Measuring graph similarity through continuous-time quantum walks and the quantum Jensen-Shannon divergence.

PubMed

Rossi, Luca; Torsello, Andrea; Hancock, Edwin R

2015-02-01

In this paper we propose a quantum algorithm to measure the similarity between a pair of unattributed graphs. We design an experiment where the two graphs are merged by establishing a complete set of connections between their nodes and the resulting structure is probed through the evolution of continuous-time quantum walks. In order to analyze the behavior of the walks without causing wave function collapse, we base our analysis on the recently introduced quantum Jensen-Shannon divergence. In particular, we show that the divergence between the evolution of two suitably initialized quantum walks over this structure is maximum when the original pair of graphs is isomorphic. We also prove that under special conditions the divergence is minimum when the sets of eigenvalues of the Hamiltonians associated with the two original graphs have an empty intersection.

Gain-Sparsity and Symmetry-Forced Rigidity in the Plane.

PubMed

Jordán, Tibor; Kaszanitzky, Viktória E; Tanigawa, Shin-Ichi

We consider planar bar-and-joint frameworks with discrete point group symmetry in which the joint positions are as generic as possible subject to the symmetry constraint. We provide combinatorial characterizations for symmetry-forced rigidity of such structures with rotation symmetry or dihedral symmetry of order 2 k with odd k , unifying and extending previous work on this subject. We also explore the matroidal background of our results and show that the matroids induced by the row independence of the orbit matrices of the symmetric frameworks are isomorphic to gain sparsity matroids defined on the quotient graph of the framework, whose edges are labeled by elements of the corresponding symmetry group. The proofs are based on new Henneberg type inductive constructions of the gain graphs that correspond to the bases of the matroids in question, which can also be seen as symmetry preserving graph operations in the original graph.

Pattern formations and optimal packing.

PubMed

Mityushev, Vladimir

2016-04-01

Patterns of different symmetries may arise after solution to reaction-diffusion equations. Hexagonal arrays, layers and their perturbations are observed in different models after numerical solution to the corresponding initial-boundary value problems. We demonstrate an intimate connection between pattern formations and optimal random packing on the plane. The main study is based on the following two points. First, the diffusive flux in reaction-diffusion systems is approximated by piecewise linear functions in the framework of structural approximations. This leads to a discrete network approximation of the considered continuous problem. Second, the discrete energy minimization yields optimal random packing of the domains (disks) in the representative cell. Therefore, the general problem of pattern formations based on the reaction-diffusion equations is reduced to the geometric problem of random packing. It is demonstrated that all random packings can be divided onto classes associated with classes of isomorphic graphs obtained from the Delaunay triangulation. The unique optimal solution is constructed in each class of the random packings. If the number of disks per representative cell is finite, the number of classes of isomorphic graphs, hence, the number of optimal packings is also finite. Copyright © 2016 Elsevier Inc. All rights reserved.

Internally connected graphs and the Kashiwara-Vergne Lie algebra

NASA Astrophysics Data System (ADS)

Felder, Matteo

2018-06-01

It is conjectured that the Kashiwara-Vergne Lie algebra \\widehat{krv}_2 is isomorphic to the direct sum of the Grothendieck-Teichmüller Lie algebra grt_1 and a one-dimensional Lie algebra. In this paper, we use the graph complex of internally connected graphs to define a nested sequence of Lie subalgebras of \\widehat{krv}_2 whose intersection is grt_1, thus giving a way to interpolate between these two Lie algebras.

Some cycle-supermagic labelings of the calendula graphs

NASA Astrophysics Data System (ADS)

Pradipta, T. R.; Salman, A. N. M.

2018-01-01

In this paper, we introduce a calendula graph, denoted by Clm,n . It is a graph constructed from a cycle on m vertices Cm and m copies of Cn which are Cn1 , Cn2 , ⋯, Cnm and grafting the i-th edge of Cm to an edge of in Cni for each i ∈ {1,2,⋯,m}. A graph G = (V, E) admits a Cn -covering, if every edge e ∈ E(G) belongs to a subgraph of G isomorphic to Cn . The graph G is called cycle-magic, if there exists a total labeling ϕ: V ∪ E → {1,2,…,|V|+|E|} such that for every subgraph Cn ‧ = (V‧,E‧) of G isomorphic to Cn has the same weight. In this case, the weight of Cn , denoted by ϕ(Cn ’), is defined as ∑ v∈V(C’n ) ϕ(v) + ∑ e∈E(C’n ) ϕ(e). Furthermore, G is called cycle-supermagic, if ϕ:V→{1,2,…,|V|}. In this paper, we provide some cycle-supermagic labelings of calendula graphs. In order to prove it, we develop a technique, to make a partition of a multiset into m sub-multisets with the same cardinality such that the sum of all elements of each sub-multiset is same. The technique is called an m-balanced multiset.

Quantum walk on a chimera graph

NASA Astrophysics Data System (ADS)

Xu, Shu; Sun, Xiangxiang; Wu, Jizhou; Zhang, Wei-Wei; Arshed, Nigum; Sanders, Barry C.

2018-05-01

We analyse a continuous-time quantum walk on a chimera graph, which is a graph of choice for designing quantum annealers, and we discover beautiful quantum walk features such as localization that starkly distinguishes classical from quantum behaviour. Motivated by technological thrusts, we study continuous-time quantum walk on enhanced variants of the chimera graph and on diminished chimera graph with a random removal of vertices. We explain the quantum walk by constructing a generating set for a suitable subgroup of graph isomorphisms and corresponding symmetry operators that commute with the quantum walk Hamiltonian; the Hamiltonian and these symmetry operators provide a complete set of labels for the spectrum and the stationary states. Our quantum walk characterization of the chimera graph and its variants yields valuable insights into graphs used for designing quantum-annealers.

Top-k similar graph matching using TraM in biological networks.

PubMed

Amin, Mohammad Shafkat; Finley, Russell L; Jamil, Hasan M

2012-01-01

Many emerging database applications entail sophisticated graph-based query manipulation, predominantly evident in large-scale scientific applications. To access the information embedded in graphs, efficient graph matching tools and algorithms have become of prime importance. Although the prohibitively expensive time complexity associated with exact subgraph isomorphism techniques has limited its efficacy in the application domain, approximate yet efficient graph matching techniques have received much attention due to their pragmatic applicability. Since public domain databases are noisy and incomplete in nature, inexact graph matching techniques have proven to be more promising in terms of inferring knowledge from numerous structural data repositories. In this paper, we propose a novel technique called TraM for approximate graph matching that off-loads a significant amount of its processing on to the database making the approach viable for large graphs. Moreover, the vector space embedding of the graphs and efficient filtration of the search space enables computation of approximate graph similarity at a throw-away cost. We annotate nodes of the query graphs by means of their global topological properties and compare them with neighborhood biased segments of the datagraph for proper matches. We have conducted experiments on several real data sets, and have demonstrated the effectiveness and efficiency of the proposed method

On P2