for repurposing existing drugs towards diseases for which they were not initially intended. Leveraging on developments in Computational Linguistics and Graph Theory, a methodology is defined to build a graph representation of knowledge, which is automatically analysed to discover hidden relations between any drug and any disease: these relations are specific paths among the biomedical entities of the graph, representing possible Modes of Action for any given pharmacological compound. We propose a measure for the likeliness of these paths based on a stochastic process on the graph. This measure depends on the abundance of indirect paths between a peptide and a disease, rather than solely on the strength of the shortest path connecting them. We provide real-world examples, showing how the method successfully retrieves known pathophysiological Mode of Action and finds new ones by meaningfully selecting and aggregating contributions from known bio-molecular interactions. Applications of this methodology are presented, and prove the efficacy of the method for selecting drugs as treatment options for rare diseases.

Bond Graph Modeling of Chemiosmotic Biomolecular Energy Transduction.

PubMed

Gawthrop, Peter J

2017-04-01

Engineering systems modeling and analysis based on the bond graph approach has been applied to biomolecular systems. In this context, the notion of a Faraday-equivalent chemical potential is introduced which allows chemical potential to be expressed in an analogous manner to electrical volts thus allowing engineering intuition to be applied to biomolecular systems. Redox reactions, and their representation by half-reactions, are key components of biological systems which involve both electrical and chemical domains. A bond graph interpretation of redox reactions is given which combines bond graphs with the Faraday-equivalent chemical potential. This approach is particularly relevant when the biomolecular system implements chemoelectrical transduction - for example chemiosmosis within the key metabolic pathway of mitochondria: oxidative phosphorylation. An alternative way of implementing computational modularity using bond graphs is introduced and used to give a physically based model of the mitochondrial electron transport chain To illustrate the overall approach, this model is analyzed using the Faraday-equivalent chemical potential approach and engineering intuition is used to guide affinity equalisation: a energy based analysis of the mitochondrial electron transport chain.

Counterbalancing for serial order carryover effects in experimental condition orders.

PubMed

Brooks, Joseph L

2012-12-01

Reactions of neural, psychological, and social systems are rarely, if ever, independent of previous inputs and states. The potential for serial order carryover effects from one condition to the next in a sequence of experimental trials makes counterbalancing of condition order an essential part of experimental design. Here, a method is proposed for generating counterbalanced sequences for repeated-measures designs including those with multiple observations of each condition on one participant and self-adjacencies of conditions. Condition ordering is reframed as a graph theory problem. Experimental conditions are represented as vertices in a graph and directed edges between them represent temporal relationships between conditions. A counterbalanced trial order results from traversing an Euler circuit through such a graph in which each edge is traversed exactly once. This method can be generalized to counterbalance for higher order serial order carryover effects as well as to create intentional serial order biases. Modern graph theory provides tools for finding other types of paths through such graph representations, providing a tool for generating experimental condition sequences with useful properties. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Characterizing networks formed by P. polycephalum

NASA Astrophysics Data System (ADS)

Dirnberger, M.; Mehlhorn, K.

2017-06-01

We present a systematic study of the characteristic vein networks formed by the slime mold P. polycephalum. Our study is based on an extensive set of graph representations of slime mold networks. We analyze a total of 1998 graphs capturing growth and network formation of P. polycephalum as observed in 36 independent, identical, wet-lab experiments. Relying on concepts from graph theory such as face cycles and cuts as well as ideas from percolation theory, we establish a broad collection of individual observables taking into account various complementary aspects of P. polycephalum networks. As a whole, the collection is intended to serve as a specialized knowledge-base providing a comprehensive characterization of P. polycephalum networks. To this end, it contains individual as well as cumulative results for all investigated observables across all available data series, down to the level of single P. polycephalum graphs. Furthermore we include the raw numerical data as well as various plotting and analysis tools to ensure reproducibility and increase the usefulness of the collection. All our results are publicly available in an organized fashion in the slime mold graph repository (Smgr).

DIGITAL LINE GRAPHS - USGS

EPA Science Inventory

USGS DLGs are digital representations of program-quadrangle format and sectional maps. All DLG data distributed by the United States Geological Survey (USGS) are DLG-Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, ...

Teaching Mathematics: Computers in the Classroom.

ERIC Educational Resources Information Center

Borba, Marcelo C.

1995-01-01

Discusses some major changes that computers, calculators, and graphing calculators have brought to the mathematics classroom, including quasi-empirical studies in the classroom, use of multiple representations, emphasis on visualization, emphasis on tables, an altered classroom "ecology," and increasing complexity for students. (SR)

Ontology Design of Influential People Identification Using Centrality

NASA Astrophysics Data System (ADS)

Maulana Awangga, Rolly; Yusril, Muhammad; Setyawan, Helmi

2018-04-01

Identifying influential people as a node in a graph theory commonly calculated by social network analysis. The social network data has the user as node and edge as relation forming a friend relation graph. This research is conducting different meaning of every nodes relation in the social network. Ontology was perfect match science to describe the social network data as conceptual and domain. Ontology gives essential relationship in a social network more than a current graph. Ontology proposed as a standard for knowledge representation for the semantic web by World Wide Web Consortium. The formal data representation use Resource Description Framework (RDF) and Web Ontology Language (OWL) which is strategic for Open Knowledge-Based website data. Ontology used in the semantic description for a relationship in the social network, it is open to developing semantic based relationship ontology by adding and modifying various and different relationship to have influential people as a conclusion. This research proposes a model using OWL and RDF for influential people identification in the social network. The study use degree centrality, between ness centrality, and closeness centrality measurement for data validation. As a conclusion, influential people identification in Facebook can use proposed Ontology model in the Group, Photos, Photo Tag, Friends, Events and Works data.

Mathematical Representation Ability by Using Project Based Learning on the Topic of Statistics

NASA Astrophysics Data System (ADS)

Widakdo, W. A.

2017-09-01

Seeing the importance of the role of mathematics in everyday life, mastery of the subject areas of mathematics is a must. Representation ability is one of the fundamental ability that used in mathematics to make connection between abstract idea with logical thinking to understanding mathematics. Researcher see the lack of mathematical representation and try to find alternative solution to dolve it by using project based learning. This research use literature study from some books and articles in journals to see the importance of mathematical representation abiliy in mathemtics learning and how project based learning able to increase this mathematical representation ability on the topic of Statistics. The indicators for mathematical representation ability in this research classifies namely visual representation (picture, diagram, graph, or table); symbolize representation (mathematical statement. Mathematical notation, numerical/algebra symbol) and verbal representation (written text). This article explain about why project based learning able to influence student’s mathematical representation by using some theories in cognitive psychology, also showing the example of project based learning that able to use in teaching statistics, one of mathematics topic that very useful to analyze data.

Complexity and non-commutativity of learning operations on graphs.

PubMed

Atmanspacher, Harald; Filk, Thomas

2006-07-01

We present results from numerical studies of supervised learning operations in small recurrent networks considered as graphs, leading from a given set of input conditions to predetermined outputs. Graphs that have optimized their output for particular inputs with respect to predetermined outputs are asymptotically stable and can be characterized by attractors, which form a representation space for an associative multiplicative structure of input operations. As the mapping from a series of inputs onto a series of such attractors generally depends on the sequence of inputs, this structure is generally non-commutative. Moreover, the size of the set of attractors, indicating the complexity of learning, is found to behave non-monotonically as learning proceeds. A tentative relation between this complexity and the notion of pragmatic information is indicated.

Information jet: Handling noisy big data from weakly disconnected network

NASA Astrophysics Data System (ADS)

Aurongzeb, Deeder

Sudden aggregation (information jet) of large amount of data is ubiquitous around connected social networks, driven by sudden interacting and non-interacting events, network security threat attacks, online sales channel etc. Clustering of information jet based on time series analysis and graph theory is not new but little work is done to connect them with particle jet statistics. We show pre-clustering based on context can element soft network or network of information which is critical to minimize time to calculate results from noisy big data. We show difference between, stochastic gradient boosting and time series-graph clustering. For disconnected higher dimensional information jet, we use Kallenberg representation theorem (Kallenberg, 2005, arXiv:1401.1137) to identify and eliminate jet similarities from dense or sparse graph.

The Effects of Visual Cues and Learners' Field Dependence in Multiple External Representations Environment for Novice Program Comprehension

ERIC Educational Resources Information Center

Wei, Liew Tze; Sazilah, Salam

2012-01-01

This study investigated the effects of visual cues in multiple external representations (MER) environment on the learning performance of novices' program comprehension. Program codes and flowchart diagrams were used as dual representations in multimedia environment to deliver lessons on C-Programming. 17 field independent participants and 16 field…

An advanced method for classifying atmospheric circulation types based on prototypes connectivity graph

NASA Astrophysics Data System (ADS)

Zagouras, Athanassios; Argiriou, Athanassios A.; Flocas, Helena A.; Economou, George; Fotopoulos, Spiros

2012-11-01

Classification of weather maps at various isobaric levels as a methodological tool is used in several problems related to meteorology, climatology, atmospheric pollution and to other fields for many years. Initially the classification was performed manually. The criteria used by the person performing the classification are features of isobars or isopleths of geopotential height, depending on the type of maps to be classified. Although manual classifications integrate the perceptual experience and other unquantifiable qualities of the meteorology specialists involved, these are typically subjective and time consuming. Furthermore, during the last years different approaches of automated methods for atmospheric circulation classification have been proposed, which present automated and so-called objective classifications. In this paper a new method of atmospheric circulation classification of isobaric maps is presented. The method is based on graph theory. It starts with an intelligent prototype selection using an over-partitioning mode of fuzzy c-means (FCM) algorithm, proceeds to a graph formulation for the entire dataset and produces the clusters based on the contemporary dominant sets clustering method. Graph theory is a novel mathematical approach, allowing a more efficient representation of spatially correlated data, compared to the classical Euclidian space representation approaches, used in conventional classification methods. The method has been applied to the classification of 850 hPa atmospheric circulation over the Eastern Mediterranean. The evaluation of the automated methods is performed by statistical indexes; results indicate that the classification is adequately comparable with other state-of-the-art automated map classification methods, for a variable number of clusters.

Combinatorial approach to the representation of the Schur-Weyl duality in one-dimensional spin systems

NASA Astrophysics Data System (ADS)

Jakubczyk, Dorota; Jakubczyk, Paweł

2018-02-01

We propose combinatorial approach to the representation of Schur-Weyl duality in physical systems on the example of one-dimensional spin chains. Exploiting the Robinson-Schensted-Knuth algorithm, we perform decomposition of the dual group representations into irreducible representations in a fully combinatorial way. As representation space, we choose the Hilbert space of the spin chains, but this approach can be easily generalized to an arbitrary physical system where the Schur-Weyl duality works.

Importance of Kier-Hall topological indices in the QSAR of anticancer drug design.

PubMed

Nandi, Sisir; Bagchi, Manish C

2012-06-01

An important area of theoretical drug design research is quantitative structure activity relationship (QSAR) using structural invariants. The impetus for this research trend comes from various directions. Researchers in chemical documentation have searched for a set of invariants which will be more convenient than the adjacency matrix (or connection table) for the storage and comparison of chemical structures. Molecular structure can be looked upon as the representation of the relationship among its various constituents. The term molecular structure represents a set of nonequivalent and probably disjoint concepts. There is no reason to believe that when we discuss diverse topics (e.g. chemical synthesis, reaction rates, spectroscopic transitions, reaction mechanisms, and ab initio calculations) using the notion of molecular structure, the different meanings we attach to the single term molecular structure originate from the same fundamental concept. On the contrary, there is a theoretical and philosophical basis for the non-homogeneity of concepts covered by the term molecular structure. In the context of molecular science, the various concepts of molecular structure (e.g. classical valence bond representations, various chemical graph-theoretic representations, ball and spoke model of a molecule, representation of a molecule by minimum energy conformation, semi symbolic contour map of a molecule, or symbolic representation of chemical species by Hamiltonian operators) are model objects derived through different abstractions of the same chemical reality. In each instance, the equivalence class (concept or model of molecular structure) is generated by selecting certain aspects while ignoring some unique properties of those actual events. This explains the plurality of the concept of molecular structure and their autonomous nature, the word autonomous being used in the same sense that one concept is not logically derived from the other. At the most fundamental level, the structural model of an assembled entity (e.g. a molecule consisting of atoms) may be defined as the pattern of relationship among its parts as distinct from the values associated with them. Constitutional formulae of molecules are graphs where vertices represent the set of atoms and edges represent chemical bonds. The pattern of connectedness of atoms in a molecule is preserved by constitutional graphs. A graph (more correctly a non-directed graph) G = [V, E] consists of a finite non-empty set V of points together with a prescribed set E of unordered pairs of distinct points of V. Thus the mathematical characterization of structures represents structural invariants having successful applications in chemical documentation, characterization of molecular branching, enumeration of molecular constitutional associated with a particular empirical formula, calculation of quantum chemical parameters for the generation of quantitative structure-property-activity correlations. Kier developed a number of structural invariants which are now-a-days called as topological indices with wide range of practical applications for QSAR and drug design. The present paper is restricted to the review of Kier-Hall topological indices for QSAR and anticancer drug design for 2,5-bis(1-aziridinyl) 1,4-benzoquinone (BABQ), pyridopyrimidine, 4-anilinoquinazoline and 2-Phenylindoles compounds utilizing various statistical multivariate regression analyses.

How mutation affects evolutionary games on graphs

PubMed Central

Allen, Benjamin; Traulsen, Arne; Tarnita, Corina E.; Nowak, Martin A.

2011-01-01

Evolutionary dynamics are affected by population structure, mutation rates and update rules. Spatial or network structure facilitates the clustering of strategies, which represents a mechanism for the evolution of cooperation. Mutation dilutes this effect. Here we analyze how mutation influences evolutionary clustering on graphs. We introduce new mathematical methods to evolutionary game theory, specifically the analysis of coalescing random walks via generating functions. These techniques allow us to derive exact identity-by-descent (IBD) probabilities, which characterize spatial assortment on lattices and Cayley trees. From these IBD probabilities we obtain exact conditions for the evolution of cooperation and other game strategies, showing the dual effects of graph topology and mutation rate. High mutation rates diminish the clustering of cooperators, hindering their evolutionary success. Our model can represent either genetic evolution with mutation, or social imitation processes with random strategy exploration. PMID:21473871

A representational basis for the development of a distributed expert system for Space Shuttle flight control

NASA Technical Reports Server (NTRS)

Helly, J. J., Jr.; Bates, W. V.; Cutler, M.; Kelem, S.

1984-01-01

A new representation of malfunction procedure logic which permits the automation of these procedures using Boolean normal forms is presented. This representation is discussed in the context of the development of an expert system for space shuttle flight control including software and hardware implementation modes, and a distributed architecture. The roles and responsibility of the flight control team as well as previous work toward the development of expert systems for flight control support at Johnson Space Center are discussed. The notion of malfunction procedures as graphs is introduced as well as the concept of hardware-equivalence.

DIGITAL LINE GRAPHS - USGS 1:24,000

EPA Science Inventory

USGS DLGs are digital representations of program-quadrangle format and sectional maps. All DLG data distributed by the United States Geological Survey (USGS) are DLG-Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, ...

DIGITAL LINE GRAPHS - USGS 1:100,000

EPA Science Inventory

USGS DLGs are digital representations of program-quadrangle format and sectional maps. All DLG data distributed by the United States Geological Survey (USGS) are DLG-Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, ...

Hierarchical graphs for rule-based modeling of biochemical systems

PubMed Central

2011-01-01

Background In rule-based modeling, graphs are used to represent molecules: a colored vertex represents a component of a molecule, a vertex attribute represents the internal state of a component, and an edge represents a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions. A rule that specifies addition (removal) of an edge represents a class of association (dissociation) reactions, and a rule that specifies a change of a vertex attribute represents a class of reactions that affect the internal state of a molecular component. A set of rules comprises an executable model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system. Results For purposes of model annotation, we propose the use of hierarchical graphs to represent structural relationships among components and subcomponents of molecules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR) complex. We also show that computational methods developed for regular graphs can be applied to hierarchical graphs. In particular, we describe a generalization of Nauty, a graph isomorphism and canonical labeling algorithm. The generalized version of the Nauty procedure, which we call HNauty, can be used to assign canonical labels to hierarchical graphs or more generally to graphs with multiple edge types. The difference between the Nauty and HNauty procedures is minor, but for completeness, we provide an explanation of the entire HNauty algorithm. Conclusions Hierarchical graphs provide more intuitive formal representations of proteins and other structured molecules with multiple functional components than do the regular graphs of current languages for specifying rule-based models, such as the BioNetGen language (BNGL). Thus, the proposed use of hierarchical graphs should promote clarity and better understanding of rule-based models. PMID:21288338

Data and graph interpretation practices among preservice science teachers

NASA Astrophysics Data System (ADS)

Bowen, G. Michael; Roth, Wolff-Michael

2005-12-01

The interpretation of data and construction and interpretation of graphs are central practices in science, which, according to recent reform documents, science and mathematics teachers are expected to foster in their classrooms. However, are (preservice) science teachers prepared to teach inquiry with the purpose of transforming and analyzing data, and interpreting graphical representations? That is, are preservice science teachers prepared to teach data analysis and graph interpretation practices that scientists use by default in their everyday work? The present study was designed to answer these and related questions. We investigated the responses of preservice elementary and secondary science teachers to data and graph interpretation tasks. Our investigation shows that, despite considerable preparation, and for many, despite bachelor of science degrees, preservice teachers do not enact the (authentic) practices that scientists routinely do when asked to interpret data or graphs. Detailed analyses are provided of what data and graph interpretation practices actually were enacted. We conclude that traditional schooling emphasizes particular beliefs in the mathematical nature of the universe that make it difficult for many individuals to deal with data possessing the random variation found in measurements of natural phenomena. The results suggest that preservice teachers need more experience in engaging in data and graph interpretation practices originating in activities that provide the degree of variation in and complexity of data present in realistic investigations.

Content-based image retrieval by matching hierarchical attributed region adjacency graphs

NASA Astrophysics Data System (ADS)

Fischer, Benedikt; Thies, Christian J.; Guld, Mark O.; Lehmann, Thomas M.

2004-05-01

Content-based image retrieval requires a formal description of visual information. In medical applications, all relevant biological objects have to be represented by this description. Although color as the primary feature has proven successful in publicly available retrieval systems of general purpose, this description is not applicable to most medical images. Additionally, it has been shown that global features characterizing the whole image do not lead to acceptable results in the medical context or that they are only suitable for specific applications. For a general purpose content-based comparison of medical images, local, i.e. regional features that are collected on multiple scales must be used. A hierarchical attributed region adjacency graph (HARAG) provides such a representation and transfers image comparison to graph matching. However, building a HARAG from an image requires a restriction in size to be computationally feasible while at the same time all visually plausible information must be preserved. For this purpose, mechanisms for the reduction of the graph size are presented. Even with a reduced graph, the problem of graph matching remains NP-complete. In this paper, the Similarity Flooding approach and Hopfield-style neural networks are adapted from the graph matching community to the needs of HARAG comparison. Based on synthetic image material build from simple geometric objects, all visually similar regions were matched accordingly showing the framework's general applicability to content-based image retrieval of medical images.

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hyman, Jeffrey De'Haven; Hagberg, Aric Arild; Mohd-Yusof, Jamaludin

Here, we present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We also derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths.more » First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. We obtain accurate estimates of first passage times with an order of magnitude reduction of CPU time and mesh size using the proposed method.« less

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

DOE PAGES

Hyman, Jeffrey De'Haven; Hagberg, Aric Arild; Mohd-Yusof, Jamaludin; ...

2017-07-10

Here, we present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We also derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths.more » First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. We obtain accurate estimates of first passage times with an order of magnitude reduction of CPU time and mesh size using the proposed method.« less

A global/local affinity graph for image segmentation.

PubMed

Xiaofang Wang; Yuxing Tang; Masnou, Simon; Liming Chen

2015-04-01

Construction of a reliable graph capturing perceptual grouping cues of an image is fundamental for graph-cut based image segmentation methods. In this paper, we propose a novel sparse global/local affinity graph over superpixels of an input image to capture both short- and long-range grouping cues, and thereby enabling perceptual grouping laws, including proximity, similarity, continuity, and to enter in action through a suitable graph-cut algorithm. Moreover, we also evaluate three major visual features, namely, color, texture, and shape, for their effectiveness in perceptual segmentation and propose a simple graph fusion scheme to implement some recent findings from psychophysics, which suggest combining these visual features with different emphases for perceptual grouping. In particular, an input image is first oversegmented into superpixels at different scales. We postulate a gravitation law based on empirical observations and divide superpixels adaptively into small-, medium-, and large-sized sets. Global grouping is achieved using medium-sized superpixels through a sparse representation of superpixels' features by solving a ℓ0-minimization problem, and thereby enabling continuity or propagation of local smoothness over long-range connections. Small- and large-sized superpixels are then used to achieve local smoothness through an adjacent graph in a given feature space, and thus implementing perceptual laws, for example, similarity and proximity. Finally, a bipartite graph is also introduced to enable propagation of grouping cues between superpixels of different scales. Extensive experiments are carried out on the Berkeley segmentation database in comparison with several state-of-the-art graph constructions. The results show the effectiveness of the proposed approach, which outperforms state-of-the-art graphs using four different objective criteria, namely, the probabilistic rand index, the variation of information, the global consistency error, and the boundary displacement error.

Graph theoretical model of a sensorimotor connectome in zebrafish.

PubMed

Stobb, Michael; Peterson, Joshua M; Mazzag, Borbala; Gahtan, Ethan

2012-01-01

Mapping the detailed connectivity patterns (connectomes) of neural circuits is a central goal of neuroscience. The best quantitative approach to analyzing connectome data is still unclear but graph theory has been used with success. We present a graph theoretical model of the posterior lateral line sensorimotor pathway in zebrafish. The model includes 2,616 neurons and 167,114 synaptic connections. Model neurons represent known cell types in zebrafish larvae, and connections were set stochastically following rules based on biological literature. Thus, our model is a uniquely detailed computational representation of a vertebrate connectome. The connectome has low overall connection density, with 2.45% of all possible connections, a value within the physiological range. We used graph theoretical tools to compare the zebrafish connectome graph to small-world, random and structured random graphs of the same size. For each type of graph, 100 randomly generated instantiations were considered. Degree distribution (the number of connections per neuron) varied more in the zebrafish graph than in same size graphs with less biological detail. There was high local clustering and a short average path length between nodes, implying a small-world structure similar to other neural connectomes and complex networks. The graph was found not to be scale-free, in agreement with some other neural connectomes. An experimental lesion was performed that targeted three model brain neurons, including the Mauthner neuron, known to control fast escape turns. The lesion decreased the number of short paths between sensory and motor neurons analogous to the behavioral effects of the same lesion in zebrafish. This model is expandable and can be used to organize and interpret a growing database of information on the zebrafish connectome.

Visual Exploratory Search of Relationship Graphs on Smartphones

PubMed Central

Ouyang, Jianquan; Zheng, Hao; Kong, Fanbin; Liu, Tianming

2013-01-01

This paper presents a novel framework for Visual Exploratory Search of Relationship Graphs on Smartphones (VESRGS) that is composed of three major components: inference and representation of semantic relationship graphs on the Web via meta-search, visual exploratory search of relationship graphs through both querying and browsing strategies, and human-computer interactions via the multi-touch interface and mobile Internet on smartphones. In comparison with traditional lookup search methodologies, the proposed VESRGS system is characterized with the following perceived advantages. 1) It infers rich semantic relationships between the querying keywords and other related concepts from large-scale meta-search results from Google, Yahoo! and Bing search engines, and represents semantic relationships via graphs; 2) the exploratory search approach empowers users to naturally and effectively explore, adventure and discover knowledge in a rich information world of interlinked relationship graphs in a personalized fashion; 3) it effectively takes the advantages of smartphones’ user-friendly interfaces and ubiquitous Internet connection and portability. Our extensive experimental results have demonstrated that the VESRGS framework can significantly improve the users’ capability of seeking the most relevant relationship information to their own specific needs. We envision that the VESRGS framework can be a starting point for future exploration of novel, effective search strategies in the mobile Internet era. PMID:24223936

A Case against Computer Symbolic Manipulation in School Mathematics Today.

ERIC Educational Resources Information Center

Waits, Bert K.; Demana, Franklin

1992-01-01

Presented are two reasons discouraging computer symbol manipulation systems use in school mathematics at present: cost for computer laboratories or expensive pocket computers; and impracticality of exact solution representations. Although development with this technology in mathematics education advances, graphing calculators are recommended to…

Multilinear Graph Embedding: Representation and Regularization for Images.

PubMed

Chen, Yi-Lei; Hsu, Chiou-Ting

2014-02-01

Given a set of images, finding a compact and discriminative representation is still a big challenge especially when multiple latent factors are hidden in the way of data generation. To represent multifactor images, although multilinear models are widely used to parameterize the data, most methods are based on high-order singular value decomposition (HOSVD), which preserves global statistics but interprets local variations inadequately. To this end, we propose a novel method, called multilinear graph embedding (MGE), as well as its kernelization MKGE to leverage the manifold learning techniques into multilinear models. Our method theoretically links the linear, nonlinear, and multilinear dimensionality reduction. We also show that the supervised MGE encodes informative image priors for image regularization, provided that an image is represented as a high-order tensor. From our experiments on face and gait recognition, the superior performance demonstrates that MGE better represents multifactor images than classic methods, including HOSVD and its variants. In addition, the significant improvement in image (or tensor) completion validates the potential of MGE for image regularization.

Dissolving variables in connectionist combinatory logic

NASA Technical Reports Server (NTRS)

Barnden, John; Srinivas, Kankanahalli

1990-01-01

A connectionist system which can represent and execute combinator expressions to elegantly solve the variable binding problem in connectionist networks is presented. This system is a graph reduction machine utilizing graph representations and traversal mechanisms similar to ones described in the BoltzCONS system of Touretzky (1986). It is shown that, as combinators eliminate variables by introducing special functions, these functions can be connectionistically implemented without reintroducing variable binding. This approach 'dissolves' an important part of the variable binding problem, in that a connectionist system still has to manipulate complex data structures, but those structures and their manipulations are rendered more uniform.

Context Graphs as an Efficient and User-Friendly Method of Describing and Recognizing a Situation in AAL

PubMed Central

Olaru, Andrei; Florea, Adina Magda

2014-01-01

In the field of ambient assisted living, the best results are achieved with systems that are less intrusive and more intelligent, that can easily integrate both formal and informal caregivers and that can easily adapt to the changes in the situation of the elderly or disabled person. This paper presents a graph-based representation for context information and a simple and intuitive method for situation recognition. Both the input and the results are easy to visualize, understand and use. Experiments have been performed on several AAL-specific scenarios. PMID:24960085

Hologram production and representation for corrected image

NASA Astrophysics Data System (ADS)

Jiao, Gui Chao; Zhang, Rui; Su, Xue Mei

2015-12-01

In this paper, a CCD sensor device is used to record the distorted homemade grid images which are taken by a wide angle camera. The distorted images are corrected by using methods of position calibration and correction of gray with vc++ 6.0 and opencv software. Holography graphes for the corrected pictures are produced. The clearly reproduced images are obtained where Fresnel algorithm is used in graph processing by reducing the object and reference light from Fresnel diffraction to delete zero-order part of the reproduced images. The investigation is useful in optical information processing and image encryption transmission.

Representation mutations from standard genetic codes

NASA Astrophysics Data System (ADS)

Aisah, I.; Suyudi, M.; Carnia, E.; Suhendi; Supriatna, A. K.

2018-03-01

Graph is widely used in everyday life especially to describe model problem and describe it concretely and clearly. In addition graph is also used to facilitate solve various kinds of problems that are difficult to be solved by calculation. In Biology, graph can be used to describe the process of protein synthesis in DNA. Protein has an important role for DNA (deoxyribonucleic acid) or RNA (ribonucleic acid). Proteins are composed of amino acids. In this study, amino acids are related to genetics, especially the genetic code. The genetic code is also known as the triplet or codon code which is a three-letter arrangement of DNA nitrogen base. The bases are adenine (A), thymine (T), guanine (G) and cytosine (C). While on RNA thymine (T) is replaced with Urasil (U). The set of all Nitrogen bases in RNA is denoted by N = {C U, A, G}. This codon works at the time of protein synthesis inside the cell. This codon also encodes the stop signal as a sign of the stop of protein synthesis process. This paper will examine the process of protein synthesis through mathematical studies and present it in three-dimensional space or graph. The study begins by analysing the set of all codons denoted by NNN such that to obtain geometric representations. At this stage there is a matching between the sets of all nitrogen bases N with Z 2 × Z 2; C=(\\overline{0},\\overline{0}),{{U}}=(\\overline{0},\\overline{1}),{{A}}=(\\overline{1},\\overline{0}),{{G}}=(\\overline{1},\\overline{1}). By matching the algebraic structure will be obtained such as group, group Klein-4,Quotien group etc. With the help of Geogebra software, the set of all codons denoted by NNN can be presented in a three-dimensional space as a multicube NNN and also can be represented as a graph, so that can easily see relationship between the codon.

Towards a multilevel cognitive probabilistic representation of space

NASA Astrophysics Data System (ADS)

Tapus, Adriana; Vasudevan, Shrihari; Siegwart, Roland

2005-03-01

This paper addresses the problem of perception and representation of space for a mobile agent. A probabilistic hierarchical framework is suggested as a solution to this problem. The method proposed is a combination of probabilistic belief with "Object Graph Models" (OGM). The world is viewed from a topological optic, in terms of objects and relationships between them. The hierarchical representation that we propose permits an efficient and reliable modeling of the information that the mobile agent would perceive from its environment. The integration of both navigational and interactional capabilities through efficient representation is also addressed. Experiments on a set of images taken from the real world that validate the approach are reported. This framework draws on the general understanding of human cognition and perception and contributes towards the overall efforts to build cognitive robot companions.

A logical foundation for representation of clinical data.

PubMed Central

Campbell, K E; Das, A K; Musen, M A

1994-01-01

OBJECTIVE: A general framework for representation of clinical data that provides a declarative semantics of terms and that allows developers to define explicitly the relationships among both terms and combinations of terms. DESIGN: Use of conceptual graphs as a standard representation of logic and of an existing standardized vocabulary, the Systematized Nomenclature of Medicine (SNOMED International), for lexical elements. Concepts such as time, anatomy, and uncertainty must be modeled explicitly in a way that allows relation of these foundational concepts to surface-level clinical descriptions in a uniform manner. RESULTS: The proposed framework was used to model a simple radiology report, which included temporal references. CONCLUSION: Formal logic provides a framework for formalizing the representation of medical concepts. Actual implementations will be required to evaluate the practicality of this approach. PMID:7719805

Knowledge Representation Standards and Interchange Formats for Causal Graphs

NASA Technical Reports Server (NTRS)

Throop, David R.; Malin, Jane T.; Fleming, Land

2005-01-01

In many domains, automated reasoning tools must represent graphs of causally linked events. These include fault-tree analysis, probabilistic risk assessment (PRA), planning, procedures, medical reasoning about disease progression, and functional architectures. Each of these fields has its own requirements for the representation of causation, events, actors and conditions. The representations include ontologies of function and cause, data dictionaries for causal dependency, failure and hazard, and interchange formats between some existing tools. In none of the domains has a generally accepted interchange format emerged. The paper makes progress towards interoperability across the wide range of causal analysis methodologies. We survey existing practice and emerging interchange formats in each of these fields. Setting forth a set of terms and concepts that are broadly shared across the domains, we examine the several ways in which current practice represents them. Some phenomena are difficult to represent or to analyze in several domains. These include mode transitions, reachability analysis, positive and negative feedback loops, conditions correlated but not causally linked and bimodal probability distributions. We work through examples and contrast the differing methods for addressing them. We detail recent work in knowledge interchange formats for causal trees in aerospace analysis applications in early design, safety and reliability. Several examples are discussed, with a particular focus on reachability analysis and mode transitions. We generalize the aerospace analysis work across the several other domains. We also recommend features and capabilities for the next generation of causal knowledge representation standards.

Melonic Phase Transition in Group Field Theory

NASA Astrophysics Data System (ADS)

Baratin, Aristide; Carrozza, Sylvain; Oriti, Daniele; Ryan, James; Smerlak, Matteo

2014-08-01

Group field theories have recently been shown to admit a 1/N expansion dominated by so-called `melonic graphs', dual to triangulated spheres. In this note, we deepen the analysis of this melonic sector. We obtain a combinatorial formula for the melonic amplitudes in terms of a graph polynomial related to a higher-dimensional generalization of the Kirchhoff tree-matrix theorem. Simple bounds on these amplitudes show the existence of a phase transition driven by melonic interaction processes. We restrict our study to the Boulatov-Ooguri models, which describe topological BF theories and are the basis for the construction of 4-dimensional models of quantum gravity.

Assembly planning based on subassembly extraction

NASA Technical Reports Server (NTRS)

Lee, Sukhan; Shin, Yeong Gil

1990-01-01

A method is presented for the automatic determination of assembly partial orders from a liaison graph representation of an assembly through the extraction of preferred subassemblies. In particular, the authors show how to select a set of tentative subassemblies by decomposing a liaison graph into a set of subgraphs based on feasibility and difficulty of disassembly, how to evaluate each of the tentative subassemblies in terms of assembly cost using the subassembly selection indices, and how to construct a hierarchical partial order graph (HPOG) as an assembly plan. The method provides an approach to assembly planning by identifying spatial parallelism in assembly as a means of constructing temporal relationships among assembly operations and solves the problem of finding a cost-effective assembly plan in a flexible environment. A case study of the assembly planning of a mechanical assembly is presented.

Identifying Threats Using Graph-based Anomaly Detection

NASA Astrophysics Data System (ADS)

Eberle, William; Holder, Lawrence; Cook, Diane

Much of the data collected during the monitoring of cyber and other infrastructures is structural in nature, consisting of various types of entities and relationships between them. The detection of threatening anomalies in such data is crucial to protecting these infrastructures. We present an approach to detecting anomalies in a graph-based representation of such data that explicitly represents these entities and relationships. The approach consists of first finding normative patterns in the data using graph-based data mining and then searching for small, unexpected deviations to these normative patterns, assuming illicit behavior tries to mimic legitimate, normative behavior. The approach is evaluated using several synthetic and real-world datasets. Results show that the approach has high truepositive rates, low false-positive rates, and is capable of detecting complex structural anomalies in real-world domains including email communications, cellphone calls and network traffic.

Graph-based layout analysis for PDF documents

NASA Astrophysics Data System (ADS)

Xu, Canhui; Tang, Zhi; Tao, Xin; Li, Yun; Shi, Cao

2013-03-01

To increase the flexibility and enrich the reading experience of e-book on small portable screens, a graph based method is proposed to perform layout analysis on Portable Document Format (PDF) documents. Digital born document has its inherent advantages like representing texts and fractional images in explicit form, which can be straightforwardly exploited. To integrate traditional image-based document analysis and the inherent meta-data provided by PDF parser, the page primitives including text, image and path elements are processed to produce text and non text layer for respective analysis. Graph-based method is developed in superpixel representation level, and page text elements corresponding to vertices are used to construct an undirected graph. Euclidean distance between adjacent vertices is applied in a top-down manner to cut the graph tree formed by Kruskal's algorithm. And edge orientation is then used in a bottom-up manner to extract text lines from each sub tree. On the other hand, non-textual objects are segmented by connected component analysis. For each segmented text and non-text composite, a 13-dimensional feature vector is extracted for labelling purpose. The experimental results on selected pages from PDF books are presented.

Probabilistic graphs as a conceptual and computational tool in hydrology and water management

NASA Astrophysics Data System (ADS)

Schoups, Gerrit

2014-05-01

Originally developed in the fields of machine learning and artificial intelligence, probabilistic graphs constitute a general framework for modeling complex systems in the presence of uncertainty. The framework consists of three components: 1. Representation of the model as a graph (or network), with nodes depicting random variables in the model (e.g. parameters, states, etc), which are joined together by factors. Factors are local probabilistic or deterministic relations between subsets of variables, which, when multiplied together, yield the joint distribution over all variables. 2. Consistent use of probability theory for quantifying uncertainty, relying on basic rules of probability for assimilating data into the model and expressing unknown variables as a function of observations (via the posterior distribution). 3. Efficient, distributed approximation of the posterior distribution using general-purpose algorithms that exploit model structure encoded in the graph. These attributes make probabilistic graphs potentially useful as a conceptual and computational tool in hydrology and water management (and beyond). Conceptually, they can provide a common framework for existing and new probabilistic modeling approaches (e.g. by drawing inspiration from other fields of application), while computationally they can make probabilistic inference feasible in larger hydrological models. The presentation explores, via examples, some of these benefits.

Automatic Molecular Design using Evolutionary Techniques

NASA Technical Reports Server (NTRS)

Globus, Al; Lawton, John; Wipke, Todd; Saini, Subhash (Technical Monitor)

1998-01-01

Molecular nanotechnology is the precise, three-dimensional control of materials and devices at the atomic scale. An important part of nanotechnology is the design of molecules for specific purposes. This paper describes early results using genetic software techniques to automatically design molecules under the control of a fitness function. The fitness function must be capable of determining which of two arbitrary molecules is better for a specific task. The software begins by generating a population of random molecules. The population is then evolved towards greater fitness by randomly combining parts of the better individuals to create new molecules. These new molecules then replace some of the worst molecules in the population. The unique aspect of our approach is that we apply genetic crossover to molecules represented by graphs, i.e., sets of atoms and the bonds that connect them. We present evidence suggesting that crossover alone, operating on graphs, can evolve any possible molecule given an appropriate fitness function and a population containing both rings and chains. Prior work evolved strings or trees that were subsequently processed to generate molecular graphs. In principle, genetic graph software should be able to evolve other graph representable systems such as circuits, transportation networks, metabolic pathways, computer networks, etc.

Graph theory findings in the pathophysiology of temporal lobe epilepsy

PubMed Central

Chiang, Sharon; Haneef, Zulfi

2014-01-01

Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Accumulating evidence has shown that TLE is a disorder of abnormal epileptogenic networks, rather than focal sources. Graph theory allows for a network-based representation of TLE brain networks, and has potential to illuminate characteristics of brain topology conducive to TLE pathophysiology, including seizure initiation and spread. We review basic concepts which we believe will prove helpful in interpreting results rapidly emerging from graph theory research in TLE. In addition, we summarize the current state of graph theory findings in TLE as they pertain its pathophysiology. Several common findings have emerged from the many modalities which have been used to study TLE using graph theory, including structural MRI, diffusion tensor imaging, surface EEG, intracranial EEG, magnetoencephalography, functional MRI, cell cultures, simulated models, and mouse models, involving increased regularity of the interictal network configuration, altered local segregation and global integration of the TLE network, and network reorganization of temporal lobe and limbic structures. As different modalities provide different views of the same phenomenon, future studies integrating data from multiple modalities are needed to clarify findings and contribute to the formation of a coherent theory on the pathophysiology of TLE. PMID:24831083

Path-sum solution of the Weyl quantum walk in 3 + 1 dimensions

NASA Astrophysics Data System (ADS)

D'Ariano, G. M.; Mosco, N.; Perinotti, P.; Tosini, A.

2017-10-01

We consider the Weyl quantum walk in 3+1 dimensions, that is a discrete-time walk describing a particle with two internal degrees of freedom moving on a Cayley graph of the group , which in an appropriate regime evolves according to Weyl's equation. The Weyl quantum walk was recently derived as the unique unitary evolution on a Cayley graph of that is homogeneous and isotropic. The general solution of the quantum walk evolution is provided here in the position representation, by the analytical expression of the propagator, i.e. transition amplitude from a node of the graph to another node in a finite number of steps. The quantum nature of the walk manifests itself in the interference of the paths on the graph joining the given nodes. The solution is based on the binary encoding of the admissible paths on the graph and on the semigroup structure of the walk transition matrices. This article is part of the themed issue `Second quantum revolution: foundational questions'.

Dual representation of lattice QCD with worldlines and worldsheets of Abelian color fluxes

NASA Astrophysics Data System (ADS)

Marchis, Carlotta; Gattringer, Christof

2018-02-01

We present a new dual representation for lattice QCD in terms of wordlines and worldsheets. The exact reformulation is carried out using the recently developed Abelian color flux method where the action is decomposed into commuting minimal terms that connect different colors on neighboring sites. Expanding the Boltzmann factors for these commuting terms allows one to reorganize the gauge field contributions according to links such that the gauge fields can be integrated out in closed form. The emerging constraints give the dual variables the structure of worldlines for the fermions and worldsheets for the gauge degrees of freedom. The partition sum has the form of a strong coupling expansion, and with the Abelian color flux approach discussed here all coefficients of the expansion are known in closed form. We present the dual form for three cases: pure SU(3) lattice gauge theory, strong coupling QCD and full QCD, and discuss in detail the constraints for the color fluxes and their physical interpretation.

Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding

PubMed Central

Ghanbari, Yasser; Smith, Alex R.; Schultz, Robert T.; Verma, Ragini

2014-01-01

Diffusion tensor imaging (DTI) offers rich insights into the physical characteristics of white matter (WM) fiber tracts and their development in the brain, facilitating a network representation of brain’s traffic pathways. Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these connectivity networks necessitates the development of methods that identify the connectivity building blocks or sub-network components that characterize the underlying variation in the population. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis. We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing variational sources in the population like age and pathology. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental variations. PMID:25037933

U.S. Geological Survey DLG-3 and Bureau of the Census TIGER data. Development and GIS applications

USGS Publications Warehouse

Batten, Lawrence G.

1990-01-01

The U.S. Geological Survey has been actively developing digital cartographic and geographic data and standards since the early 1970's. One product is Digital Line Graph data, which offer a consistently accurate source of base category geographic information. The Bureau of the Census has combined their Dual Independent Map Encoding data with the Geological Survey's 1:100,000-scale Digital Line Graph data to prepare for the 1990 decennial census. The resulting Topologically Integrated Geographic Encoding and Referencing data offer a wealth of information. A major area of research using these data is in transportation analysis. The attributes associated with Digital Line Graphs can be used to determine the average travel times along each segment. Geographic information system functions can then be used to optimize routes through the network and to generate street name lists. Additional aspects of the subject are discussed.

The Transition to Formal Thinking in Mathematics

ERIC Educational Resources Information Center

Tall, David

2008-01-01

This paper focuses on the changes in thinking involved in the transition from school mathematics to formal proof in pure mathematics at university. School mathematics is seen as a combination of visual representations, including geometry and graphs, together with symbolic calculations and manipulations. Pure mathematics in university shifts…

Representations of an Osmosis Problem.

ERIC Educational Resources Information Center

Zuckerman, June Trop

1998-01-01

Explores whether students with several years of high school science are able to represent an osmosis problem correctly. The study problem features a typical osmotic system with students expected to make a graph to show how the solution level in the stem of the funnel changes over time. (DDR)

Perception in statistical graphics

NASA Astrophysics Data System (ADS)

VanderPlas, Susan Ruth

There has been quite a bit of research on statistical graphics and visualization, generally focused on new types of graphics, new software to create graphics, interactivity, and usability studies. Our ability to interpret and use statistical graphics hinges on the interface between the graph itself and the brain that perceives and interprets it, and there is substantially less research on the interplay between graph, eye, brain, and mind than is sufficient to understand the nature of these relationships. The goal of the work presented here is to further explore the interplay between a static graph, the translation of that graph from paper to mental representation (the journey from eye to brain), and the mental processes that operate on that graph once it is transferred into memory (mind). Understanding the perception of statistical graphics should allow researchers to create more effective graphs which produce fewer distortions and viewer errors while reducing the cognitive load necessary to understand the information presented in the graph. Taken together, these experiments should lay a foundation for exploring the perception of statistical graphics. There has been considerable research into the accuracy of numerical judgments viewers make from graphs, and these studies are useful, but it is more effective to understand how errors in these judgments occur so that the root cause of the error can be addressed directly. Understanding how visual reasoning relates to the ability to make judgments from graphs allows us to tailor graphics to particular target audiences. In addition, understanding the hierarchy of salient features in statistical graphics allows us to clearly communicate the important message from data or statistical models by constructing graphics which are designed specifically for the perceptual system.

Lung vessel segmentation in CT images using graph-cuts

NASA Astrophysics Data System (ADS)

Zhai, Zhiwei; Staring, Marius; Stoel, Berend C.

2016-03-01

Accurate lung vessel segmentation is an important operation for lung CT analysis. Filters that are based on analyzing the eigenvalues of the Hessian matrix are popular for pulmonary vessel enhancement. However, due to their low response at vessel bifurcations and vessel boundaries, extracting lung vessels by thresholding the vesselness is not sufficiently accurate. Some methods turn to graph-cuts for more accurate segmentation, as it incorporates neighbourhood information. In this work, we propose a new graph-cuts cost function combining appearance and shape, where CT intensity represents appearance and vesselness from a Hessian-based filter represents shape. Due to the amount of voxels in high resolution CT scans, the memory requirement and time consumption for building a graph structure is very high. In order to make the graph representation computationally tractable, those voxels that are considered clearly background are removed from the graph nodes, using a threshold on the vesselness map. The graph structure is then established based on the remaining voxel nodes, source/sink nodes and the neighbourhood relationship of the remaining voxels. Vessels are segmented by minimizing the energy cost function with the graph-cuts optimization framework. We optimized the parameters used in the graph-cuts cost function and evaluated the proposed method with two manually labeled sub-volumes. For independent evaluation, we used 20 CT scans of the VESSEL12 challenge. The evaluation results of the sub-volume data show that the proposed method produced a more accurate vessel segmentation compared to the previous methods, with F1 score 0.76 and 0.69. In the VESSEL12 data-set, our method obtained a competitive performance with an area under the ROC curve of 0.975, especially among the binary submissions.

On the partition dimension of comb product of path and complete graph

NASA Astrophysics Data System (ADS)

Darmaji, Alfarisi, Ridho

2017-08-01

For a vertex v of a connected graph G(V, E) with vertex set V(G), edge set E(G) and S ⊆ V(G). Given an ordered partition Π = {S1, S2, S3, …, Sk} of the vertex set V of G, the representation of a vertex v ∈ V with respect to Π is the vector r(v|Π) = (d(v, S1), d(v, S2), …, d(v, Sk)), where d(v, Sk) represents the distance between the vertex v and the set Sk and d(v, Sk) = min{d(v, x)|x ∈ Sk}. A partition Π of V(G) is a resolving partition if different vertices of G have distinct representations, i.e., for every pair of vertices u, v ∈ V(G), r(u|Π) ≠ r(v|Π). The minimum k of Π resolving partition is a partition dimension of G, denoted by pd(G). Finding the partition dimension of G is classified to be a NP-Hard problem. In this paper, we will show that the partition dimension of comb product of path and complete graph. The results show that comb product of complete grapph Km and path Pn namely p d (Km⊳Pn)=m where m ≥ 3 and n ≥ 2 and p d (Pn⊳Km)=m where m ≥ 3, n ≥ 2 and m ≥ n.

A graph theoretic approach to scene matching

NASA Technical Reports Server (NTRS)

Ranganath, Heggere S.; Chipman, Laure J.

1991-01-01

The ability to match two scenes is a fundamental requirement in a variety of computer vision tasks. A graph theoretic approach to inexact scene matching is presented which is useful in dealing with problems due to imperfect image segmentation. A scene is described by a set of graphs, with nodes representing objects and arcs representing relationships between objects. Each node has a set of values representing the relations between pairs of objects, such as angle, adjacency, or distance. With this method of scene representation, the task in scene matching is to match two sets of graphs. Because of segmentation errors, variations in camera angle, illumination, and other conditions, an exact match between the sets of observed and stored graphs is usually not possible. In the developed approach, the problem is represented as an association graph, in which each node represents a possible mapping of an observed region to a stored object, and each arc represents the compatibility of two mappings. Nodes and arcs have weights indicating the merit or a region-object mapping and the degree of compatibility between two mappings. A match between the two graphs corresponds to a clique, or fully connected subgraph, in the association graph. The task is to find the clique that represents the best match. Fuzzy relaxation is used to update the node weights using the contextual information contained in the arcs and neighboring nodes. This simplifies the evaluation of cliques. A method of handling oversegmentation and undersegmentation problems is also presented. The approach is tested with a set of realistic images which exhibit many types of sementation errors.

Image understanding systems based on the unifying representation of perceptual and conceptual information and the solution of mid-level and high-level vision problems

NASA Astrophysics Data System (ADS)

Kuvychko, Igor

2001-10-01

Vision is a part of a larger information system that converts visual information into knowledge structures. These structures drive vision process, resolving ambiguity and uncertainty via feedback, and provide image understanding, that is an interpretation of visual information in terms of such knowledge models. A computer vision system based on such principles requires unifying representation of perceptual and conceptual information. Computer simulation models are built on the basis of graphs/networks. The ability of human brain to emulate similar graph/networks models is found. That means a very important shift of paradigm in our knowledge about brain from neural networks to the cortical software. Starting from the primary visual areas, brain analyzes an image as a graph-type spatial structure. Primary areas provide active fusion of image features on a spatial grid-like structure, where nodes are cortical columns. The spatial combination of different neighbor features cannot be described as a statistical/integral characteristic of the analyzed region, but uniquely characterizes such region itself. Spatial logic and topology naturally present in such structures. Mid-level vision processes like clustering, perceptual grouping, multilevel hierarchical compression, separation of figure from ground, etc. are special kinds of graph/network transformations. They convert low-level image structure into the set of more abstract ones, which represent objects and visual scene, making them easy for analysis by higher-level knowledge structures. Higher-level vision phenomena like shape from shading, occlusion, etc. are results of such analysis. Such approach gives opportunity not only to explain frequently unexplainable results of the cognitive science, but also to create intelligent computer vision systems that simulate perceptional processes in both what and where visual pathways. Such systems can open new horizons for robotic and computer vision industries.

Modeling multi-process connectivity in river deltas: extending the single layer network analysis to a coupled multilayer network framework

NASA Astrophysics Data System (ADS)

Tejedor, A.; Longjas, A.; Foufoula-Georgiou, E.

2017-12-01

Previous work [e.g. Tejedor et al., 2016 - GRL] has demonstrated the potential of using graph theory to study key properties of the structure and dynamics of river delta channel networks. Although the distribution of fluxes in river deltas is mostly driven by the connectivity of its channel network a significant part of the fluxes might also arise from connectivity between the channels and islands due to overland flow and seepage. This channel-island-subsurface interaction creates connectivity pathways which facilitate or inhibit transport depending on their degree of coupling. The question we pose here is how to collectively study system connectivity that emerges from the aggregated action of different processes (different in nature, intensity and time scales). Single-layer graphs as those introduced for delta channel networks are inadequate as they lack the ability to represent coupled processes, and neglecting across-process interactions can lead to mis-representation of the overall system dynamics. We present here a framework that generalizes the traditional representation of networks (single-layer graphs) to the so-called multi-layer networks or multiplex. A multi-layer network conceptualizes the overall connectivity arising from different processes as distinct graphs (layers), while allowing at the same time to represent interactions between layers by introducing interlayer links (across process interactions). We illustrate this framework using a study of the joint connectivity that arises from the coupling of the confined flow on the channel network and the overland flow on islands, on a prototype delta. We show the potential of the multi-layer framework to answer quantitatively questions related to the characteristic time scales to steady-state transport in the system as a whole when different levels of channel-island coupling are modulated by different magnitudes of discharge rates.

M-theoretic derivations of 4d-2d dualities: from a geometric Langlands duality for surfaces, to the AGT correspondence, to integrable systems

NASA Astrophysics Data System (ADS)

Tan, Meng-Chwan

2013-07-01

In part I, we extend our analysis in [arXiv:0807.1107], and show that a mathematically conjectured geometric Langlands duality for complex surfaces in [1], and its generalizations — which relate some cohomology of the moduli space of certain ("ramified") G-instantons to the integrable representations of the Langlands dual of certain affine (sub) G-algebras, where G is any compact Lie group — can be derived, purely physically, from the principle that the spacetime BPS spectra of string-dual M-theory compactifications ought to be equivalent. In part II, to the setup in part I, we introduce Omega-deformation via fluxbranes and add half-BPS boundary defects via M9-branes, and show that the celebrated AGT correspondence in [2, 3], and its generalizations — which essentially relate, among other things, some equivariant cohomology of the moduli space of certain ("ramified") G-instantons to the integrable representations of the Langlands dual of certain affine -algebras — can likewise be derived from the principle that the spacetime BPS spectra of string-dual M-theory compactifications ought to be equivalent. In part III, we consider various limits of our setup in part II, and connect our story to chiral fermions and integrable systems. Among other things, we derive the NekrasovOkounkov conjecture in [4] — which relates the topological string limit of the dual Nekrasov partition function for pure G to the integrable representations of the Langlands dual of an affine G-algebra — and also demonstrate that the Nekrasov-Shatashvili limit of the "fullyramified" Nekrasov instanton partition function for pure G is a simultaneous eigenfunction of the quantum Toda Hamiltonians associated with the Langlands dual of an affine G-algebra. Via the case with matter, we also make contact with Hitchin systems and the "ramified" geometric Langlands correspondence for curves.

Hologram representation of design data in an expert system knowledge base

NASA Technical Reports Server (NTRS)

Shiva, S. G.; Klon, Peter F.

1988-01-01

A novel representational scheme for design object descriptions is presented. An abstract notion of modules and signals is developed as a conceptual foundation for the scheme. This abstraction relates the objects to the meaning of system descriptions. Anchored on this abstraction, a representational model which incorporates dynamic semantics for these objects is presented. This representational model is called a hologram scheme since it represents dual level information, namely, structural and semantic. The benefits of this scheme are presented.

Coherent multiscale image processing using dual-tree quaternion wavelets.

PubMed

Chan, Wai Lam; Choi, Hyeokho; Baraniuk, Richard G

2008-07-01

The dual-tree quaternion wavelet transform (QWT) is a new multiscale analysis tool for geometric image features. The QWT is a near shift-invariant tight frame representation whose coefficients sport a magnitude and three phases: two phases encode local image shifts while the third contains image texture information. The QWT is based on an alternative theory for the 2-D Hilbert transform and can be computed using a dual-tree filter bank with linear computational complexity. To demonstrate the properties of the QWT's coherent magnitude/phase representation, we develop an efficient and accurate procedure for estimating the local geometrical structure of an image. We also develop a new multiscale algorithm for estimating the disparity between a pair of images that is promising for image registration and flow estimation applications. The algorithm features multiscale phase unwrapping, linear complexity, and sub-pixel estimation accuracy.

Action, outcome, and value: a dual-system framework for morality.

PubMed

Cushman, Fiery

2013-08-01

Dual-system approaches to psychology explain the fundamental properties of human judgment, decision making, and behavior across diverse domains. Yet, the appropriate characterization of each system is a source of debate. For instance, a large body of research on moral psychology makes use of the contrast between "emotional" and "rational/cognitive" processes, yet even the chief proponents of this division recognize its shortcomings. Largely independently, research in the computational neurosciences has identified a broad division between two algorithms for learning and choice derived from formal models of reinforcement learning. One assigns value to actions intrinsically based on past experience, while another derives representations of value from an internally represented causal model of the world. This division between action- and outcome-based value representation provides an ideal framework for a dual-system theory in the moral domain.

A new formal graphic language for the representation of complex energy distribution systems

NASA Astrophysics Data System (ADS)

Benes, E.; Viehboeck, F. P.

A schematic notation system for the representation in design and analysis of multi-component heating systems is presented. This graphic language is clear and rigorous and allows quick changes between two basic levels of abstraction, as shown by two examples: a swimming pool with combined solar/electric heating system and the low temperature heating system of the Institute of Molecular Biology in Salzburg, Austria. The notation's 'energy path graphs' are more adequate for judging the relative merits of alternative system configurations than commonly used simplified installation schemes.

On the Road to Graphicacy: The Learning of Graphical Representation Systems

ERIC Educational Resources Information Center

Postigo, Yolanda; Pozo, Juan Ignacio

2004-01-01

This article examines the learning of different types of graphic information by subjects with different levels of education and knowledge of the content represented. Three levels of graphic information learning were distinguished (explicit, implicit, and conceptual information processing) and two experiments were conducted, looking at graph and…

From the Laboratory to the Classroom: A Technology-Intensive Curriculum for Functions and Graphs.

ERIC Educational Resources Information Center

Magidson, Susan

1992-01-01

Addresses the challenges, risks, and rewards of teaching about linear functions in a technology-rich environment from a constructivist perspective. Describes an algebra class designed for junior high school students that focuses of the representations and real-world applications of linear functions. (MDH)

The "No Crossing Constraint" in Autosegmental Phonology.

ERIC Educational Resources Information Center

Coleman, John; Local, John

A discussion of autosegmental phonology (AP), a theory of phonological representation that uses graphs rather than strings as the central data structure, considers its principal constraint, the "No Crossing Constraint" (NCC). The NCC is the statement that in a well-formed autosegmental diagram, lines of association may not cross. After…

Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing

NASA Astrophysics Data System (ADS)

Fan, Lei

Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each individual pixel in a hyperspectral image (HSI), a continuous spectrum is sampled as the spectral reflectance/radiance signature to facilitate identification of ground cover and surface material. The abundant spectrum knowledge allows all available information from the data to be mined. The superior qualities within hyperspectral imaging allow wide applications such as mineral exploration, agriculture monitoring, and ecological surveillance, etc. The processing of massive high-dimensional HSI datasets is a challenge since many data processing techniques have a computational complexity that grows exponentially with the dimension. Besides, a HSI dataset may contain a limited number of degrees of freedom due to the high correlations between data points and among the spectra. On the other hand, merely taking advantage of the sampled spectrum of individual HSI data point may produce inaccurate results due to the mixed nature of raw HSI data, such as mixed pixels, optical interferences and etc. Fusion strategies are widely adopted in data processing to achieve better performance, especially in the field of classification and clustering. There are mainly three types of fusion strategies, namely low-level data fusion, intermediate-level feature fusion, and high-level decision fusion. Low-level data fusion combines multi-source data that is expected to be complementary or cooperative. Intermediate-level feature fusion aims at selection and combination of features to remove redundant information. Decision level fusion exploits a set of classifiers to provide more accurate results. The fusion strategies have wide applications including HSI data processing. With the fast development of multiple remote sensing modalities, e.g. Very High Resolution (VHR) optical sensors, LiDAR, etc., fusion of multi-source data can in principal produce more detailed information than each single source. On the other hand, besides the abundant spectral information contained in HSI data, features such as texture and shape may be employed to represent data points from a spatial perspective. Furthermore, feature fusion also includes the strategy of removing redundant and noisy features in the dataset. One of the major problems in machine learning and pattern recognition is to develop appropriate representations for complex nonlinear data. In HSI processing, a particular data point is usually described as a vector with coordinates corresponding to the intensities measured in the spectral bands. This vector representation permits the application of linear and nonlinear transformations with linear algebra to find an alternative representation of the data. More generally, HSI is multi-dimensional in nature and the vector representation may lose the contextual correlations. Tensor representation provides a more sophisticated modeling technique and a higher-order generalization to linear subspace analysis. In graph theory, data points can be generalized as nodes with connectivities measured from the proximity of a local neighborhood. The graph-based framework efficiently characterizes the relationships among the data and allows for convenient mathematical manipulation in many applications, such as data clustering, feature extraction, feature selection and data alignment. In this thesis, graph-based approaches applied in the field of multi-source feature and data fusion in remote sensing area are explored. We will mainly investigate the fusion of spatial, spectral and LiDAR information with linear and multilinear algebra under graph-based framework for data clustering and classification problems.

Fisher metric, geometric entanglement, and spin networks

NASA Astrophysics Data System (ADS)

Chirco, Goffredo; Mele, Fabio M.; Oriti, Daniele; Vitale, Patrizia

2018-02-01

Starting from recent results on the geometric formulation of quantum mechanics, we propose a new information geometric characterization of entanglement for spin network states in the context of quantum gravity. For the simple case of a single-link fixed graph (Wilson line), we detail the construction of a Riemannian Fisher metric tensor and a symplectic structure on the graph Hilbert space, showing how these encode the whole information about separability and entanglement. In particular, the Fisher metric defines an entanglement monotone which provides a notion of distance among states in the Hilbert space. In the maximally entangled gauge-invariant case, the entanglement monotone is proportional to a power of the area of the surface dual to the link thus supporting a connection between entanglement and the (simplicial) geometric properties of spin network states. We further extend such analysis to the study of nonlocal correlations between two nonadjacent regions of a generic spin network graph characterized by the bipartite unfolding of an intertwiner state. Our analysis confirms the interpretation of spin network bonds as a result of entanglement and to regard the same spin network graph as an information graph, whose connectivity encodes, both at the local and nonlocal level, the quantum correlations among its parts. This gives a further connection between entanglement and geometry.

Analyzing cross-college course enrollments via contextual graph mining

PubMed Central

Liu, Xiaozhong; Chen, Yan

2017-01-01

The ability to predict what courses a student may enroll in the coming semester plays a pivotal role in the allocation of learning resources, which is a hot topic in the domain of educational data mining. In this study, we propose an innovative approach to characterize students’ cross-college course enrollments by leveraging a novel contextual graph. Specifically, different kinds of variables, such as students, courses, colleges and diplomas, as well as various types of variable relations, are utilized to depict the context of each variable, and then a representation learning algorithm node2vec is applied to extracting sophisticated graph-based features for the enrollment analysis. In this manner, the relations between any pair of variables can be measured quantitatively, which enables the variable type to transform from nominal to ratio. These graph-based features are examined by the random forest algorithm, and experiments on 24,663 students, 1,674 courses and 417,590 enrollment records demonstrate that the contextual graph can successfully improve analyzing the cross-college course enrollments, where three of the graph-based features have significantly stronger impacts on prediction accuracy than the others. Besides, the empirical results also indicate that the student’s course preference is the most important factor in predicting future course enrollments, which is consistent to the previous studies that acknowledge the course interest is a key point for course recommendations. PMID:29186171

Visualization and recommendation of large image collections toward effective sensemaking

NASA Astrophysics Data System (ADS)

Gu, Yi; Wang, Chaoli; Nemiroff, Robert; Kao, David; Parra, Denis

2016-03-01

In our daily lives, images are among the most commonly found data which we need to handle. We present iGraph, a graph-based approach for visual analytics of large image collections and their associated text information. Given such a collection, we compute the similarity between images, the distance between texts, and the connection between image and text to construct iGraph, a compound graph representation which encodes the underlying relationships among these images and texts. To enable effective visual navigation and comprehension of iGraph with tens of thousands of nodes and hundreds of millions of edges, we present a progressive solution that offers collection overview, node comparison, and visual recommendation. Our solution not only allows users to explore the entire collection with representative images and keywords but also supports detailed comparison for understanding and intuitive guidance for navigation. The visual exploration of iGraph is further enhanced with the implementation of bubble sets to highlight group memberships of nodes, suggestion of abnormal keywords or time periods based on text outlier detection, and comparison of four different recommendation solutions. For performance speedup, multiple graphics processing units and central processing units are utilized for processing and visualization in parallel. We experiment with two image collections and leverage a cluster driving a display wall of nearly 50 million pixels. We show the effectiveness of our approach by demonstrating experimental results and conducting a user study.

Analyzing cross-college course enrollments via contextual graph mining.

PubMed

Wang, Yongzhen; Liu, Xiaozhong; Chen, Yan

2017-01-01

The ability to predict what courses a student may enroll in the coming semester plays a pivotal role in the allocation of learning resources, which is a hot topic in the domain of educational data mining. In this study, we propose an innovative approach to characterize students' cross-college course enrollments by leveraging a novel contextual graph. Specifically, different kinds of variables, such as students, courses, colleges and diplomas, as well as various types of variable relations, are utilized to depict the context of each variable, and then a representation learning algorithm node2vec is applied to extracting sophisticated graph-based features for the enrollment analysis. In this manner, the relations between any pair of variables can be measured quantitatively, which enables the variable type to transform from nominal to ratio. These graph-based features are examined by the random forest algorithm, and experiments on 24,663 students, 1,674 courses and 417,590 enrollment records demonstrate that the contextual graph can successfully improve analyzing the cross-college course enrollments, where three of the graph-based features have significantly stronger impacts on prediction accuracy than the others. Besides, the empirical results also indicate that the student's course preference is the most important factor in predicting future course enrollments, which is consistent to the previous studies that acknowledge the course interest is a key point for course recommendations.

The effects of learner-generated representations versus computer-generated representations on physics problem solving

NASA Astrophysics Data System (ADS)

Price, Gwyneth A.

In this study, multiple external representations and Generative Learning Theory were used to design instruction that would facilitate physics learning. Specifically, the study looks at the learning differences that may occur when students are engaged in generating a graphical representation as compared to being presented with a computer-generated graph. It is hypothesized that by generating the graphical representation students will be able to overcome obstacles to integration and determine the relationships involved within a representation. In doing so, students will build a more complete mental model of the situation and be able to more readily use this information in transfer situations, thus improving their problem solving ability. Though the results of this study do not lend strong support for the hypothesis, the results are still informative and encouraging. Though several of the obstacles associated with learning from multiple representations such as cognitive load were cause for concern, those students with appropriate prior knowledge and familiarity with graphical representations were able to benefit from the generative activity. This finding indicates that if the issues are directly addressed within instruction, it may be that all students may be able to benefit from being actively engaged in generating representations.

Multiple kernels learning-based biological entity relationship extraction method.

PubMed

Dongliang, Xu; Jingchang, Pan; Bailing, Wang

2017-09-20

Automatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts included in MEDLINE, which is the most authoritative textual database in the field of biomedicine, and follow an exponential growth over time. This frantic expansion of the biomedical literature can often be difficult to absorb or manually analyze. Thus efficient and automated search engines are necessary to efficiently explore the biomedical literature using text mining techniques. The P, R, and F value of tag graph method in Aimed corpus are 50.82, 69.76, and 58.61%, respectively. The P, R, and F value of tag graph kernel method in other four evaluation corpuses are 2-5% higher than that of all-paths graph kernel. And The P, R and F value of feature kernel and tag graph kernel fuse methods is 53.43, 71.62 and 61.30%, respectively. The P, R and F value of feature kernel and tag graph kernel fuse methods is 55.47, 70.29 and 60.37%, respectively. It indicated that the performance of the two kinds of kernel fusion methods is better than that of simple kernel. In comparison with the all-paths graph kernel method, the tag graph kernel method is superior in terms of overall performance. Experiments show that the performance of the multi-kernels method is better than that of the three separate single-kernel method and the dual-mutually fused kernel method used hereof in five corpus sets.

Prototype Vector Machine for Large Scale Semi-Supervised Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Kai; Kwok, James T.; Parvin, Bahram

2009-04-29

Practicaldataminingrarelyfalls exactlyinto the supervisedlearning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computationalintensivenessofgraph-based SSLarises largely from the manifold or graph regularization, which in turn lead to large models that are dificult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highlyscalable,graph-based algorithm for large-scale SSL. Our key innovation is the use of"prototypes vectors" for effcient approximation on both the graph-based regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of themore » kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. We demonstrate encouraging performance and appealing scaling properties of the PVM on a number of machine learning benchmark data sets.« less

Graph-based real-time fault diagnostics

NASA Technical Reports Server (NTRS)

Padalkar, S.; Karsai, G.; Sztipanovits, J.

1988-01-01

A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.

Graph theory for feature extraction and classification: a migraine pathology case study.

PubMed

Jorge-Hernandez, Fernando; Garcia Chimeno, Yolanda; Garcia-Zapirain, Begonya; Cabrera Zubizarreta, Alberto; Gomez Beldarrain, Maria Angeles; Fernandez-Ruanova, Begonya

2014-01-01

Graph theory is also widely used as a representational form and characterization of brain connectivity network, as is machine learning for classifying groups depending on the features extracted from images. Many of these studies use different techniques, such as preprocessing, correlations, features or algorithms. This paper proposes an automatic tool to perform a standard process using images of the Magnetic Resonance Imaging (MRI) machine. The process includes pre-processing, building the graph per subject with different correlations, atlas, relevant feature extraction according to the literature, and finally providing a set of machine learning algorithms which can produce analyzable results for physicians or specialists. In order to verify the process, a set of images from prescription drug abusers and patients with migraine have been used. In this way, the proper functioning of the tool has been proved, providing results of 87% and 92% of success depending on the classifier used.

Social inertia and diversity in collaboration networks

NASA Astrophysics Data System (ADS)

Ramasco, J. J.

2007-04-01

Random graphs are useful tools to study social interactions. In particular, the use of weighted random graphs allows to handle a high level of information concerning which agents interact and in which degree the interactions take place. Taking advantage of this representation, we recently defined a magnitude, the Social Inertia, that measures the eagerness of agents to keep ties with previous partners. To study this magnitude, we used collaboration networks that are specially appropriate to obtain valid statitical results due to the large size of publically available databases. In this work, I study the Social Inertia in two of these empirical networks, IMDB movie database and condmat. More specifically, I focus on how the Inertia relates to other properties of the graphs, and show that the Inertia provides information on how the weight of neighboring edges correlates. A social interpretation of this effect is also offered.

Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal.

PubMed

Ge, Qi; Jing, Xiao-Yuan; Wu, Fei; Wei, Zhi-Hui; Xiao, Liang; Shao, Wen-Ze; Yue, Dong; Li, Hai-Bo

2017-07-01

Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images. To address this problem, we propose a structure-based low-rank model with graph nuclear norm regularization. We exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure. With the manifold structure information, a graph nuclear norm regularization is established and incorporated into a low-rank approximation model. We then prove that the graph-based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular-value thresholding algorithm. Extensive experiments on additive white Gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves a better performance than several state-of-the-art algorithms.

DOGMA: A Disk-Oriented Graph Matching Algorithm for RDF Databases

NASA Astrophysics Data System (ADS)

Bröcheler, Matthias; Pugliese, Andrea; Subrahmanian, V. S.

RDF is an increasingly important paradigm for the representation of information on the Web. As RDF databases increase in size to approach tens of millions of triples, and as sophisticated graph matching queries expressible in languages like SPARQL become increasingly important, scalability becomes an issue. To date, there is no graph-based indexing method for RDF data where the index was designed in a way that makes it disk-resident. There is therefore a growing need for indexes that can operate efficiently when the index itself resides on disk. In this paper, we first propose the DOGMA index for fast subgraph matching on disk and then develop a basic algorithm to answer queries over this index. This algorithm is then significantly sped up via an optimized algorithm that uses efficient (but correct) pruning strategies when combined with two different extensions of the index. We have implemented a preliminary system and tested it against four existing RDF database systems developed by others. Our experiments show that our algorithm performs very well compared to these systems, with orders of magnitude improvements for complex graph queries.

A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kou, Qiang; Wu, Si; Tolić, Nikola

Motivation: Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a “bird’s eye view” of intact proteoforms. The combinatorial explosion of various alterations on a protein may result inmore » billions of possible proteoforms, making proteoform identification a challenging computational problem. Results: We propose a new data structure, called the mass graph, for efficient representation of proteoforms and design mass graph alignment algorithms. We developed TopMG, a mass graph-based software tool for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry data sets showed that TopMG outperformed existing methods in identifying complex proteoforms.« less

Unwinding the hairball graph: Pruning algorithms for weighted complex networks

NASA Astrophysics Data System (ADS)

Dianati, Navid

2016-01-01

Empirical networks of weighted dyadic relations often contain "noisy" edges that alter the global characteristics of the network and obfuscate the most important structures therein. Graph pruning is the process of identifying the most significant edges according to a generative null model and extracting the subgraph consisting of those edges. Here, we focus on integer-weighted graphs commonly arising when weights count the occurrences of an "event" relating the nodes. We introduce a simple and intuitive null model related to the configuration model of network generation and derive two significance filters from it: the marginal likelihood filter (MLF) and the global likelihood filter (GLF). The former is a fast algorithm assigning a significance score to each edge based on the marginal distribution of edge weights, whereas the latter is an ensemble approach which takes into account the correlations among edges. We apply these filters to the network of air traffic volume between US airports and recover a geographically faithful representation of the graph. Furthermore, compared with thresholding based on edge weight, we show that our filters extract a larger and significantly sparser giant component.

Bruner's Three Forms of Representation Revisited: Action, Pictures and Words for Effective Computer Instruction.

ERIC Educational Resources Information Center

Presno, Caroline

1997-01-01

Discusses computer instruction in light of Bruner's theory of three forms of representation (action, icons, and symbols). Examines how studies regarding Paivio's dual-coding theory and studies focusing on procedural knowledge support Bruner's theory. Provides specific examples for instruction in three categories: demonstrations, pictures and…

Implications of Neuroscientific Evidence for the Cognitive Models of Post-Traumatic Stress Disorder

ERIC Educational Resources Information Center

Cruwys, Tegan; O'Kearney, Richard

2008-01-01

Brewin's dual representation theory, Ehlers and Clark's cognitive appraisal model, and Dalgleish's schematic, propositional, analogue and associative representational systems model are considered in the light of recent evidence on the neural substrates of post-traumatic stress disorder (PTSD). The models' proposals about the cognitive mechanism of…

Improving Preschoolers’ Arithmetic through Number Magnitude Training: The Impact of Non-Symbolic and Symbolic Training

PubMed Central

2016-01-01

The numerical cognition literature offers two views to explain numerical and arithmetical development. The unique-representation view considers the approximate number system (ANS) to represent the magnitude of both symbolic and non-symbolic numbers and to be the basis of numerical learning. In contrast, the dual-representation view suggests that symbolic and non-symbolic skills rely on different magnitude representations and that it is the ability to build an exact representation of symbolic numbers that underlies math learning. Support for these hypotheses has come mainly from correlative studies with inconsistent results. In this study, we developed two training programs aiming at enhancing the magnitude processing of either non-symbolic numbers or symbolic numbers and compared their effects on arithmetic skills. Fifty-six preschoolers were randomly assigned to one of three 10-session-training conditions: (1) non-symbolic training (2) symbolic training and (3) control training working on story understanding. Both numerical training conditions were significantly more efficient than the control condition in improving magnitude processing. Moreover, symbolic training led to a significantly larger improvement in arithmetic than did non-symbolic training and the control condition. These results support the dual-representation view. PMID:27875540

Dual deep modeling: multi-level modeling with dual potencies and its formalization in F-Logic.

PubMed

Neumayr, Bernd; Schuetz, Christoph G; Jeusfeld, Manfred A; Schrefl, Michael

2018-01-01

An enterprise database contains a global, integrated, and consistent representation of a company's data. Multi-level modeling facilitates the definition and maintenance of such an integrated conceptual data model in a dynamic environment of changing data requirements of diverse applications. Multi-level models transcend the traditional separation of class and object with clabjects as the central modeling primitive, which allows for a more flexible and natural representation of many real-world use cases. In deep instantiation, the number of instantiation levels of a clabject or property is indicated by a single potency. Dual deep modeling (DDM) differentiates between source potency and target potency of a property or association and supports the flexible instantiation and refinement of the property by statements connecting clabjects at different modeling levels. DDM comes with multiple generalization of clabjects, subsetting/specialization of properties, and multi-level cardinality constraints. Examples are presented using a UML-style notation for DDM together with UML class and object diagrams for the representation of two-level user views derived from the multi-level model. Syntax and semantics of DDM are formalized and implemented in F-Logic, supporting the modeler with integrity checks and rich query facilities.

Rule-based graph theory to enable exploration of the space system architecture design space

NASA Astrophysics Data System (ADS)

Arney, Dale Curtis

The primary goal of this research is to improve upon system architecture modeling in order to enable the exploration of design space options. A system architecture is the description of the functional and physical allocation of elements and the relationships, interactions, and interfaces between those elements necessary to satisfy a set of constraints and requirements. The functional allocation defines the functions that each system (element) performs, and the physical allocation defines the systems required to meet those functions. Trading the functionality between systems leads to the architecture-level design space that is available to the system architect. The research presents a methodology that enables the modeling of complex space system architectures using a mathematical framework. To accomplish the goal of improved architecture modeling, the framework meets five goals: technical credibility, adaptability, flexibility, intuitiveness, and exhaustiveness. The framework is technically credible, in that it produces an accurate and complete representation of the system architecture under consideration. The framework is adaptable, in that it provides the ability to create user-specified locations, steady states, and functions. The framework is flexible, in that it allows the user to model system architectures to multiple destinations without changing the underlying framework. The framework is intuitive for user input while still creating a comprehensive mathematical representation that maintains the necessary information to completely model complex system architectures. Finally, the framework is exhaustive, in that it provides the ability to explore the entire system architecture design space. After an extensive search of the literature, graph theory presents a valuable mechanism for representing the flow of information or vehicles within a simple mathematical framework. Graph theory has been used in developing mathematical models of many transportation and network flow problems in the past, where nodes represent physical locations and edges represent the means by which information or vehicles travel between those locations. In space system architecting, expressing the physical locations (low-Earth orbit, low-lunar orbit, etc.) and steady states (interplanetary trajectory) as nodes and the different means of moving between the nodes (propulsive maneuvers, etc.) as edges formulates a mathematical representation of this design space. The selection of a given system architecture using graph theory entails defining the paths that the systems take through the space system architecture graph. A path through the graph is defined as a list of edges that are traversed, which in turn defines functions performed by the system. A structure to compactly represent this information is a matrix, called the system map, in which the column indices are associated with the systems that exist and row indices are associated with the edges, or functions, to which each system has access. Several contributions have been added to the state of the art in space system architecture analysis. The framework adds the capability to rapidly explore the design space without the need to limit trade options or the need for user interaction during the exploration process. The unique mathematical representation of a system architecture, through the use of the adjacency, incidence, and system map matrices, enables automated design space exploration using stochastic optimization processes. The innovative rule-based graph traversal algorithm ensures functional feasibility of each system architecture that is analyzed, and the automatic generation of the system hierarchy eliminates the need for the user to manually determine the relationships between systems during or before the design space exploration process. Finally, the rapid evaluation of system architectures for various mission types enables analysis of the system architecture design space for multiple destinations within an evolutionary exploration program. (Abstract shortened by UMI.).

Modeling flow and transport in fracture networks using graphs

NASA Astrophysics Data System (ADS)

Karra, S.; O'Malley, D.; Hyman, J. D.; Viswanathan, H. S.; Srinivasan, G.

2018-03-01

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizations of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. The good accuracy and the low computational cost, with O (104) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.

Modeling flow and transport in fracture networks using graphs.

PubMed

Karra, S; O'Malley, D; Hyman, J D; Viswanathan, H S; Srinivasan, G

2018-03-01

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizations of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. The good accuracy and the low computational cost, with O(10^{4}) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.

Modeling flow and transport in fracture networks using graphs

DOE PAGES

Karra, S.; O'Malley, D.; Hyman, J. D.; ...

2018-03-09

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizationsmore » of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. In conclusion, the good accuracy and the low computational cost, with O(10 4) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.« less

Modeling flow and transport in fracture networks using graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karra, S.; O'Malley, D.; Hyman, J. D.

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizationsmore » of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. In conclusion, the good accuracy and the low computational cost, with O(10 4) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.« less

An efficient and scalable graph modeling approach for capturing information at different levels in next generation sequencing reads

PubMed Central

2013-01-01

Background Next generation sequencing technologies have greatly advanced many research areas of the biomedical sciences through their capability to generate massive amounts of genetic information at unprecedented rates. The advent of next generation sequencing has led to the development of numerous computational tools to analyze and assemble the millions to billions of short sequencing reads produced by these technologies. While these tools filled an important gap, current approaches for storing, processing, and analyzing short read datasets generally have remained simple and lack the complexity needed to efficiently model the produced reads and assemble them correctly. Results Previously, we presented an overlap graph coarsening scheme for modeling read overlap relationships on multiple levels. Most current read assembly and analysis approaches use a single graph or set of clusters to represent the relationships among a read dataset. Instead, we use a series of graphs to represent the reads and their overlap relationships across a spectrum of information granularity. At each information level our algorithm is capable of generating clusters of reads from the reduced graph, forming an integrated graph modeling and clustering approach for read analysis and assembly. Previously we applied our algorithm to simulated and real 454 datasets to assess its ability to efficiently model and cluster next generation sequencing data. In this paper we extend our algorithm to large simulated and real Illumina datasets to demonstrate that our algorithm is practical for both sequencing technologies. Conclusions Our overlap graph theoretic algorithm is able to model next generation sequencing reads at various levels of granularity through the process of graph coarsening. Additionally, our model allows for efficient representation of the read overlap relationships, is scalable for large datasets, and is practical for both Illumina and 454 sequencing technologies. PMID:24564333

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cohen, J; Dossa, D; Gokhale, M

Critical data science applications requiring frequent access to storage perform poorly on today's computing architectures. This project addresses efficient computation of data-intensive problems in national security and basic science by exploring, advancing, and applying a new form of computing called storage-intensive supercomputing (SISC). Our goal is to enable applications that simply cannot run on current systems, and, for a broad range of data-intensive problems, to deliver an order of magnitude improvement in price/performance over today's data-intensive architectures. This technical report documents much of the work done under LDRD 07-ERD-063 Storage Intensive Supercomputing during the period 05/07-09/07. The following chapters describe:more » (1) a new file I/O monitoring tool iotrace developed to capture the dynamic I/O profiles of Linux processes; (2) an out-of-core graph benchmark for level-set expansion of scale-free graphs; (3) an entity extraction benchmark consisting of a pipeline of eight components; and (4) an image resampling benchmark drawn from the SWarp program in the LSST data processing pipeline. The performance of the graph and entity extraction benchmarks was measured in three different scenarios: data sets residing on the NFS file server and accessed over the network; data sets stored on local disk; and data sets stored on the Fusion I/O parallel NAND Flash array. The image resampling benchmark compared performance of software-only to GPU-accelerated. In addition to the work reported here, an additional text processing application was developed that used an FPGA to accelerate n-gram profiling for language classification. The n-gram application will be presented at SC07 at the High Performance Reconfigurable Computing Technologies and Applications Workshop. The graph and entity extraction benchmarks were run on a Supermicro server housing the NAND Flash 40GB parallel disk array, the Fusion-io. The Fusion system specs are as follows: SuperMicro X7DBE Xeon Dual Socket Blackford Server Motherboard; 2 Intel Xeon Dual-Core 2.66 GHz processors; 1 GB DDR2 PC2-5300 RAM (2 x 512); 80GB Hard Drive (Seagate SATA II Barracuda). The Fusion board is presently capable of 4X in a PCIe slot. The image resampling benchmark was run on a dual Xeon workstation with NVIDIA graphics card (see Chapter 5 for full specification). An XtremeData Opteron+FPGA was used for the language classification application. We observed that these benchmarks are not uniformly I/O intensive. The only benchmark that showed greater that 50% of the time in I/O was the graph algorithm when it accessed data files over NFS. When local disk was used, the graph benchmark spent at most 40% of its time in I/O. The other benchmarks were CPU dominated. The image resampling benchmark and language classification showed order of magnitude speedup over software by using co-processor technology to offload the CPU-intensive kernels. Our experiments to date suggest that emerging hardware technologies offer significant benefit to boosting the performance of data-intensive algorithms. Using GPU and FPGA co-processors, we were able to improve performance by more than an order of magnitude on the benchmark algorithms, eliminating the processor bottleneck of CPU-bound tasks. Experiments with a prototype solid state nonvolative memory available today show 10X better throughput on random reads than disk, with a 2X speedup on a graph processing benchmark when compared to the use of local SATA disk.« less

Graph theory findings in the pathophysiology of temporal lobe epilepsy.

PubMed

Chiang, Sharon; Haneef, Zulfi

2014-07-01

Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Accumulating evidence has shown that TLE is a disorder of abnormal epileptogenic networks, rather than focal sources. Graph theory allows for a network-based representation of TLE brain networks, and has potential to illuminate characteristics of brain topology conducive to TLE pathophysiology, including seizure initiation and spread. We review basic concepts which we believe will prove helpful in interpreting results rapidly emerging from graph theory research in TLE. In addition, we summarize the current state of graph theory findings in TLE as they pertain its pathophysiology. Several common findings have emerged from the many modalities which have been used to study TLE using graph theory, including structural MRI, diffusion tensor imaging, surface EEG, intracranial EEG, magnetoencephalography, functional MRI, cell cultures, simulated models, and mouse models, involving increased regularity of the interictal network configuration, altered local segregation and global integration of the TLE network, and network reorganization of temporal lobe and limbic structures. As different modalities provide different views of the same phenomenon, future studies integrating data from multiple modalities are needed to clarify findings and contribute to the formation of a coherent theory on the pathophysiology of TLE. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

A strand graph semantics for DNA-based computation

PubMed Central

Petersen, Rasmus L.; Lakin, Matthew R.; Phillips, Andrew

2015-01-01

DNA nanotechnology is a promising approach for engineering computation at the nanoscale, with potential applications in biofabrication and intelligent nanomedicine. DNA strand displacement is a general strategy for implementing a broad range of nanoscale computations, including any computation that can be expressed as a chemical reaction network. Modelling and analysis of DNA strand displacement systems is an important part of the design process, prior to experimental realisation. As experimental techniques improve, it is important for modelling languages to keep pace with the complexity of structures that can be realised experimentally. In this paper we present a process calculus for modelling DNA strand displacement computations involving rich secondary structures, including DNA branches and loops. We prove that our calculus is also sufficiently expressive to model previous work on non-branching structures, and propose a mapping from our calculus to a canonical strand graph representation, in which vertices represent DNA strands, ordered sites represent domains, and edges between sites represent bonds between domains. We define interactions between strands by means of strand graph rewriting, and prove the correspondence between the process calculus and strand graph behaviours. Finally, we propose a mapping from strand graphs to an efficient implementation, which we use to perform modelling and simulation of DNA strand displacement systems with rich secondary structure. PMID:27293306

Visualizing risks in cancer communication: A systematic review of computer-supported visual aids.

PubMed

Stellamanns, Jan; Ruetters, Dana; Dahal, Keshav; Schillmoeller, Zita; Huebner, Jutta

2017-08-01

Health websites are becoming important sources for cancer information. Lay users, patients and carers seek support for critical decisions, but they are prone to common biases when quantitative information is presented. Graphical representations of risk data can facilitate comprehension, and interactive visualizations are popular. This review summarizes the evidence on computer-supported graphs that present risk data and their effects on various measures. The systematic literature search was conducted in several databases, including MEDLINE, EMBASE and CINAHL. Only studies with a controlled design were included. Relevant publications were carefully selected and critically appraised by two reviewers. Thirteen studies were included. Ten studies evaluated static graphs and three dynamic formats. Most decision scenarios were hypothetical. Static graphs could improve accuracy, comprehension, and behavioural intention. But the results were heterogeneous and inconsistent among the studies. Dynamic formats were not superior or even impaired performance compared to static formats. Static graphs show promising but inconsistent results, while research on dynamic visualizations is scarce and must be interpreted cautiously due to methodical limitations. Well-designed and context-specific static graphs can support web-based cancer risk communication in particular populations. The application of dynamic formats cannot be recommended and needs further research. Copyright © 2017 Elsevier B.V. All rights reserved.

Advanced Age Dissociates Dual Functions of the Perirhinal Cortex

PubMed Central

Burke, Sara N.; Maurer, Andrew P.; Nematollahi, Saman; Uprety, Ajay; Wallace, Jenelle L.

2014-01-01

The perirhinal cortex (PRC) is proposed to both represent high-order sensory information and maintain those representations across delays. These cognitive processes are required for recognition memory, which declines during normal aging. Whether or not advanced age affects the ability of PRC principal cells to support these dual roles, however, is not known. The current experiment recorded PRC neurons as young and aged rats traversed a track. When objects were placed on the track, a subset of the neurons became active at discrete locations adjacent to objects. Importantly, the aged rats had a lower proportion of neurons that were activated by objects. Once PRC activity patterns in the presence of objects were established, however, both age groups maintained these representations across delays up to 2 h. These data support the hypothesis that age-associated deficits in stimulus recognition arise from impairments in high-order stimulus representation rather than difficulty in sustaining stable activity patterns over time. PMID:24403147

Advanced age dissociates dual functions of the perirhinal cortex.

PubMed

Burke, Sara N; Maurer, Andrew P; Nematollahi, Saman; Uprety, Ajay; Wallace, Jenelle L; Barnes, Carol A

2014-01-08

The perirhinal cortex (PRC) is proposed to both represent high-order sensory information and maintain those representations across delays. These cognitive processes are required for recognition memory, which declines during normal aging. Whether or not advanced age affects the ability of PRC principal cells to support these dual roles, however, is not known. The current experiment recorded PRC neurons as young and aged rats traversed a track. When objects were placed on the track, a subset of the neurons became active at discrete locations adjacent to objects. Importantly, the aged rats had a lower proportion of neurons that were activated by objects. Once PRC activity patterns in the presence of objects were established, however, both age groups maintained these representations across delays up to 2 h. These data support the hypothesis that age-associated deficits in stimulus recognition arise from impairments in high-order stimulus representation rather than difficulty in sustaining stable activity patterns over time.

Atypical language representation in children with intractable temporal lobe epilepsy.

PubMed

Maulisova, Alice; Korman, Brandon; Rey, Gustavo; Bernal, Byron; Duchowny, Michael; Niederlova, Marketa; Krsek, Pavel; Novak, Vilem

2016-05-01

This study evaluated language organization in children with intractable epilepsy caused by temporal lobe focal cortical dysplasia (FCD) alone or dual pathology (temporal lobe FCD and hippocampal sclerosis, HS). We analyzed clinical, neurological, fMRI, neuropsychological, and histopathologic data in 46 pediatric patients with temporal lobe lesions who underwent excisional epilepsy surgery. The frequency of atypical language representation was similar in both groups, but children with dual pathology were more likely to be left-handed. Atypical receptive language cortex correlated with lower intellectual capacity, verbal abstract conceptualization, receptive language abilities, verbal working memory, and a history of status epilepticus but did not correlate with higher seizure frequency or early seizure onset. Histopathologic substrate had only a minor influence on neuropsychological status. Greater verbal comprehension deficits were noted in children with atypical receptive language representation, a risk factor for cognitive morbidity. Copyright © 2016 Elsevier Inc. All rights reserved.

Supporting Multimedia Learning with Visual Signalling and Animated Pedagogical Agent: Moderating Effects of Prior Knowledge

ERIC Educational Resources Information Center

Johnson, A. M.; Ozogul, G.; Reisslein, M.

2015-01-01

An experiment examined the effects of visual signalling to relevant information in multiple external representations and the visual presence of an animated pedagogical agent (APA). Students learned electric circuit analysis using a computer-based learning environment that included Cartesian graphs, equations and electric circuit diagrams. The…

Equivalent Expressions Using CAS and Paper-and-Pencil Techniques

ERIC Educational Resources Information Center

Fonger, Nicole L.

2014-01-01

How can the key concept of equivalent expressions be addressed so that students strengthen their representational fluency with symbols, graphs, and numbers? How can research inform the synergistic use of both paper-and-pencil analysis and computer algebra systems (CAS) in a classroom learning environment? These and other related questions have…

Plotting Points: Implications of "Over and Up" on Students' Covariational Reasoning

ERIC Educational Resources Information Center

Frank, Kristin M.

2016-01-01

In this study I investigate Saldanha and Thompson's (1998) claim that conceptualizing a coordinate pair in the Cartesian coordinate system as a multiplicative object, a way to unite two quantities' values, supports students in conceptualizing graphs as emergent representations of how two quantities' values change together. I presented three…

Review of "Conceptual Structures: Information Processing in Mind and Machine."

ERIC Educational Resources Information Center

Smoliar, Stephen W.

This review of the book, "Conceptual Structures: Information Processing in Mind and Machine," by John F. Sowa, argues that anyone who plans to get involved with issues of knowledge representation should have at least a passing acquaintance with Sowa's conceptual graphs for a database interface. (Used to model the underlying semantics of…

Using Microsoft Excel[R] to Calculate Descriptive Statistics and Create Graphs

ERIC Educational Resources Information Center

Carr, Nathan T.

2008-01-01

Descriptive statistics and appropriate visual representations of scores are important for all test developers, whether they are experienced testers working on large-scale projects, or novices working on small-scale local tests. Many teachers put in charge of testing projects do not know "why" they are important, however, and are utterly convinced…

Augmenting Paper-Based Reading Activity with Direct Access to Digital Materials and Scaffolded Questioning

ERIC Educational Resources Information Center

Chen, Nian-Shing; Teng, Daniel Chia-En; Lee, Cheng-Han; Kinshuk

2011-01-01

Comprehension is the goal of reading. However, students often encounter reading difficulties due to the lack of background knowledge and proper reading strategy. Unfortunately, print text provides very limited assistance to one's reading comprehension through its static knowledge representations such as symbols, charts, and graphs. Integrating…

DEMO: Action Recommendation for Cyber Resilience

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rodriguez, Luke R.; Curtis, Darren S.; Choudhury, Sutanay

In this demonstration we show the usefulness of our unifying graph-based model for the representation of infrastructure, behavior, and missions of cyber enterprise in both a software simulation and on an Amazon Web Services (AWS) instance. We show the effectiveness of our recommendation algorithm for preserving various system health metrics in both cases.

Developing Creativity and Abstraction in Representing Data

ERIC Educational Resources Information Center

South, Andy

2012-01-01

Creating charts and graphs is all about visual abstraction: the process of representing aspects of data with imagery that can be interpreted by the reader. Children may need help making the link between the "real" and the image. This abstraction can be achieved using symbols, size, colour and position. Where the representation is close to what…

Graphical Description of Johnson-Neyman Outcomes for Linear and Quadratic Regression Surfaces.

ERIC Educational Resources Information Center

Schafer, William D.; Wang, Yuh-Yin

A modification of the usual graphical representation of heterogeneous regressions is described that can aid in interpreting significant regions for linear or quadratic surfaces. The standard Johnson-Neyman graph is a bivariate plot with the criterion variable on the ordinate and the predictor variable on the abscissa. Regression surfaces are drawn…

Energy Blocks--A Physical Model for Teaching Energy Concepts

ERIC Educational Resources Information Center

Hertting, Scott

2016-01-01

Most physics educators would agree that energy is a very useful, albeit abstract topic. It is therefore important to use various methods to help the student internalize the concept of energy itself and its related ideas. These methods include using representations such as energy bar graphs, energy pie charts, or energy tracking diagrams.…

Embedded Multiprocessor Technology for VHSIC Insertion

NASA Technical Reports Server (NTRS)

Hayes, Paul J.

1990-01-01

Viewgraphs on embedded multiprocessor technology for VHSIC insertion are presented. The objective was to develop multiprocessor system technology providing user-selectable fault tolerance, increased throughput, and ease of application representation for concurrent operation. The approach was to develop graph management mapping theory for proper performance, model multiprocessor performance, and demonstrate performance in selected hardware systems.

A S[t]imulating Study of Map Projections: An Exploration Integrating Mathematics and Social Studies.

ERIC Educational Resources Information Center

Wilkins, Jesse L. M.; Hicks, David

2001-01-01

Presents a map-projection activity that combines mathematics and geography through investigating the proportion of land and water that covers the earth. Focuses on helping students become familiar with characteristics of different projections or representations of the world while estimating and graphing and encouraging them to investigate the…

Use of a Card Sort Task to Assess Students' Ability to Coordinate Three Levels of Representation in Chemistry

ERIC Educational Resources Information Center

Irby, Stefan M.; Phu, Andy L.; Borda, Emily J.; Haskell, Todd R.; Steed, Nicole; Meyer, Zachary

2016-01-01

There is much agreement among chemical education researchers that expertise in chemistry depends in part on the ability to coordinate understanding of phenomena on three levels: macroscopic (observable), sub-microscopic (atoms, molecules, and ions) and symbolic (chemical equations, graphs, etc.). We hypothesize this "level-coordination…

[Environmental Education Units.

ERIC Educational Resources Information Center

Minneapolis Independent School District 275, Minn.

Two of these three pamphlets describe methods of teaching young elementary school children the principles of sampling. Tiles of five colors are added to a tub and children sample these randomly; using the tiles as units for a graph, they draw a representation of the population. Pooling results leads to a more reliable sample. Practice is given in…

The Visual Side to Numeracy: Students' Sensemaking with Graphics

ERIC Educational Resources Information Center

Diezmann, Carmel; Lowrie, Tom; Sugars, Lindy; Logan, Tracy

2009-01-01

The 21st century has placed increasing demand on individuals' proficiency with a wide array of visual representations, that is graphics. Hence, proficiency with visual tasks needs to be embedded across the curriculum. In mathematics, various graphics (e.g., maps, charts, number lines, graphs) are used as means of communication of mathematical…

The role of picture of process (pp) on senior high school students’ collision concept learning activities and multirepresentation ability

NASA Astrophysics Data System (ADS)

Sutarto; Indrawati; Wicaksono, I.

2018-04-01

The objectives of the study are to describe the effect of PP collision concepts to high school students’ learning activities and multirepresentation abilities. This study was a quasi experimental with non- equivalent post-test only control group design. The population of this study were students who will learn the concept of collision in three state Senior High Schools in Indonesia, with a sample of each school 70 students, 35 students as an experimental group and 35 students as a control group. Technique of data collection were observation and test. The data were analized by descriptive and inferensial statistic. Student learning activities were: group discussions, describing vectors of collision events, and formulating problem-related issues of impact. Multirepresentation capabilities were student ability on image representation, verbal, mathematics, and graph. The results showed that the learning activities in the three aspects for the three high school average categorized good. The impact of using PP on students’ ability on image and graph representation were a significant impact, but for verbal and mathematical skills there are differences but not significant.

A large-grain mapping approach for multiprocessor systems through data flow model. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Kim, Hwa-Soo

1991-01-01

A large-grain level mapping method is presented of numerical oriented applications onto multiprocessor systems. The method is based on the large-grain data flow representation of the input application and it assumes a general interconnection topology of the multiprocessor system. The large-grain data flow model was used because such representation best exhibits inherited parallelism in many important applications, e.g., CFD models based on partial differential equations can be presented in large-grain data flow format, very effectively. A generalized interconnection topology of the multiprocessor architecture is considered, including such architectural issues as interprocessor communication cost, with the aim to identify the 'best matching' between the application and the multiprocessor structure. The objective is to minimize the total execution time of the input algorithm running on the target system. The mapping strategy consists of the following: (1) large-grain data flow graph generation from the input application using compilation techniques; (2) data flow graph partitioning into basic computation blocks; and (3) physical mapping onto the target multiprocessor using a priority allocation scheme for the computation blocks.

A python tool for the implementation of domain-specific languages

NASA Astrophysics Data System (ADS)

Dejanović, Igor; Vaderna, Renata; Milosavljević, Gordana; Simić, Miloš; Vuković, Željko

2017-07-01

In this paper we describe textX, a meta-language and a tool for building Domain-Specific Languages. It is implemented in Python using Arpeggio PEG (Parsing Expression Grammar) parser library. From a single language description (grammar) textX will build a parser and a meta-model (a.k.a. abstract syntax) of the language. The parser is used to parse textual representations of models conforming to the meta-model. As a result of parsing, a Python object graph will be automatically created. The structure of the object graph will conform to the meta-model defined by the grammar. This approach frees a developer from the need to manually analyse a parse tree and transform it to other suitable representation. The textX library is independent of any integrated development environment and can be easily integrated in any Python project. The textX tool works as a grammar interpreter. The parser is configured at run-time using the grammar. The textX tool is a free and open-source project available at GitHub.

Exact representation of the asymptotic drift speed and diffusion matrix for a class of velocity-jump processes

NASA Astrophysics Data System (ADS)

Mascia, Corrado

2016-01-01

This paper examines a class of linear hyperbolic systems which generalizes the Goldstein-Kac model to an arbitrary finite number of speeds vi with transition rates μij. Under the basic assumptions that the transition matrix is symmetric and irreducible, and the differences vi -vj generate all the space, the system exhibits a large-time behavior described by a parabolic advection-diffusion equation. The main contribution is to determine explicit formulas for the asymptotic drift speed and diffusion matrix in term of the kinetic parameters vi and μij, establishing a complete connection between microscopic and macroscopic coefficients. It is shown that the drift speed is the arithmetic mean of the velocities vi. The diffusion matrix has a more complicate representation, based on the graph with vertices the velocities vi and arcs weighted by the transition rates μij. The approach is based on an exhaustive analysis of the dispersion relation and on the application of a variant of the Kirchoff's matrix tree Theorem from graph theory.

Predicting and Detecting Emerging Cyberattack Patterns Using StreamWorks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chin, George; Choudhury, Sutanay; Feo, John T.

2014-06-30

The number and sophistication of cyberattacks on industries and governments have dramatically grown in recent years. To counter this movement, new advanced tools and techniques are needed to detect cyberattacks in their early stages such that defensive actions may be taken to avert or mitigate potential damage. From a cybersecurity analysis perspective, detecting cyberattacks may be cast as a problem of identifying patterns in computer network traffic. Logically and intuitively, these patterns may take on the form of a directed graph that conveys how an attack or intrusion propagates through the computers of a network. Such cyberattack graphs could providemore » cybersecurity analysts with powerful conceptual representations that are natural to express and analyze. We have been researching and developing graph-centric approaches and algorithms for dynamic cyberattack detection. The advanced dynamic graph algorithms we are developing will be packaged into a streaming network analysis framework known as StreamWorks. With StreamWorks, a scientist or analyst may detect and identify precursor events and patterns as they emerge in complex networks. This analysis framework is intended to be used in a dynamic environment where network data is streamed in and is appended to a large-scale dynamic graph. Specific graphical query patterns are decomposed and collected into a graph query library. The individual decomposed subpatterns in the library are continuously and efficiently matched against the dynamic graph as it evolves to identify and detect early, partial subgraph patterns. The scalable emerging subgraph pattern algorithms will match on both structural and semantic network properties.« less

Attribute-based Decision Graphs: A framework for multiclass data classification.

PubMed

Bertini, João Roberto; Nicoletti, Maria do Carmo; Zhao, Liang

2017-01-01

Graph-based algorithms have been successfully applied in machine learning and data mining tasks. A simple but, widely used, approach to build graphs from vector-based data is to consider each data instance as a vertex and connecting pairs of it using a similarity measure. Although this abstraction presents some advantages, such as arbitrary shape representation of the original data, it is still tied to some drawbacks, for example, it is dependent on the choice of a pre-defined distance metric and is biased by the local information among data instances. Aiming at exploring alternative ways to build graphs from data, this paper proposes an algorithm for constructing a new type of graph, called Attribute-based Decision Graph-AbDG. Given a vector-based data set, an AbDG is built by partitioning each data attribute range into disjoint intervals and representing each interval as a vertex. The edges are then established between vertices from different attributes according to a pre-defined pattern. Classification is performed through a matching process among the attribute values of the new instance and AbDG. Moreover, AbDG provides an inner mechanism to handle missing attribute values, which contributes for expanding its applicability. Results of classification tasks have shown that AbDG is a competitive approach when compared to well-known multiclass algorithms. The main contribution of the proposed framework is the combination of the advantages of attribute-based and graph-based techniques to perform robust pattern matching data classification, while permitting the analysis the input data considering only a subset of its attributes. Copyright © 2016 Elsevier Ltd. All rights reserved.

The representation of abstract words: why emotion matters.

PubMed

Kousta, Stavroula-Thaleia; Vigliocco, Gabriella; Vinson, David P; Andrews, Mark; Del Campo, Elena

2011-02-01

Although much is known about the representation and processing of concrete concepts, knowledge of what abstract semantics might be is severely limited. In this article we first address the adequacy of the 2 dominant accounts (dual coding theory and the context availability model) put forward in order to explain representation and processing differences between concrete and abstract words. We find that neither proposal can account for experimental findings and that this is, at least partly, because abstract words are considered to be unrelated to experiential information in both of these accounts. We then address a particular type of experiential information, emotional content, and demonstrate that it plays a crucial role in the processing and representation of abstract concepts: Statistically, abstract words are more emotionally valenced than are concrete words, and this accounts for a residual latency advantage for abstract words, when variables such as imageability (a construct derived from dual coding theory) and rated context availability are held constant. We conclude with a discussion of our novel hypothesis for embodied abstract semantics. (c) 2010 APA, all rights reserved.

Interferometric synthetic aperture radar phase unwrapping based on sparse Markov random fields by graph cuts

NASA Astrophysics Data System (ADS)

Zhou, Lifan; Chai, Dengfeng; Xia, Yu; Ma, Peifeng; Lin, Hui

2018-01-01

Phase unwrapping (PU) is one of the key processes in reconstructing the digital elevation model of a scene from its interferometric synthetic aperture radar (InSAR) data. It is known that two-dimensional (2-D) PU problems can be formulated as maximum a posteriori estimation of Markov random fields (MRFs). However, considering that the traditional MRF algorithm is usually defined on a rectangular grid, it fails easily if large parts of the wrapped data are dominated by noise caused by large low-coherence area or rapid-topography variation. A PU solution based on sparse MRF is presented to extend the traditional MRF algorithm to deal with sparse data, which allows the unwrapping of InSAR data dominated by high phase noise. To speed up the graph cuts algorithm for sparse MRF, we designed dual elementary graphs and merged them to obtain the Delaunay triangle graph, which is used to minimize the energy function efficiently. The experiments on simulated and real data, compared with other existing algorithms, both confirm the effectiveness of the proposed MRF approach, which suffers less from decorrelation effects caused by large low-coherence area or rapid-topography variation.

Model of twelve properties of a set of organic solvents with graph-theoretical and/or experimental parameters.

PubMed

Pogliani, Lionello

2010-01-30

Twelve properties of a highly heterogeneous class of organic solvents have been modeled with a graph-theoretical molecular connectivity modified (MC) method, which allows to encode the core electrons and the hydrogen atoms. The graph-theoretical method uses the concepts of simple, general, and complete graphs, where these last types of graphs are used to encode the core electrons. The hydrogen atoms have been encoded by the aid of a graph-theoretical perturbation parameter, which contributes to the definition of the valence delta, delta(v), a key parameter in molecular connectivity studies. The model of the twelve properties done with a stepwise search algorithm is always satisfactory, and it allows to check the influence of the hydrogen content of the solvent molecules on the choice of the type of descriptor. A similar argument holds for the influence of the halogen atoms on the type of core electron representation. In some cases the molar mass, and in a minor way, special "ad hoc" parameters have been used to improve the model. A very good model of the surface tension could be obtained by the aid of five experimental parameters. A mixed model method based on experimental parameters plus molecular connectivity indices achieved, instead, to consistently improve the model quality of five properties. To underline is the importance of the boiling point temperatures as descriptors in these last two model methodologies. Copyright 2009 Wiley Periodicals, Inc.

Clinical correlates of graph theory findings in temporal lobe epilepsy.

PubMed

Haneef, Zulfi; Chiang, Sharon

2014-11-01

Temporal lobe epilepsy (TLE) is considered a brain network disorder, additionally representing the most common form of pharmaco-resistant epilepsy in adults. There is increasing evidence that seizures in TLE arise from abnormal epileptogenic networks, which extend beyond the clinico-radiologically determined epileptogenic zone and may contribute to the failure rate of 30-50% following epilepsy surgery. Graph theory allows for a network-based representation of TLE brain networks using several neuroimaging and electrophysiologic modalities, and has potential to provide clinicians with clinically useful biomarkers for diagnostic and prognostic purposes. We performed a review of the current state of graph theory findings in TLE as they pertain to localization of the epileptogenic zone, prediction of pre- and post-surgical seizure frequency and cognitive performance, and monitoring cognitive decline in TLE. Although different neuroimaging and electrophysiologic modalities have yielded occasionally conflicting results, several potential biomarkers have been characterized for identifying the epileptogenic zone, pre-/post-surgical seizure prediction, and assessing cognitive performance. For localization, graph theory measures of centrality have shown the most potential, including betweenness centrality, outdegree, and graph index complexity, whereas for prediction of seizure frequency, measures of synchronizability have shown the most potential. The utility of clustering coefficient and characteristic path length for assessing cognitive performance in TLE is also discussed. Future studies integrating data from multiple modalities and testing predictive models are needed to clarify findings and develop graph theory for its clinical utility. Copyright © 2014 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Clinical correlates of graph theory findings in temporal lobe epilepsy

PubMed Central

Haneef, Zulfi; Chiang, Sharon

2014-01-01

Purpose Temporal lobe epilepsy (TLE) is considered a brain network disorder, additionally representing the most common form of pharmaco-resistant epilepsy in adults. There is increasing evidence that seizures in TLE arise from abnormal epileptogenic networks, which extend beyond the clinico-radiologically determined epileptogenic zone and may contribute to the failure rate of 30–50% following epilepsy surgery. Graph theory allows for a network-based representation of TLE brain networks using several neuroimaging and electrophysiologic modalities, and has potential to provide clinicians with clinically useful biomarkers for diagnostic and prognostic purposes. Methods We performed a review of the current state of graph theory findings in TLE as they pertain to localization of the epileptogenic zone, prediction of pre- and post-surgical seizure frequency and cognitive performance, and monitoring cognitive decline in TLE. Results Although different neuroimaging and electrophysiologic modalities have yielded occasionally conflicting results, several potential biomarkers have been characterized for identifying the epileptogenic zone, pre-/post-surgical seizure prediction, and assessing cognitive performance. For localization, graph theory measures of centrality have shown the most potential, including betweenness centrality, outdegree, and graph index complexity, whereas for prediction of seizure frequency, measures of synchronizability have shown the most potential. The utility of clustering coefficient and characteristic path length for assessing cognitive performance in TLE is also discussed. Conclusions Future studies integrating data from multiple modalities and testing predictive models are needed to clarify findings and develop graph theory for its clinical utility. PMID:25127370

Cycle/Cocycle Oblique Projections on Oriented Graphs

NASA Astrophysics Data System (ADS)

Polettini, Matteo

2015-01-01

It is well known that the edge vector space of an oriented graph can be decomposed in terms of cycles and cocycles (alias cuts, or bonds), and that a basis for the cycle and the cocycle spaces can be generated by adding and removing edges to an arbitrarily chosen spanning tree. In this paper, we show that the edge vector space can also be decomposed in terms of cycles and the generating edges of cocycles (called cochords), or of cocycles and the generating edges of cycles (called chords). From this observation follows a construction in terms of oblique complementary projection operators. We employ this algebraic construction to prove several properties of unweighted Kirchhoff-Symanzik matrices, encoding the mutual superposition between cycles and cocycles. In particular, we prove that dual matrices of planar graphs have the same spectrum (up to multiplicities). We briefly comment on how this construction provides a refined formalization of Kirchhoff's mesh analysis of electrical circuits, which has lately been applied to generic thermodynamic networks.

The U(1)-Kepler Problems

NASA Astrophysics Data System (ADS)

Meng, Guowu

2010-12-01

Let n ⩾ 2 be a positive integer. To each irreducible representation σ of U(1), a U(1)-Kepler problem in dimension (2n - 1) is constructed and analyzed. This system is superintegrable and when n = 2 it is equivalent to a MICZ-Kepler problem. The dynamical symmetry group of this system is widetildeU(n, n), and the Hilbert space of bound states {{H}}(σ ) is the unitary highest weight representation of widetildeU(n, n) with the minimal positive Gelfand-Kirillov dimension. Furthermore, it is shown that the correspondence between σ ^* (the dual of σ) and {H}(σ ) is the theta-correspondence for dual pair (U(1), U(n,n))subseteq Sp_{4n}({R}).

Contextualisation in the revised dual representation theory of PTSD: a response to Pearson and colleagues.

PubMed

Brewin, Chris R; Burgess, Neil

2014-03-01

Three recent studies (Pearson, 2012; Pearson, Ross, & Webster, 2012) purported to test the revised dual representation theory of posttraumatic stress disorder (Brewin, Gregory, Lipton, & Burgess, 2010) by manipulating the amount of additional information accompanying traumatic stimulus materials and assessing the effect on subsequent intrusive memories. Here we point out that these studies involve a misunderstanding of the meaning of "contextual" within the theory, such that the manipulation would be unlikely to have had the intended effect and the results are ambiguous with respect to the theory. Past and future experimental tests of the theory are discussed. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Didactic Conditions of Improvement of Pedagogical Personnel Training at Higher Education Institutions to Dual Education in the System of VET

ERIC Educational Resources Information Center

Zholdasbekova, Saule; Nurzhanbayeva, Zhanet; Mavedov, Rixsibai; Saipov, Amangeldi; Zhiyentayeva, Begaim; Tlemissova, Alja

2016-01-01

The purpose of the article consists in representation to discussion by specialists of the revealed didactic conditions of enhancement of preparation of the pedagogical personnel in higher education institution to dual training in the system of vocational and educational training. Modeling, aspect system, comparative and structural analyses…

Dual Coding Theory, Word Abstractness, and Emotion: A Critical Review of Kousta et al. (2011)

ERIC Educational Resources Information Center

Paivio, Allan

2013-01-01

Kousta, Vigliocco, Del Campo, Vinson, and Andrews (2011) questioned the adequacy of dual coding theory and the context availability model as explanations of representational and processing differences between concrete and abstract words. They proposed an alternative approach that focuses on the role of emotional content in the processing of…

Using the Dual-Target Cost to Explore the Nature of Search Target Representations

ERIC Educational Resources Information Center

Stroud, Michael J.; Menneer, Tamaryn; Cave, Kyle R.; Donnelly, Nick

2012-01-01

Eye movements were monitored to examine search efficiency and infer how color is mentally represented to guide search for multiple targets. Observers located a single color target very efficiently by fixating colors similar to the target. However, simultaneous search for 2 colors produced a dual-target cost. In addition, as the similarity between…

Cartographic generalization of urban street networks based on gravitational field theory

NASA Astrophysics Data System (ADS)

Liu, Gang; Li, Yongshu; Li, Zheng; Guo, Jiawei

2014-05-01

The automatic generalization of urban street networks is a constant and important aspect of geographical information science. Previous studies show that the dual graph for street-street relationships more accurately reflects the overall morphological properties and importance of streets than do other methods. In this study, we construct a dual graph to represent street-street relationship and propose an approach to generalize street networks based on gravitational field theory. We retain the global structural properties and topological connectivity of an original street network and borrow from gravitational field theory to define the gravitational force between nodes. The concept of multi-order neighbors is introduced and the gravitational force is taken as the measure of the importance contribution between nodes. The importance of a node is defined as the result of the interaction between a given node and its multi-order neighbors. Degree distribution is used to evaluate the level of maintaining the global structure and topological characteristics of a street network and to illustrate the efficiency of the suggested method. Experimental results indicate that the proposed approach can be used in generalizing street networks and retaining their density characteristics, connectivity and global structure.

Static versus Dynamic Disposition: The Role of GeoGebra in Representing Polynomial-Rational Inequalities and Exponential-Logarithmic Functions

ERIC Educational Resources Information Center

Caglayan, Günhan

2014-01-01

This study investigates prospective secondary mathematics teachers' visual representations of polynomial and rational inequalities, and graphs of exponential and logarithmic functions with GeoGebra Dynamic Software. Five prospective teachers in a university in the United States participated in this research study, which was situated within a…

Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction

ERIC Educational Resources Information Center

Trivedi, Shubhendu; Pardos, Zachary A.; Sarkozy, Gabor N.; Heffernan, Neil T.

2012-01-01

Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of…

Signifying the Accumulation Graph in a Dynamic and Multi-Representation Environment

ERIC Educational Resources Information Center

Yerushalmy, Michal; Swidan, Osama

2012-01-01

The present study focuses on the accumulation process involved in the integration of a single-variable function. Observing the work of two high-school calculus students who had not yet learned any other integral-related ideas, we analyze the emergence of the semiotic relationship between personal and mathematical meanings, as expressed through the…

Mathematical Modelling with Technology: The Role of Dynamic Representations

ERIC Educational Resources Information Center

Arzarello, Ferdinando; Ferrara, Francesca; Robutti, Ornella

2012-01-01

In this research we present the use of some technologies in problem solving activities (at different secondary school grades), aimed at finding a model for a geometric configuration, and representing this model in various ways: through a construction, through a Cartesian graph, etc. The task is part of a teaching experiment, in which students used…

On the Existence of Simultaneous Edge Disjoint Realizations of Degree Sequences with ’Few’ Edges

DTIC Science & Technology

1975-08-01

constructing graphs and digraphs with given valences and factors. Discrete Math . 6 (1973) 79-88. 3. M. Keren, Realization of a sun of sequences by a sum...appear. 5. S. Kundu, The k factor conjecture is true. Discrete Math . 6 (1973) 367-376. 6. S. Kundu, Disjoint representation of tree realizable

The Total Interval of a Graph.

DTIC Science & Technology

1988-01-01

about them in a mathematical con- text. A thorough treatment of multiple interval representations, including applications, is given by Roberts [21...8217-. -- + .".-)’""- +_ .. ,_ _ CA6 46 operation applied to a member of .4 U 3 T U.) U.3 UU- T T i Figure 11.2.18 I Fieure 11.2.19 ,* This completes the proof

Pre-Service Science Teachers' Construction and Interpretation of Graphs

ERIC Educational Resources Information Center

Ergül, N. Remziye

2018-01-01

Data and graphic analysis and interpretation are important parts of science process skills and science curriculum. So it refers to visual display of data using relevant graphical representations. One of the tools used in science courses is graphics for explain the relationship among each of the concepts and therefore it is important to know data…

Introducing Algebra through the Graphical Representation of Functions: A Study among LD Students

ERIC Educational Resources Information Center

Sauriol, Jennifer

2013-01-01

This longitudinal study evaluates the impact of a new Algebra 1 course at a High School for language-based learning-disabled (LD) students. The new course prioritized the teaching of relationship graphs and functions as an introduction to algebra. Across three studies, the dissertation documents and evaluates the progress made by LD high school…

Multiple External Representations: Bridges or Barriers to Climate Literacy?

NASA Astrophysics Data System (ADS)

Holzer, M. A.

2012-12-01

The continuous barrage of science related headlines and other media sources warn us of the need to heed the imperative for a science literate society. Climate change, genetics, evolution are a few of the charged and complex scientific topics requiring public understanding of the science to fully grasp the enormous reach of these topics in our daily lives. For instance, our global climate is changing as evidenced by the analysis of Earth observing satellite data, in-situ data, and proxy data records. How we as a global society decide to address the needs associated with a changing climate are contingent upon having a population that understands how the climate system functions, and can therefore make informed decisions on how to mitigate the effects of climate change. Communication in science relies heavily on the use of multiple representations to support the claims presented. However, these multiple representations require spatial and temporal skills to interpret information portrayed in them, and how a person engages with complex text and the multiple representations varies with the level of expertise one has with the content area. For example, a climatologist will likely identify anomalous data more quickly than a novice when presented with a graph of temperature change over time. These representations are used throughout textbooks as well as popular reading materials such as newspapers and magazines without much consideration for how a reader engages with complex text, diagrams, images, and graphs. If the ability to read and interact with scientific text found in popular literature is perceived as a worthy goal of scientific literacy, then it is imperative that readers understand the relationship between multiple representations and the text while interacting with the science literature they are reading. For example, in climate related articles multiple representations not only support the content, but they are part of the content not to be overlooked by a reader. Climatologists recognize the wealth of data and content found in these representations and therefore find themselves in a position where they can effectively interact with the author and their claims. This expert ability to seamlessly integrate text with the associated representations is at one end of the continuum of scientific text comprehension, but what abilities define a novice and those in between expert and novice in this continuum of scientific text comprehension? This talk will describe an ongoing research project with the overarching goal to establish the balance of this continuum in order to identify scaffolds that will assist non expert readers negotiate meaning from complex scientific text inclusive of multiple representations found in popular literature in climatology. It will inform those creating data representations on how best to create the representations so that claims and causal relationships may be derived from the literature or media source.

Pedagogical Affordances of Multiple External Representations in Scientific Processes

ERIC Educational Resources Information Center

Wu, Hsin-Kai; Puntambekar, Sadhana

2012-01-01

Multiple external representations (MERs) have been widely used in science teaching and learning. Theories such as dual coding theory and cognitive flexibility theory have been developed to explain why the use of MERs is beneficial to learning, but they do not provide much information on pedagogical issues such as how and in what conditions MERs…

Phase Shadows: An Enhanced Representation of Nonlinear Dynamic Systems

NASA Astrophysics Data System (ADS)

Luque, Amalia; Barbancho, Julio; Cañete, Javier Fernández; Córdoba, Antonio

2017-12-01

Many nonlinear dynamic systems have a rotating behavior where an angle defining its state may extend to more than 360∘. In these cases the use of the phase portrait does not properly depict the system’s evolution. Normalized phase portraits or cylindrical phase portraits have been extensively used to overcome the original phase portrait’s disadvantages. In this research a new graphic representation is introduced: the phase shadow. Its use clearly reveals the system behavior while overcoming the drawback of the existing plots. Through the paper the method to obtain the graphic is stated. Additionally, to show the phase shadow’s expressiveness, a rotating pendulum is considered. The work exposes that the new graph is an enhanced representational tool for systems having equilibrium points, limit cycles, chaotic attractors and/or bifurcations.

Multilabel user classification using the community structure of online networks

PubMed Central

Papadopoulos, Symeon; Kompatsiaris, Yiannis

2017-01-01

We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user’s graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score. PMID:28278242

Multilabel user classification using the community structure of online networks.

PubMed

Rizos, Georgios; Papadopoulos, Symeon; Kompatsiaris, Yiannis

2017-01-01

We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

Graph cuts for curvature based image denoising.

PubMed

Bae, Egil; Shi, Juan; Tai, Xue-Cheng

2011-05-01

Minimization of total variation (TV) is a well-known method for image denoising. Recently, the relationship between TV minimization problems and binary MRF models has been much explored. This has resulted in some very efficient combinatorial optimization algorithms for the TV minimization problem in the discrete setting via graph cuts. To overcome limitations, such as staircasing effects, of the relatively simple TV model, variational models based upon higher order derivatives have been proposed. The Euler's elastica model is one such higher order model of central importance, which minimizes the curvature of all level lines in the image. Traditional numerical methods for minimizing the energy in such higher order models are complicated and computationally complex. In this paper, we will present an efficient minimization algorithm based upon graph cuts for minimizing the energy in the Euler's elastica model, by simplifying the problem to that of solving a sequence of easy graph representable problems. This sequence has connections to the gradient flow of the energy function, and converges to a minimum point. The numerical experiments show that our new approach is more effective in maintaining smooth visual results while preserving sharp features better than TV models.

Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

NASA Astrophysics Data System (ADS)

Alshehhi, Rasha; Marpu, Prashanth Reddy

2017-04-01

Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

The partition dimension of subdivision of a graph

NASA Astrophysics Data System (ADS)

Amrullah, Baskoro, Edy Tri; Uttunggadewa, Saladin; Simanjuntak, Rinovia

2016-02-01

Let G = (V,E) be a connected graph, u,v ∈ V (G), e = uv ∈ E(G) and k be a positive integer. A k-subdivision of an edge e is a replacement of e = uv with a path u, x1, x2, x ..., xk, v. A graph G with a k-subdivided edge is denoted with S(G(e; k)). Let p be a positive integer and Π = {L1, L2, L3, …, Lp} be a p-partition of V (G). The representation of a vertex v with respect to Π, r(v|Π), is the vector (d(v, L1), d(v, L2), d(v, L3),…, d(v, Lp)) where d(v, Li) for i ∈ [1, p] is the minimum distance between v and the vertices of Li. The partition Π is called a resolving partition of G if r(w|Π) ≠ r(v|Π) for all w ≠ v ∈ V (G). The partition dimension, pd(G), of G is the smallest integer p such that G has a resolving p-partition. In this paper, we present sharp upper and lower bounds of the partition dimension of S(G(e; k)) for any graph G.

Graph analysis of functional brain networks: practical issues in translational neuroscience

PubMed Central

De Vico Fallani, Fabrizio; Richiardi, Jonas; Chavez, Mario; Achard, Sophie

2014-01-01

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes. PMID:25180301

Multiresolution analysis over graphs for a motor imagery based online BCI game.

PubMed

Asensio-Cubero, Javier; Gan, John Q; Palaniappan, Ramaswamy

2016-01-01

Multiresolution analysis (MRA) over graph representation of EEG data has proved to be a promising method for offline brain-computer interfacing (BCI) data analysis. For the first time we aim to prove the feasibility of the graph lifting transform in an online BCI system. Instead of developing a pointer device or a wheel-chair controller as test bed for human-machine interaction, we have designed and developed an engaging game which can be controlled by means of imaginary limb movements. Some modifications to the existing MRA analysis over graphs for BCI have also been proposed, such as the use of common spatial patterns for feature extraction at the different levels of decomposition, and sequential floating forward search as a best basis selection technique. In the online game experiment we obtained for three classes an average classification rate of 63.0% for fourteen naive subjects. The application of a best basis selection method helps significantly decrease the computing resources needed. The present study allows us to further understand and assess the benefits of the use of tailored wavelet analysis for processing motor imagery data and contributes to the further development of BCI for gaming purposes. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks

PubMed Central

Hruz, Tomas; Lucas, Christoph; Laule, Oliver; Zimmermann, Philip

2013-01-01

Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs. PMID:23864855

Local metric dimension of circulant graph c i r c (n :1 ,2 ,…,n/+1 2 )

NASA Astrophysics Data System (ADS)

Rimadhany, Ruzika; Darmaji

2017-08-01

Let G be a connected graph with two vertices u and v. The distance between u and v, denoted by d(u, v), is defined as length of the shortest path from u to v in G. For an ordered set W = {w1, w2, w3, … , wk} of k distinct vertices in a nontrivial connected graph G, the representation of a vertex v of V(G) respect to W is r(v|W) = (d(v, w1), d(v, w2), … , d(v, wk)). The set W is a resolving set of G if r(v|W) for each vertex v ∈ V(G) is distinct. A resolving set of minimum cardinality is a metric dimension and denoted by dim(G). The set W is a local resolving set of G if r(v|W) for every two adjacent vertices of V(G) is distinct. The minimum cardinality of local resolving set of G is a local metric dimension and denoted by ldim(G). In this research, we determine local metric dimension of circulant graph c i r c (n :1 ,2 ,3 ,…,n/+1 2 ) .

An exploratory study of cognitive load in diagnosing patient conditions.

PubMed

Workman, Michael; Lesser, Michael F; Kim, Joonmin

2007-06-01

To determine whether the ways in which information is presented to physicians will improve their ability to respond in a timely and accurate manner to acute care needs. The forms of the presentation compared traditional textual, chart and graph representations with equivalent symbolic language representations. To test this objective, our investigation involved two studies of interpreting patient conditions using two forms of information representation. The first assessed the level of cognitive effort (the outcome variable is known as cognitive load), and the second assessed the time and accuracy outcome variables. Our investigation consisted of two studies, the first study involved 3rd and 4th year medical students, and the second study involved three board certified physicians who worked in an intensive care unit of a metropolitan hospital. The first study utilized an all-within-subject design with repeated measures, where pretests were utilized as control covariate for prior learning and individual differences. The second study utilized a random sampling of records analyzed by two physicians and qualitatively evaluated by board-certified intensivists. The first study indicated that the cognitive load to interpret the symbolic representation was less than those presented in the more traditional textual, chart and graphic form. The second study suggests that experienced physicians may react in a more timely fashion with at least the same accuracy when the symbolic language was used than with traditional charts and graphs. The ways in which information is presented to physicians may affect the quality of acute care, such as in intensive, critical and emergency care units. When information can be presented in symbolic form, it may be cognitively processed more efficiently than when it is presented in the usual textual and chart form, potentially lowering errors in diagnosis and increasing the responsiveness to patient conditions.

Looking for robust properties in the growth of an academic network: the case of the Uruguayan biological research community.

PubMed

Cabana, Alvaro; Mizraji, Eduardo; Pomi, Andrés; Valle-Lisboa, Juan Carlos

2008-04-01

Graph-theoretical methods have recently been used to analyze certain properties of natural and social networks. In this work, we have investigated the early stages in the growth of a Uruguayan academic network, the Biology Area of the Programme for the Development of Basic Science (PEDECIBA). This transparent social network is a territory for the exploration of the reliability of clustering methods that can potentially be used when we are confronted with opaque natural systems that provide us with a limited spectrum of observables (happens in research on the relations between brain, thought and language). From our social net, we constructed two different graph representations based on the relationships among researchers revealed by their co-participation in Master's thesis committees. We studied these networks at different times and found that they achieve connectedness early in their evolution and exhibit the small-world property (i.e. high clustering with short path lengths). The data seem compatible with power law distributions of connectivity, clustering coefficients and betweenness centrality. Evidence of preferential attachment of new nodes and of new links between old nodes was also found in both representations. These results suggest that there are topological properties observed throughout the growth of the network that do not depend on the representations we have chosen but reflect intrinsic properties of the academic collective under study. Researchers in PEDECIBA are classified according to their specialties. We analysed the community structure detected by a standard algorithm in both representations. We found that much of the pre-specified structure is recovered and part of the mismatches can be attributed to convergent interests between scientists from different sub-disciplines. This result shows the potentiality of some clustering methods for the analysis of partially known natural systems.

Adaptation of Chain Event Graphs for use with Case-Control Studies in Epidemiology.

PubMed

Keeble, Claire; Thwaites, Peter Adam; Barber, Stuart; Law, Graham Richard; Baxter, Paul David

2017-09-26

Case-control studies are used in epidemiology to try to uncover the causes of diseases, but are a retrospective study design known to suffer from non-participation and recall bias, which may explain their decreased popularity in recent years. Traditional analyses report usually only the odds ratio for given exposures and the binary disease status. Chain event graphs are a graphical representation of a statistical model derived from event trees which have been developed in artificial intelligence and statistics, and only recently introduced to the epidemiology literature. They are a modern Bayesian technique which enable prior knowledge to be incorporated into the data analysis using the agglomerative hierarchical clustering algorithm, used to form a suitable chain event graph. Additionally, they can account for missing data and be used to explore missingness mechanisms. Here we adapt the chain event graph framework to suit scenarios often encountered in case-control studies, to strengthen this study design which is time and financially efficient. We demonstrate eight adaptations to the graphs, which consist of two suitable for full case-control study analysis, four which can be used in interim analyses to explore biases, and two which aim to improve the ease and accuracy of analyses. The adaptations are illustrated with complete, reproducible, fully-interpreted examples, including the event tree and chain event graph. Chain event graphs are used here for the first time to summarise non-participation, data collection techniques, data reliability, and disease severity in case-control studies. We demonstrate how these features of a case-control study can be incorporated into the analysis to provide further insight, which can help to identify potential biases and lead to more accurate study results.

MO-FG-CAMPUS-TeP2-01: A Graph Form ADMM Algorithm for Constrained Quadratic Radiation Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, X; Belcher, AH; Wiersma, R

Purpose: In radiation therapy optimization the constraints can be either hard constraints which must be satisfied or soft constraints which are included but do not need to be satisfied exactly. Currently the voxel dose constraints are viewed as soft constraints and included as a part of the objective function and approximated as an unconstrained problem. However in some treatment planning cases the constraints should be specified as hard constraints and solved by constrained optimization. The goal of this work is to present a computation efficiency graph form alternating direction method of multipliers (ADMM) algorithm for constrained quadratic treatment planning optimizationmore » and compare it with several commonly used algorithms/toolbox. Method: ADMM can be viewed as an attempt to blend the benefits of dual decomposition and augmented Lagrangian methods for constrained optimization. Various proximal operators were first constructed as applicable to quadratic IMRT constrained optimization and the problem was formulated in a graph form of ADMM. A pre-iteration operation for the projection of a point to a graph was also proposed to further accelerate the computation. Result: The graph form ADMM algorithm was tested by the Common Optimization for Radiation Therapy (CORT) dataset including TG119, prostate, liver, and head & neck cases. Both unconstrained and constrained optimization problems were formulated for comparison purposes. All optimizations were solved by LBFGS, IPOPT, Matlab built-in toolbox, CVX (implementing SeDuMi) and Mosek solvers. For unconstrained optimization, it was found that LBFGS performs the best, and it was 3–5 times faster than graph form ADMM. However, for constrained optimization, graph form ADMM was 8 – 100 times faster than the other solvers. Conclusion: A graph form ADMM can be applied to constrained quadratic IMRT optimization. It is more computationally efficient than several other commercial and noncommercial optimizers and it also used significantly less computer memory.« less

A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications

PubMed Central

Cameron, Delroy; Bodenreider, Olivier; Yalamanchili, Hima; Danh, Tu; Vallabhaneni, Sreeram; Thirunarayan, Krishnaprasad; Sheth, Amit P.; Rindflesch, Thomas C.

2014-01-01

Objectives This paper presents a methodology for recovering and decomposing Swanson’s Raynaud Syndrome–Fish Oil Hypothesis semi-automatically. The methodology leverages the semantics of assertions extracted from biomedical literature (called semantic predications) along with structured background knowledge and graph-based algorithms to semi-automatically capture the informative associations originally discovered manually by Swanson. Demonstrating that Swanson’s manually intensive techniques can be undertaken semi-automatically, paves the way for fully automatic semantics-based hypothesis generation from scientific literature. Methods Semantic predications obtained from biomedical literature allow the construction of labeled directed graphs which contain various associations among concepts from the literature. By aggregating such associations into informative subgraphs, some of the relevant details originally articulated by Swanson has been uncovered. However, by leveraging background knowledge to bridge important knowledge gaps in the literature, a methodology for semi-automatically capturing the detailed associations originally explicated in natural language by Swanson has been developed. Results Our methodology not only recovered the 3 associations commonly recognized as Swanson’s Hypothesis, but also decomposed them into an additional 16 detailed associations, formulated as chains of semantic predications. Altogether, 14 out of the 19 associations that can be attributed to Swanson were retrieved using our approach. To the best of our knowledge, such an in-depth recovery and decomposition of Swanson’s Hypothesis has never been attempted. Conclusion In this work therefore, we presented a methodology for semi- automatically recovering and decomposing Swanson’s RS-DFO Hypothesis using semantic representations and graph algorithms. Our methodology provides new insights into potential prerequisites for semantics-driven Literature-Based Discovery (LBD). These suggest that three critical aspects of LBD include: 1) the need for more expressive representations beyond Swanson’s ABC model; 2) an ability to accurately extract semantic information from text; and 3) the semantic integration of scientific literature with structured background knowledge. PMID:23026233

RAG-3D: A search tool for RNA 3D substructures

DOE PAGES

Zahran, Mai; Sevim Bayrak, Cigdem; Elmetwaly, Shereef; ...

2015-08-24

In this study, to address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally describedmore » in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding.« less

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.

PubMed

Akama, Hiroyuki; Miyake, Maki; Jung, Jaeyoung; Murphy, Brian

2015-01-01

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

RAG-3D: a search tool for RNA 3D substructures

PubMed Central

Zahran, Mai; Sevim Bayrak, Cigdem; Elmetwaly, Shereef; Schlick, Tamar

2015-01-01

To address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally described in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding. PMID:26304547

An analysis of multi-type relational interactions in FMA using graph motifs with disjointness constraints.

PubMed

Zhang, Guo-Qiang; Luo, Lingyun; Ogbuji, Chime; Joslyn, Cliff; Mejino, Jose; Sahoo, Satya S

2012-01-01

The interaction of multiple types of relationships among anatomical classes in the Foundational Model of Anatomy (FMA) can provide inferred information valuable for quality assurance. This paper introduces a method called Motif Checking (MOCH) to study the effects of such multi-relation type interactions for detecting logical inconsistencies as well as other anomalies represented by the motifs. MOCH represents patterns of multi-type interaction as small labeled (with multiple types of edges) sub-graph motifs, whose nodes represent class variables, and labeled edges represent relational types. By representing FMA as an RDF graph and motifs as SPARQL queries, fragments of FMA are automatically obtained as auditing candidates. Leveraging the scalability and reconfigurability of Semantic Web Technology, we performed exhaustive analyses of a variety of labeled sub-graph motifs. The quality assurance feature of MOCH comes from the distinct use of a subset of the edges of the graph motifs as constraints for disjointness, whereby bringing in rule-based flavor to the approach as well. With possible disjointness implied by antonyms, we performed manual inspection of the resulting FMA fragments and tracked down sources of abnormal inferred conclusions (logical inconsistencies), which are amendable for programmatic revision of the FMA. Our results demonstrate that MOCH provides a unique source of valuable information for quality assurance. Since our approach is general, it is applicable to any ontological system with an OWL representation.

A graph signal filtering-based approach for detection of different edge types on airborne lidar data

NASA Astrophysics Data System (ADS)

Bayram, Eda; Vural, Elif; Alatan, Aydin

2017-10-01

Airborne Laser Scanning is a well-known remote sensing technology, which provides a dense and highly accurate, yet unorganized point cloud of earth surface. During the last decade, extracting information from the data generated by airborne LiDAR systems has been addressed by many studies in geo-spatial analysis and urban monitoring applications. However, the processing of LiDAR point clouds is challenging due to their irregular structure and 3D geometry. In this study, we propose a novel framework for the detection of the boundaries of an object or scene captured by LiDAR. Our approach is motivated by edge detection techniques in vision research and it is established on graph signal filtering which is an exciting and promising field of signal processing for irregular data types. Due to the convenient applicability of graph signal processing tools on unstructured point clouds, we achieve the detection of the edge points directly on 3D data by using a graph representation that is constructed exclusively to answer the requirements of the application. Moreover, considering the elevation data as the (graph) signal, we leverage aerial characteristic of the airborne LiDAR data. The proposed method can be employed both for discovering the jump edges on a segmentation problem and for exploring the crease edges on a LiDAR object on a reconstruction/modeling problem, by only adjusting the filter characteristics.

An Analysis of Multi-type Relational Interactions in FMA Using Graph Motifs with Disjointness Constraints

PubMed Central

Zhang, Guo-Qiang; Luo, Lingyun; Ogbuji, Chime; Joslyn, Cliff; Mejino, Jose; Sahoo, Satya S

2012-01-01

The interaction of multiple types of relationships among anatomical classes in the Foundational Model of Anatomy (FMA) can provide inferred information valuable for quality assurance. This paper introduces a method called Motif Checking (MOCH) to study the effects of such multi-relation type interactions for detecting logical inconsistencies as well as other anomalies represented by the motifs. MOCH represents patterns of multi-type interaction as small labeled (with multiple types of edges) sub-graph motifs, whose nodes represent class variables, and labeled edges represent relational types. By representing FMA as an RDF graph and motifs as SPARQL queries, fragments of FMA are automatically obtained as auditing candidates. Leveraging the scalability and reconfigurability of Semantic Web Technology, we performed exhaustive analyses of a variety of labeled sub-graph motifs. The quality assurance feature of MOCH comes from the distinct use of a subset of the edges of the graph motifs as constraints for disjointness, whereby bringing in rule-based flavor to the approach as well. With possible disjointness implied by antonyms, we performed manual inspection of the resulting FMA fragments and tracked down sources of abnormal inferred conclusions (logical inconsistencies), which are amendable for programmatic revision of the FMA. Our results demonstrate that MOCH provides a unique source of valuable information for quality assurance. Since our approach is general, it is applicable to any ontological system with an OWL representation. PMID:23304382

Expert system validation in prolog

NASA Technical Reports Server (NTRS)

Stock, Todd; Stachowitz, Rolf; Chang, Chin-Liang; Combs, Jacqueline

1988-01-01

An overview of the Expert System Validation Assistant (EVA) is being implemented in Prolog at the Lockheed AI Center. Prolog was chosen to facilitate rapid prototyping of the structure and logic checkers and since February 1987, we have implemented code to check for irrelevance, subsumption, duplication, deadends, unreachability, and cycles. The architecture chosen is extremely flexible and expansible, yet concise and complementary with the normal interactive style of Prolog. The foundation of the system is in the connection graph representation. Rules and facts are modeled as nodes in the graph and arcs indicate common patterns between rules. The basic activity of the validation system is then a traversal of the connection graph, searching for various patterns the system recognizes as erroneous. To aid in specifying these patterns, a metalanguage is developed, providing the user with the basic facilities required to reason about the expert system. Using the metalanguage, the user can, for example, give the Prolog inference engine the goal of finding inconsistent conclusions among the rules, and Prolog will search the graph intantiations which can match the definition of inconsistency. Examples of code for some of the checkers are provided and the algorithms explained. Technical highlights include automatic construction of a connection graph, demonstration of the use of metalanguage, the A* algorithm modified to detect all unique cycles, general-purpose stacks in Prolog, and a general-purpose database browser with pattern completion.

Searching social networks for subgraph patterns

NASA Astrophysics Data System (ADS)

Ogaard, Kirk; Kase, Sue; Roy, Heather; Nagi, Rakesh; Sambhoos, Kedar; Sudit, Moises

2013-06-01

Software tools for Social Network Analysis (SNA) are being developed which support various types of analysis of social networks extracted from social media websites (e.g., Twitter). Once extracted and stored in a database such social networks are amenable to analysis by SNA software. This data analysis often involves searching for occurrences of various subgraph patterns (i.e., graphical representations of entities and relationships). The authors have developed the Graph Matching Toolkit (GMT) which provides an intuitive Graphical User Interface (GUI) for a heuristic graph matching algorithm called the Truncated Search Tree (TruST) algorithm. GMT is a visual interface for graph matching algorithms processing large social networks. GMT enables an analyst to draw a subgraph pattern by using a mouse to select categories and labels for nodes and links from drop-down menus. GMT then executes the TruST algorithm to find the top five occurrences of the subgraph pattern within the social network stored in the database. GMT was tested using a simulated counter-insurgency dataset consisting of cellular phone communications within a populated area of operations in Iraq. The results indicated GMT (when executing the TruST graph matching algorithm) is a time-efficient approach to searching large social networks. GMT's visual interface to a graph matching algorithm enables intelligence analysts to quickly analyze and summarize the large amounts of data necessary to produce actionable intelligence.

RAG-3D: A search tool for RNA 3D substructures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zahran, Mai; Sevim Bayrak, Cigdem; Elmetwaly, Shereef

In this study, to address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally describedmore » in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding.« less

Beyond Markov: Accounting for independence violations in causal reasoning.

PubMed

Rehder, Bob

2018-06-01

Although many theories of causal cognition are based on causal graphical models, a key property of such models-the independence relations stipulated by the Markov condition-is routinely violated by human reasoners. This article presents three new accounts of those independence violations, accounts that share the assumption that people's understanding of the correlational structure of data generated from a causal graph differs from that stipulated by causal graphical model framework. To distinguish these models, experiments assessed how people reason with causal graphs that are larger than those tested in previous studies. A traditional common cause network (Y 1 ←X→Y 2 ) was extended so that the effects themselves had effects (Z 1 ←Y 1 ←X→Y 2 →Z 2 ). A traditional common effect network (Y 1 →X←Y 2 ) was extended so that the causes themselves had causes (Z 1 →Y 1 →X←Y 2 ←Z 2 ). Subjects' inferences were most consistent with the beta-Q model in which consistent states of the world-those in which variables are either mostly all present or mostly all absent-are viewed as more probable than stipulated by the causal graphical model framework. Substantial variability in subjects' inferences was also observed, with the result that substantial minorities of subjects were best fit by one of the other models (the dual prototype or a leaky gate models). The discrepancy between normative and human causal cognition stipulated by these models is foundational in the sense that they locate the error not in people's causal reasoning but rather in their causal representations. As a result, they are applicable to any cognitive theory grounded in causal graphical models, including theories of analogy, learning, explanation, categorization, decision-making, and counterfactual reasoning. Preliminary evidence that independence violations indeed generalize to other judgment types is presented. Copyright © 2018 Elsevier Inc. All rights reserved.

Reflection Positive Stochastic Processes Indexed by Lie Groups

NASA Astrophysics Data System (ADS)

Jorgensen, Palle E. T.; Neeb, Karl-Hermann; Ólafsson, Gestur

2016-06-01

Reflection positivity originates from one of the Osterwalder-Schrader axioms for constructive quantum field theory. It serves as a bridge between euclidean and relativistic quantum field theory. In mathematics, more specifically, in representation theory, it is related to the Cartan duality of symmetric Lie groups (Lie groups with an involution) and results in a transformation of a unitary representation of a symmetric Lie group to a unitary representation of its Cartan dual. In this article we continue our investigation of representation theoretic aspects of reflection positivity by discussing reflection positive Markov processes indexed by Lie groups, measures on path spaces, and invariant gaussian measures in spaces of distribution vectors. This provides new constructions of reflection positive unitary representations.

PathFinder: reconstruction and dynamic visualization of metabolic pathways.

PubMed

Goesmann, Alexander; Haubrock, Martin; Meyer, Folker; Kalinowski, Jörn; Giegerich, Robert

2002-01-01

Beyond methods for a gene-wise annotation and analysis of sequenced genomes new automated methods for functional analysis on a higher level are needed. The identification of realized metabolic pathways provides valuable information on gene expression and regulation. Detection of incomplete pathways helps to improve a constantly evolving genome annotation or discover alternative biochemical pathways. To utilize automated genome analysis on the level of metabolic pathways new methods for the dynamic representation and visualization of pathways are needed. PathFinder is a tool for the dynamic visualization of metabolic pathways based on annotation data. Pathways are represented as directed acyclic graphs, graph layout algorithms accomplish the dynamic drawing and visualization of the metabolic maps. A more detailed analysis of the input data on the level of biochemical pathways helps to identify genes and detect improper parts of annotations. As an Relational Database Management System (RDBMS) based internet application PathFinder reads a list of EC-numbers or a given annotation in EMBL- or Genbank-format and dynamically generates pathway graphs.

Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection.

PubMed

Zhu, Xiaofeng; Li, Xuelong; Zhang, Shichao; Ju, Chunhua; Wu, Xindong

2017-06-01

In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (JGSC) model. In JGSC, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution. Furthermore, we extend JGSC to a robust JGSC (RJGSC) via replacing the least square loss function with a robust loss function, for achieving the same goals and also avoiding the impact of outliers. Finally, experimental results on real data sets showed that both JGSC and RJGSC outperformed the state-of-the-art algorithms in terms of k -nearest neighbor classification performance.

Unapparent Information Revelation: Text Mining for Counterterrorism

NASA Astrophysics Data System (ADS)

Srihari, Rohini K.

Unapparent information revelation (UIR) is a special case of text mining that focuses on detecting possible links between concepts across multiple text documents by generating an evidence trail explaining the connection. A traditional search involving, for example, two or more person names will attempt to find documents mentioning both these individuals. This research focuses on a different interpretation of such a query: what is the best evidence trail across documents that explains a connection between these individuals? For example, all may be good golfers. A generalization of this task involves query terms representing general concepts (e.g. indictment, foreign policy). Previous approaches to this problem have focused on graph mining involving hyperlinked documents, and link analysis exploiting named entities. A new robust framework is presented, based on (i) generating concept chain graphs, a hybrid content representation, (ii) performing graph matching to select candidate subgraphs, and (iii) subsequently using graphical models to validate hypotheses using ranked evidence trails. We adapt the DUC data set for cross-document summarization to evaluate evidence trails generated by this approach

Superspace geometrical realization of the N-extended super Virasoro algebra and its dual

NASA Astrophysics Data System (ADS)

Curto, C.; Gates, S. J., Jr.; Rodgers, V. G. J.

2000-05-01

We derive properties of N-extended /GR super Virasoro algebras. These include adding central extensions, identification of all primary fields and the action of the adjoint representation on its dual. The final result suggest identification with the spectrum of fields in supergravity theories and superstring/M-theory constructed from NSR N-extended supersymmetric /GR Virasoro algebras.

Using pedagogical discipline representations (PDRs) to enable Astro 101 students to reason about modern astrophysics

NASA Astrophysics Data System (ADS)

Wallace, Colin Scott; Prather, Edward E.; Chambers, Timothy G.; Kamenetzky, Julia R.; Hornstein, Seth D.

2017-01-01

Instructors of introductory, college-level, general education astronomy (Astro 101) often want to include topics from the cutting-edge of modern astrophysics in their course. Unfortunately, the teaching of these cutting-edge topics is typically confined to advanced undergraduate or graduate classes, using representations (graphical, mathematical, etc.) that are inaccessible to the vast majority of Astro 101 students. Consequently, many Astro 101 instructors feel that they have no choice but to cover these modern topics at a superficial level. Pedagogical discipline representations (PDRs) are one solution to this problem. Pedagogical discipline representations are representations that are explicitly designed to enhance the teaching and learning of a topic, even though these representations may not typically be found in traditional textbooks or used by experts in the discipline who are engaged in topic-specific discourse. In some cases, PDRs are significantly simplified or altered versions of typical discipline representations (graphs, data tables, etc.); in others they may be novel and highly contextualized representations with unique features that purposefully engage novice learners’ pre-existing mental models and reasoning difficulties, facilitating critical discourse. In this talk, I will discuss important lessons that my colleagues and I have learned while developing PDRs and describe how PDRs can enable students to reason about complex modern astrophysical topics.

A note on the stability and discriminability of graph-based features for classification problems in digital pathology

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Xu, Jun; Madabhushi, Anant

2015-01-01

Nuclear architecture or the spatial arrangement of individual cancer nuclei on histopathology images has been shown to be associated with different grades and differential risk for a number of solid tumors such as breast, prostate, and oropharyngeal. Graph-based representations of individual nuclei (nuclei representing the graph nodes) allows for mining of quantitative metrics to describe tumor morphology. These graph features can be broadly categorized into global and local depending on the type of graph construction method. While a number of local graph (e.g. Cell Cluster Graphs) and global graph (e.g. Voronoi, Delaunay Triangulation, Minimum Spanning Tree) features have been shown to associated with cancer grade, risk, and outcome for different cancer types, the sensitivity of the preceding segmentation algorithms in identifying individual nuclei can have a significant bearing on the discriminability of the resultant features. This therefore begs the question as to which features while being discriminative of cancer grade and aggressiveness are also the most resilient to the segmentation errors. These properties are particularly desirable in the context of digital pathology images, where the method of slide preparation, staining, and type of nuclear segmentation algorithm employed can all dramatically affect the quality of the nuclear graphs and corresponding features. In this paper we evaluated the trade off between discriminability and stability of both global and local graph-based features in conjunction with a few different segmentation algorithms and in the context of two different histopathology image datasets of breast cancer from whole-slide images (WSI) and tissue microarrays (TMA). Specifically in this paper we investigate a few different performance measures including stability, discriminability and stability vs discriminability trade off, all of which are based on p-values from the Kruskal-Wallis one-way analysis of variance for local and global graph features. Apart from identifying the set of local and global features that satisfied the trade off between stability and discriminability, our most interesting finding was that a simple segmentation method was sufficient to identify the most discriminant features for invasive tumour detection in TMAs, whereas for tumour grading in WSI, the graph based features were more sensitive to the accuracy of the segmentation algorithm employed.

Derivatives in discrete mathematics: a novel graph-theoretical invariant for generating new 2/3D molecular descriptors. I. Theory and QSPR application

NASA Astrophysics Data System (ADS)

Marrero-Ponce, Yovani; Santiago, Oscar Martínez; López, Yoan Martínez; Barigye, Stephen J.; Torrens, Francisco

2012-11-01

In this report, we present a new mathematical approach for describing chemical structures of organic molecules at atomic-molecular level, proposing for the first time the use of the concept of the derivative ( partial ) of a molecular graph (MG) with respect to a given event ( E), to obtain a new family of molecular descriptors (MDs). With this purpose, a new matrix representation of the MG, which generalizes graph's theory's traditional incidence matrix, is introduced. This matrix, denominated the generalized incidence matrix, Q, arises from the Boolean representation of molecular sub- graphs that participate in the formation of the graph molecular skeleton MG and could be complete (representing all possible connected sub-graphs) or constitute sub-graphs of determined orders or types as well as a combination of these. The Q matrix is a non-quadratic and unsymmetrical in nature, its columns ( n) and rows ( m) are conditions (letters) and collection of conditions (words) with which the event occurs. This non-quadratic and unsymmetrical matrix is transformed, by algebraic manipulation, to a quadratic and symmetric matrix known as relations frequency matrix, F, which characterizes the participation intensity of the conditions (letters) in the events (words). With F, we calculate the derivative over a pair of atomic nuclei. The local index for the atomic nuclei i, Δ i , can therefore be obtained as a linear combination of all the pair derivatives of the atomic nuclei i with all the rest of the j's atomic nuclei. Here, we also define new strategies that generalize the present form of obtaining global or local (group or atom-type) invariants from atomic contributions (local vertex invariants, LOVIs). In respect to this, metric (norms), means and statistical invariants are introduced. These invariants are applied to a vector whose components are the values Δ i for the atomic nuclei of the molecule or its fragments. Moreover, with the purpose of differentiating among different atoms, an atomic weighting scheme (atom-type labels) is used in the formation of the matrix Q or in LOVIs state. The obtained indices were utilized to describe the partition coefficient (Log P) and the reactivity index (Log K) of the 34 derivatives of 2-furylethylenes. In all the cases, our MDs showed better statistical results than those previously obtained using some of the most used families of MDs in chemometric practice. Therefore, it has been demonstrated to that the proposed MDs are useful in molecular design and permit obtaining easier and robust mathematical models than the majority of those reported in the literature. All this range of mentioned possibilities open "the doors" to the creation of a new family of MDs, using the graph derivative, and avail a new tool for QSAR/QSPR and molecular diversity/similarity studies.

Derivatives in discrete mathematics: a novel graph-theoretical invariant for generating new 2/3D molecular descriptors. I. Theory and QSPR application.

PubMed

Marrero-Ponce, Yovani; Santiago, Oscar Martínez; López, Yoan Martínez; Barigye, Stephen J; Torrens, Francisco

2012-11-01

In this report, we present a new mathematical approach for describing chemical structures of organic molecules at atomic-molecular level, proposing for the first time the use of the concept of the derivative ([Formula: see text]) of a molecular graph (MG) with respect to a given event (E), to obtain a new family of molecular descriptors (MDs). With this purpose, a new matrix representation of the MG, which generalizes graph's theory's traditional incidence matrix, is introduced. This matrix, denominated the generalized incidence matrix, Q, arises from the Boolean representation of molecular sub-graphs that participate in the formation of the graph molecular skeleton MG and could be complete (representing all possible connected sub-graphs) or constitute sub-graphs of determined orders or types as well as a combination of these. The Q matrix is a non-quadratic and unsymmetrical in nature, its columns (n) and rows (m) are conditions (letters) and collection of conditions (words) with which the event occurs. This non-quadratic and unsymmetrical matrix is transformed, by algebraic manipulation, to a quadratic and symmetric matrix known as relations frequency matrix, F, which characterizes the participation intensity of the conditions (letters) in the events (words). With F, we calculate the derivative over a pair of atomic nuclei. The local index for the atomic nuclei i, Δ(i), can therefore be obtained as a linear combination of all the pair derivatives of the atomic nuclei i with all the rest of the j's atomic nuclei. Here, we also define new strategies that generalize the present form of obtaining global or local (group or atom-type) invariants from atomic contributions (local vertex invariants, LOVIs). In respect to this, metric (norms), means and statistical invariants are introduced. These invariants are applied to a vector whose components are the values Δ(i) for the atomic nuclei of the molecule or its fragments. Moreover, with the purpose of differentiating among different atoms, an atomic weighting scheme (atom-type labels) is used in the formation of the matrix Q or in LOVIs state. The obtained indices were utilized to describe the partition coefficient (Log P) and the reactivity index (Log K) of the 34 derivatives of 2-furylethylenes. In all the cases, our MDs showed better statistical results than those previously obtained using some of the most used families of MDs in chemometric practice. Therefore, it has been demonstrated to that the proposed MDs are useful in molecular design and permit obtaining easier and robust mathematical models than the majority of those reported in the literature. All this range of mentioned possibilities open "the doors" to the creation of a new family of MDs, using the graph derivative, and avail a new tool for QSAR/QSPR and molecular diversity/similarity studies.

Deformation quantization with separation of variables of an endomorphism bundle

NASA Astrophysics Data System (ADS)

Karabegov, Alexander

2014-01-01

Given a holomorphic Hermitian vector bundle E and a star-product with separation of variables on a pseudo-Kähler manifold, we construct a star product on the sections of the endomorphism bundle of the dual bundle E∗ which also has the appropriately generalized property of separation of variables. For this star product we prove a generalization of Gammelgaard's graph-theoretic formula.

Digital Workflows for a 3d Semantic Representation of AN Ancient Mining Landscape

NASA Astrophysics Data System (ADS)

Hiebel, G.; Hanke, K.

2017-08-01

The ancient mining landscape of Schwaz/Brixlegg in the Tyrol, Austria witnessed mining from prehistoric times to modern times creating a first order cultural landscape when it comes to one of the most important inventions in human history: the production of metal. In 1991 a part of this landscape was lost due to an enormous landslide that reshaped part of the mountain. With our work we want to propose a digital workflow to create a 3D semantic representation of this ancient mining landscape with its mining structures to preserve it for posterity. First, we define a conceptual model to integrate the data. It is based on the CIDOC CRM ontology and CRMgeo for geometric data. To transform our information sources to a formal representation of the classes and properties of the ontology we applied semantic web technologies and created a knowledge graph in RDF (Resource Description Framework). Through the CRMgeo extension coordinate information of mining features can be integrated into the RDF graph and thus related to the detailed digital elevation model that may be visualized together with the mining structures using Geoinformation systems or 3D visualization tools. The RDF network of the triple store can be queried using the SPARQL query language. We created a snapshot of mining, settlement and burial sites in the Bronze Age. The results of the query were loaded into a Geoinformation system and a visualization of known bronze age sites related to mining, settlement and burial activities was created.

The Representation of Abstract Words: What Matters? Reply to Paivio's (2013) Comment on Kousta et al. (2011)

ERIC Educational Resources Information Center

Vigliocco, Gabriella; Kousta, Stavroula; Vinson, David; Andrews, Mark; Del Campo, Elena

2013-01-01

In Kousta, Vigliocco, Vinson, Andrews, and Del Campo (2011), we presented an embodied theory of semantic representation, which crucially included abstract concepts as internally embodied via affective states. Paivio (2013) took issue with our treatment of dual coding theory, our reliance on data from lexical decision, and our theoretical proposal.…

Semantics based approach for analyzing disease-target associations.

PubMed

Kaalia, Rama; Ghosh, Indira

2016-08-01

A complex disease is caused by heterogeneous biological interactions between genes and their products along with the influence of environmental factors. There have been many attempts for understanding the cause of these diseases using experimental, statistical and computational methods. In the present work the objective is to address the challenge of representation and integration of information from heterogeneous biomedical aspects of a complex disease using semantics based approach. Semantic web technology is used to design Disease Association Ontology (DAO-db) for representation and integration of disease associated information with diabetes as the case study. The functional associations of disease genes are integrated using RDF graphs of DAO-db. Three semantic web based scoring algorithms (PageRank, HITS (Hyperlink Induced Topic Search) and HITS with semantic weights) are used to score the gene nodes on the basis of their functional interactions in the graph. Disease Association Ontology for Diabetes (DAO-db) provides a standard ontology-driven platform for describing genes, proteins, pathways involved in diabetes and for integrating functional associations from various interaction levels (gene-disease, gene-pathway, gene-function, gene-cellular component and protein-protein interactions). An automatic instance loader module is also developed in present work that helps in adding instances to DAO-db on a large scale. Our ontology provides a framework for querying and analyzing the disease associated information in the form of RDF graphs. The above developed methodology is used to predict novel potential targets involved in diabetes disease from the long list of loose (statistically associated) gene-disease associations. Copyright © 2016 Elsevier Inc. All rights reserved.

The Systems Biology Markup Language (SBML) Level 3 Package: Layout, Version 1 Core.

PubMed

Gauges, Ralph; Rost, Ursula; Sahle, Sven; Wengler, Katja; Bergmann, Frank T

2015-06-01

Many software tools provide facilities for depicting reaction network diagrams in a visual form. Two aspects of such a visual diagram can be distinguished: the layout (i.e.: the positioning and connections) of the elements in the diagram, and the graphical form of the elements (for example, the glyphs used for symbols, the properties of the lines connecting them, and so on). For software tools that also read and write models in SBML (Systems Biology Markup Language) format, a common need is to store the network diagram together with the SBML representation of the model. This in turn raises the question of how to encode the layout and the rendering of these diagrams. The SBML Level 3 Version 1 Core specification does not provide a mechanism for explicitly encoding diagrams, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs. The Layout package for SBML Level 3 adds the necessary features to SBML so that diagram layouts can be encoded in SBML files, and a companion package called SBML Rendering specifies how the graphical rendering of elements can be encoded. The SBML Layout package is based on the principle that reaction network diagrams should be described as representations of entities such as species and reactions (with direct links to the underlying SBML elements), and not as arbitrary drawings or graphs; for this reason, existing languages for the description of vector drawings (such as SVG) or general graphs (such as GraphML) cannot be used.

The Systems Biology Markup Language (SBML) Level 3 Package: Layout, Version 1 Core.

PubMed

Gauges, Ralph; Rost, Ursula; Sahle, Sven; Wengler, Katja; Bergmann, Frank Thomas

2015-09-04

Many software tools provide facilities for depicting reaction network diagrams in a visual form. Two aspects of such a visual diagram can be distinguished: the layout (i.e.: the positioning and connections) of the elements in the diagram, and the graphical form of the elements (for example, the glyphs used for symbols, the properties of the lines connecting them, and so on). For software tools that also read and write models in SBML (Systems Biology Markup Language) format, a common need is to store the network diagram together with the SBML representation of the model. This in turn raises the question of how to encode the layout and the rendering of these diagrams. The SBML Level 3 Version 1 Core specification does not provide a mechanism for explicitly encoding diagrams, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs. The Layout package for SBML Level 3 adds the necessary features to SBML so that diagram layouts can be encoded in SBML files, and a companion package called SBML Rendering specifies how the graphical rendering of elements can be encoded. The SBML Layout package is based on the principle that reaction network diagrams should be described as representations of entities such as species and reactions (with direct links to the underlying SBML elements), and not as arbitrary drawings or graphs; for this reason, existing languages for the description of vector drawings (such as SVG) or general graphs (such as GraphML) cannot be used.

Efficient Synthesis of Graph Methods: a Dynamically Scheduled Architecture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Minutoli, Marco; Castellana, Vito G.; Tumeo, Antonino

RDF databases naturally map to a graph representation and employ languages, such as SPARQL, that implements queries as graph pattern matching routines. Graph methods exhibit an irregular behavior: they present unpredictable, fine-grained data accesses, and are synchronization inten- sive. Graph data structures expose large amounts of dy- namic parallelism, but are difficult to partition without gen- erating load unbalance. In this paper, we present a novel ar- chitecture to improve the synthesis of graph methods. Our design addresses the issues of these algorithms with two com- ponents: a Dynamic Task Scheduler (DTS), which reduces load unbalance and maximize resource utilization,more » and a Hi- erarchical Memory Interface controller (HMI), which pro- vides support for concurrent memory operations on multi- ported/multi-banked shared memories. We evaluate our ap- proach by generating the accelerators for a set of SPARQL queries from the Lehigh University Benchmark (LUBM). We first analyze the load unbalance of these queries, showing that execution time among tasks can differ even of order of magnitudes. We then synthesize the queries and com- pare the performance of the resulting accelerators against the current state of the art. Experimental results show that our solution provides a speedup over the serial implementa- tion close to the theoretical maximum and a speedup up to 3.45 over a baseline parallel implementation. We conclude our study by exploring the design space to achieve maximum memory channels utilization. The best design used at least three of the four memory channels for more than 90% of the execution time.« less

Dynamical modeling and analysis of large cellular regulatory networks

NASA Astrophysics Data System (ADS)

Bérenguier, D.; Chaouiya, C.; Monteiro, P. T.; Naldi, A.; Remy, E.; Thieffry, D.; Tichit, L.

2013-06-01

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Small-world bias of correlation networks: From brain to climate

NASA Astrophysics Data System (ADS)

Hlinka, Jaroslav; Hartman, David; Jajcay, Nikola; Tomeček, David; Tintěra, Jaroslav; Paluš, Milan

2017-03-01

Complex systems are commonly characterized by the properties of their graph representation. Dynamical complex systems are then typically represented by a graph of temporal dependencies between time series of state variables of their subunits. It has been shown recently that graphs constructed in this way tend to have relatively clustered structure, potentially leading to spurious detection of small-world properties even in the case of systems with no or randomly distributed true interactions. However, the strength of this bias depends heavily on a range of parameters and its relevance for real-world data has not yet been established. In this work, we assess the relevance of the bias using two examples of multivariate time series recorded in natural complex systems. The first is the time series of local brain activity as measured by functional magnetic resonance imaging in resting healthy human subjects, and the second is the time series of average monthly surface air temperature coming from a large reanalysis of climatological data over the period 1948-2012. In both cases, the clustering in the thresholded correlation graph is substantially higher compared with a realization of a density-matched random graph, while the shortest paths are relatively short, showing thus distinguishing features of small-world structure. However, comparable or even stronger small-world properties were reproduced in correlation graphs of model processes with randomly scrambled interconnections. This suggests that the small-world properties of the correlation matrices of these real-world systems indeed do not reflect genuinely the properties of the underlying interaction structure, but rather result from the inherent properties of correlation matrix.

Small-world bias of correlation networks: From brain to climate.

PubMed

Hlinka, Jaroslav; Hartman, David; Jajcay, Nikola; Tomeček, David; Tintěra, Jaroslav; Paluš, Milan

2017-03-01

Complex systems are commonly characterized by the properties of their graph representation. Dynamical complex systems are then typically represented by a graph of temporal dependencies between time series of state variables of their subunits. It has been shown recently that graphs constructed in this way tend to have relatively clustered structure, potentially leading to spurious detection of small-world properties even in the case of systems with no or randomly distributed true interactions. However, the strength of this bias depends heavily on a range of parameters and its relevance for real-world data has not yet been established. In this work, we assess the relevance of the bias using two examples of multivariate time series recorded in natural complex systems. The first is the time series of local brain activity as measured by functional magnetic resonance imaging in resting healthy human subjects, and the second is the time series of average monthly surface air temperature coming from a large reanalysis of climatological data over the period 1948-2012. In both cases, the clustering in the thresholded correlation graph is substantially higher compared with a realization of a density-matched random graph, while the shortest paths are relatively short, showing thus distinguishing features of small-world structure. However, comparable or even stronger small-world properties were reproduced in correlation graphs of model processes with randomly scrambled interconnections. This suggests that the small-world properties of the correlation matrices of these real-world systems indeed do not reflect genuinely the properties of the underlying interaction structure, but rather result from the inherent properties of correlation matrix.

Reading Guided by Automated Graphical Representations: How Model-Based Text Visualizations Facilitate Learning in Reading Comprehension Tasks

ERIC Educational Resources Information Center

Pirnay-Dummer, Pablo; Ifenthaler, Dirk

2011-01-01

Our study integrates automated natural language-oriented assessment and analysis methodologies into feasible reading comprehension tasks. With the newly developed T-MITOCAR toolset, prose text can be automatically converted into an association net which has similarities to a concept map. The "text to graph" feature of the software is based on…

A Multimedia Knowledge Representation for an "Intelligent" Computerized Tutor. Technical Report No. 142.

ERIC Educational Resources Information Center

Baggett, Patricia; Ehrenfeucht, Andrzej

The intended end product of the research project described is an "intelligent" multimedia tutoring system for procedural tasks, in particular, the repair of physical objects. This paper presents the data structure that will be used, i.e., a graph with five types of nodes (mental, abstract, motoric or action, visual, and verbal) and two types of…

Refutational Text and Multiple External Representations as a Method to Remediate the Misinterpretation of Box Plots

ERIC Educational Resources Information Center

Lem, Stephanie; Baert, Kathy; Ceulemans, Eva; Onghena, Patrick; Verschaffel, Lieven; Van Dooren, Wim

2017-01-01

The ability to interpret graphs is highly important in modern society, but has proven to be a challenge for many people. In this paper, two teaching methods were used to remediate one specific misinterpretation: the area misinterpretation of box plots. First, we used refutational text to explicitly state and invalidate the area misinterpretation…

Job Knowledge Test Design: A Cognitively-Oriented Approach. Institute Report No. 241.

ERIC Educational Resources Information Center

DuBois, David; And Others

Selected cognitive science methods were used to modify existing test development procedures so that the modified procedures could in turn be used to improve the usefulness of job knowledge tests as a proxy for hands-on performance. A plan-goal graph representation was used to capture the knowledge content and goal structure of the task of using a…

Using Dynamic Mathematics Software to Teach One-Variable Inequalities by the View of Semiotic Registers

ERIC Educational Resources Information Center

Kabaca, Tolga

2013-01-01

Solution set of any inequality or compound inequality, which has one-variable, lies in the real line which is one dimensional. So a difficulty appears when computer assisted graphical representation is intended to use for teaching these topics. Sketching a one-dimensional graph by using computer software is not a straightforward work. In this…

The Diagnostic Drawing Series and the Tree Rating Scale: An Isomorphic Representation of Multiple Personality Disorder, Major Depression, and Schizophrenic Populations.

ERIC Educational Resources Information Center

Morris, Maureen Batza

1995-01-01

The tree drawings of 80 subjects, who were diagnosed with either multiple personality disorder, schizophrenia, or major depression, and a control group, were rated. Patterns were examined and graphs were used to depict results. Certain features were found to distinguish each category. The descriptive statistical findings were both consistent and…

Review of Designs for Haptic Data Visualization.

PubMed

Paneels, Sabrina; Roberts, Jonathan C

2010-01-01

There are many different uses for haptics, such as training medical practitioners, teleoperation, or navigation of virtual environments. This review focuses on haptic methods that display data. The hypothesis is that haptic devices can be used to present information, and consequently, the user gains quantitative, qualitative, or holistic knowledge about the presented data. Not only is this useful for users who are blind or partially sighted (who can feel line graphs, for instance), but also the haptic modality can be used alongside other modalities, to increase the amount of variables being presented, or to duplicate some variables to reinforce the presentation. Over the last 20 years, a significant amount of research has been done in haptic data presentation; e.g., researchers have developed force feedback line graphs, bar charts, and other forms of haptic representations. However, previous research is published in different conferences and journals, with different application emphases. This paper gathers and collates these various designs to provide a comprehensive review of designs for haptic data visualization. The designs are classified by their representation: Charts, Maps, Signs, Networks, Diagrams, Images, and Tables. This review provides a comprehensive reference for researchers and learners, and highlights areas for further research.

Graph-Based Norm Explanation

NASA Astrophysics Data System (ADS)

Croitoru, Madalina; Oren, Nir; Miles, Simon; Luck, Michael

Norms impose obligations, permissions and prohibitions on individual agents operating as part of an organisation. Typically, the purpose of such norms is to ensure that an organisation acts in some socially (or mutually) beneficial manner, possibly at the expense of individual agent utility. In this context, agents are normaware if they are able to reason about which norms are applicable to them, and to decide whether to comply with or ignore them. While much work has focused on the creation of norm-aware agents, much less has been concerned with aiding system designers in understanding the effects of norms on a system. The ability to understand such norm effects can aid the designer in avoiding incorrect norm specification, eliminating redundant norms and reducing normative conflict. In this paper, we address the problem of norm understanding by providing explanations as to why a norm is applicable, violated, or in some other state. We make use of conceptual graph based semantics to provide a graphical representation of the norms within a system. Given knowledge of the current and historical state of the system, such a representation allows for explanation of the state of norms, showing for example why they may have been activated or violated.

Wasatch: An architecture-proof multiphysics development environment using a Domain Specific Language and graph theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saad, Tony; Sutherland, James C.

To address the coding and software challenges of modern hybrid architectures, we propose an approach to multiphysics code development for high-performance computing. This approach is based on using a Domain Specific Language (DSL) in tandem with a directed acyclic graph (DAG) representation of the problem to be solved that allows runtime algorithm generation. When coupled with a large-scale parallel framework, the result is a portable development framework capable of executing on hybrid platforms and handling the challenges of multiphysics applications. In addition, we share our experience developing a code in such an environment – an effort that spans an interdisciplinarymore » team of engineers and computer scientists.« less

Knowledge represented using RDF semantic network in the concept of semantic web

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lukasova, A., E-mail: alena.lukasova@osu.cz; Vajgl, M., E-mail: marek.vajgl@osu.cz; Zacek, M., E-mail: martin.zacek@osu.cz

The RDF(S) model has been declared as the basic model to capture knowledge of the semantic web. It provides a common and flexible way to decompose composed knowledge to elementary statements, which can be represented by RDF triples or by RDF graph vectors. From the logical point of view, elements of knowledge can be expressed using at most binary predicates, which can be converted to RDF-triples or graph vectors. However, it is not able to capture implicit knowledge representable by logical formulas. This contribution shows how existing approaches (semantic networks and clausal form logic) can be combined together with RDFmore » to obtain RDF-compatible system with ability to represent implicit knowledge and inference over knowledge base.« less

Wasatch: An architecture-proof multiphysics development environment using a Domain Specific Language and graph theory

DOE PAGES

Saad, Tony; Sutherland, James C.

2016-05-04

To address the coding and software challenges of modern hybrid architectures, we propose an approach to multiphysics code development for high-performance computing. This approach is based on using a Domain Specific Language (DSL) in tandem with a directed acyclic graph (DAG) representation of the problem to be solved that allows runtime algorithm generation. When coupled with a large-scale parallel framework, the result is a portable development framework capable of executing on hybrid platforms and handling the challenges of multiphysics applications. In addition, we share our experience developing a code in such an environment – an effort that spans an interdisciplinarymore » team of engineers and computer scientists.« less

Neighborhood graph and learning discriminative distance functions for clinical decision support.

PubMed

Tsymbal, Alexey; Zhou, Shaohua Kevin; Huber, Martin

2009-01-01

There are two essential reasons for the slow progress in the acceptance of clinical case retrieval and similarity search-based decision support systems; the especial complexity of clinical data making it difficult to define a meaningful and effective distance function on them and the lack of transparency and explanation ability in many existing clinical case retrieval decision support systems. In this paper, we try to address these two problems by introducing a novel technique for visualizing inter-patient similarity based on a node-link representation with neighborhood graphs and by considering two techniques for learning discriminative distance function that help to combine the power of strong "black box" learners with the transparency of case retrieval and nearest neighbor classification.

Systems Engineering Design Via Experimental Operation Research: Complex Organizational Metric for Programmatic Risk Environments (COMPRE)

NASA Technical Reports Server (NTRS)

Mog, Robert A.

1999-01-01

Unique and innovative graph theory, neural network, organizational modeling, and genetic algorithms are applied to the design and evolution of programmatic and organizational architectures. Graph theory representations of programs and organizations increase modeling capabilities and flexibility, while illuminating preferable programmatic/organizational design features. Treating programs and organizations as neural networks results in better system synthesis, and more robust data modeling. Organizational modeling using covariance structures enhances the determination of organizational risk factors. Genetic algorithms improve programmatic evolution characteristics, while shedding light on rulebase requirements for achieving specified technological readiness levels, given budget and schedule resources. This program of research improves the robustness and verifiability of systems synthesis tools, including the Complex Organizational Metric for Programmatic Risk Environments (COMPRE).

Lexicons, contexts, events, and images: commentary on Elman (2009) from the perspective of dual coding theory.

PubMed

Paivio, Allan; Sadoski, Mark

2011-01-01

Elman (2009) proposed that the traditional role of the mental lexicon in language processing can largely be replaced by a theoretical model of schematic event knowledge founded on dynamic context-dependent variables. We evaluate Elman's approach and propose an alternative view, based on dual coding theory and evidence that modality-specific cognitive representations contribute strongly to word meaning and language performance across diverse contexts which also have effects predictable from dual coding theory. Copyright © 2010 Cognitive Science Society, Inc.

Whiteboard Confessionals: Investigating a New Model Using Student Representations in Teaching Astro 101

NASA Astrophysics Data System (ADS)

Prather, Edward

2018-01-01

Astronomy education researchers in the Department of Astronomy at the University of Arizona have been investigating a new framework for getting students to engage in discussions about fundamental astronomy topics. This framework is intended to also provide students with explicit feedback on the correctness and coherency of their mental models on these topics. This framework builds upon our prior efforts to create productive Pedagogical Discipline Representations (PDR). Students are asked to work collaboratively to generate their own representations (drawings, graphs, data tables, etc.) that reflect important characteristics of astrophysical scenarios presented in class. We have found these representation tasks offer tremendous insight into the broad range of ideas and knowledge students possess after instruction that includes both traditional lecture and actively learning strategies. In particular, we find that some of our students are able to correctly answer challenging multiple-choice questions on topics, however, they struggle to accurately create representations of these same topics themselves. Our work illustrates that some of our students are not developing a robust level of discipline fluency with many core ideas in astronomy, even after engaging with active learning strategies.

The Real and Promised Brasilia: "An Asymmetrical Symbol in 1960s Brazilian Literature"

ERIC Educational Resources Information Center

Beal, Sophia

2010-01-01

To write about Brasilia in the 1960s was to write about a dual city, part symbol and part reality. This dual city grew out of an impulse to oversimplify representations of the real city in order to preserve its mythic promise of national progress. The oversimplifications derived from a national desire to showcase how Brazil had found an authentic…

Navigation based on a sensorimotor representation: a virtual reality study

NASA Astrophysics Data System (ADS)

Zetzsche, Christoph; Galbraith, Christopher; Wolter, Johannes; Schill, Kerstin

2007-02-01

We investigate the hypothesis that the basic representation of space which underlies human navigation does not resemble an image-like map and is not restricted by the laws of Euclidean geometry. For this we developed a new experimental technique in which we use the properties of a virtual environment (VE) to directly influence the development of the representation. We compared the navigation performance of human observers under two conditions. Either the VE is consistent with the geometrical properties of physical space and could hence be represented in a map-like fashion, or it contains severe violations of Euclidean metric and planar topology, and would thus pose difficulties for the correct development of such a representation. Performance is not influenced by this difference, suggesting that a map-like representation is not the major basis of human navigation. Rather, the results are consistent with a representation which is similar to a non-planar graph augmented with path length information, or with a sensorimotor representation which combines sensory properties and motor actions. The latter may be seen as part of a revised view of perceptual processes due to recent results in psychology and neurobiology, which indicate that the traditional strict separation of sensory and motor systems is no longer tenable.

Dual coding theory, word abstractness, and emotion: a critical review of Kousta et al. (2011).

PubMed

Paivio, Allan

2013-02-01

Kousta, Vigliocco, Del Campo, Vinson, and Andrews (2011) questioned the adequacy of dual coding theory and the context availability model as explanations of representational and processing differences between concrete and abstract words. They proposed an alternative approach that focuses on the role of emotional content in the processing of abstract concepts. Their dual coding critique is, however, based on impoverished and, in some respects, incorrect interpretations of the theory and its implications. This response corrects those gaps and misinterpretations and summarizes research findings that show predicted variations in the effects of dual coding variables in different tasks and contexts. Especially emphasized is an empirically supported dual coding theory of emotion that goes beyond the Kousta et al. emphasis on emotion in abstract semantics. 2013 APA, all rights reserved

EpiGeNet: A Graph Database of Interdependencies Between Genetic and Epigenetic Events in Colorectal Cancer.

PubMed

Balaur, Irina; Saqi, Mansoor; Barat, Ana; Lysenko, Artem; Mazein, Alexander; Rawlings, Christopher J; Ruskin, Heather J; Auffray, Charles

2017-10-01

The development of colorectal cancer (CRC)-the third most common cancer type-has been associated with deregulations of cellular mechanisms stimulated by both genetic and epigenetic events. StatEpigen is a manually curated and annotated database, containing information on interdependencies between genetic and epigenetic signals, and specialized currently for CRC research. Although StatEpigen provides a well-developed graphical user interface for information retrieval, advanced queries involving associations between multiple concepts can benefit from more detailed graph representation of the integrated data. This can be achieved by using a graph database (NoSQL) approach. Data were extracted from StatEpigen and imported to our newly developed EpiGeNet, a graph database for storage and querying of conditional relationships between molecular (genetic and epigenetic) events observed at different stages of colorectal oncogenesis. We illustrate the enhanced capability of EpiGeNet for exploration of different queries related to colorectal tumor progression; specifically, we demonstrate the query process for (i) stage-specific molecular events, (ii) most frequently observed genetic and epigenetic interdependencies in colon adenoma, and (iii) paths connecting key genes reported in CRC and associated events. The EpiGeNet framework offers improved capability for management and visualization of data on molecular events specific to CRC initiation and progression.

A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra.

PubMed

Kou, Qiang; Wu, Si; Tolic, Nikola; Paša-Tolic, Ljiljana; Liu, Yunlong; Liu, Xiaowen

2017-05-01

Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a 'bird's eye view' of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem. We propose a new data structure, called the mass graph, for efficient representation of proteoforms and design mass graph alignment algorithms. We developed TopMG, a mass graph-based software tool for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry datasets showed that TopMG outperformed existing methods in identifying complex proteoforms. http://proteomics.informatics.iupui.edu/software/topmg/. xwliu@iupui.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Optimal Multiple Surface Segmentation With Shape and Context Priors

PubMed Central

Bai, Junjie; Garvin, Mona K.; Sonka, Milan; Buatti, John M.; Wu, Xiaodong

2014-01-01

Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary evidence, large object deformations, and mutual influence between adjacent objects. This paper reports a novel approach to multi-object segmentation that incorporates both shape and context prior knowledge in a 3-D graph-theoretic framework to help overcome the stated challenges. We employ an arc-based graph representation to incorporate a wide spectrum of prior information through pair-wise energy terms. In particular, a shape-prior term is used to penalize local shape changes and a context-prior term is used to penalize local surface-distance changes from a model of the expected shape and surface distances, respectively. The globally optimal solution for multiple surfaces is obtained by computing a maximum flow in a low-order polynomial time. The proposed method was validated on intraretinal layer segmentation of optical coherence tomography images and demonstrated statistically significant improvement of segmentation accuracy compared to our earlier graph-search method that was not utilizing shape and context priors. The mean unsigned surface positioning errors obtained by the conventional graph-search approach (6.30 ± 1.58 μm) was improved to 5.14 ± 0.99 μm when employing our new method with shape and context priors. PMID:23193309

Introducing the slime mold graph repository

NASA Astrophysics Data System (ADS)

Dirnberger, M.; Mehlhorn, K.; Mehlhorn, T.

2017-07-01

We introduce the slime mold graph repository or SMGR, a novel data collection promoting the visibility, accessibility and reuse of experimental data revolving around network-forming slime molds. By making data readily available to researchers across multiple disciplines, the SMGR promotes novel research as well as the reproduction of original results. While SMGR data may take various forms, we stress the importance of graph representations of slime mold networks due to their ease of handling and their large potential for reuse. Data added to the SMGR stands to gain impact beyond initial publications or even beyond its domain of origin. We initiate the SMGR with the comprehensive Kist Europe data set focusing on the slime mold Physarum polycephalum, which we obtained in the course of our original research. It contains sequences of images documenting growth and network formation of the organism under constant conditions. Suitable image sequences depicting the typical P. polycephalum network structures are used to compute sequences of graphs faithfully capturing them. Given such sequences, node identities are computed, tracking the development of nodes over time. Based on this information we demonstrate two out of many possible ways to begin exploring the data. The entire data set is well-documented, self-contained and ready for inspection at http://smgr.mpi-inf.mpg.de.

Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.

PubMed

Jie, Biao; Liu, Mingxia; Zhang, Daoqiang; Shen, Dinggang

2018-05-01

As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging data obtained from the Alzheimer's disease neuroimaging initiative database. Experimental results demonstrate that the proposed method outperforms several state-of-the-art graph-based methods in MCI classification.

Discrete bacteria foraging optimization algorithm for graph based problems - a transition from continuous to discrete

NASA Astrophysics Data System (ADS)

Sur, Chiranjib; Shukla, Anupam

2018-03-01

Bacteria Foraging Optimisation Algorithm is a collective behaviour-based meta-heuristics searching depending on the social influence of the bacteria co-agents in the search space of the problem. The algorithm faces tremendous hindrance in terms of its application for discrete problems and graph-based problems due to biased mathematical modelling and dynamic structure of the algorithm. This had been the key factor to revive and introduce the discrete form called Discrete Bacteria Foraging Optimisation (DBFO) Algorithm for discrete problems which exceeds the number of continuous domain problems represented by mathematical and numerical equations in real life. In this work, we have mainly simulated a graph-based road multi-objective optimisation problem and have discussed the prospect of its utilisation in other similar optimisation problems and graph-based problems. The various solution representations that can be handled by this DBFO has also been discussed. The implications and dynamics of the various parameters used in the DBFO are illustrated from the point view of the problems and has been a combination of both exploration and exploitation. The result of DBFO has been compared with Ant Colony Optimisation and Intelligent Water Drops Algorithms. Important features of DBFO are that the bacteria agents do not depend on the local heuristic information but estimates new exploration schemes depending upon the previous experience and covered path analysis. This makes the algorithm better in combination generation for graph-based problems and combination generation for NP hard problems.

VIGOR: Interactive Visual Exploration of Graph Query Results.

PubMed

Pienta, Robert; Hohman, Fred; Endert, Alex; Tamersoy, Acar; Roundy, Kevin; Gates, Chris; Navathe, Shamkant; Chau, Duen Horng

2018-01-01

Finding patterns in graphs has become a vital challenge in many domains from biological systems, network security, to finance (e.g., finding money laundering rings of bankers and business owners). While there is significant interest in graph databases and querying techniques, less research has focused on helping analysts make sense of underlying patterns within a group of subgraph results. Visualizing graph query results is challenging, requiring effective summarization of a large number of subgraphs, each having potentially shared node-values, rich node features, and flexible structure across queries. We present VIGOR, a novel interactive visual analytics system, for exploring and making sense of query results. VIGOR uses multiple coordinated views, leveraging different data representations and organizations to streamline analysts sensemaking process. VIGOR contributes: (1) an exemplar-based interaction technique, where an analyst starts with a specific result and relaxes constraints to find other similar results or starts with only the structure (i.e., without node value constraints), and adds constraints to narrow in on specific results; and (2) a novel feature-aware subgraph result summarization. Through a collaboration with Symantec, we demonstrate how VIGOR helps tackle real-world problems through the discovery of security blindspots in a cybersecurity dataset with over 11,000 incidents. We also evaluate VIGOR with a within-subjects study, demonstrating VIGOR's ease of use over a leading graph database management system, and its ability to help analysts understand their results at higher speed and make fewer errors.

Euclidean commute time distance embedding and its application to spectral anomaly detection

NASA Astrophysics Data System (ADS)

Albano, James A.; Messinger, David W.

2012-06-01

Spectral image analysis problems often begin by performing a preprocessing step composed of applying a transformation that generates an alternative representation of the spectral data. In this paper, a transformation based on a Markov-chain model of a random walk on a graph is introduced. More precisely, we quantify the random walk using a quantity known as the average commute time distance and find a nonlinear transformation that embeds the nodes of a graph in a Euclidean space where the separation between them is equal to the square root of this quantity. This has been referred to as the Commute Time Distance (CTD) transformation and it has the important characteristic of increasing when the number of paths between two nodes decreases and/or the lengths of those paths increase. Remarkably, a closed form solution exists for computing the average commute time distance that avoids running an iterative process and is found by simply performing an eigendecomposition on the graph Laplacian matrix. Contained in this paper is a discussion of the particular graph constructed on the spectral data for which the commute time distance is then calculated from, an introduction of some important properties of the graph Laplacian matrix, and a subspace projection that approximately preserves the maximal variance of the square root commute time distance. Finally, RX anomaly detection and Topological Anomaly Detection (TAD) algorithms will be applied to the CTD subspace followed by a discussion of their results.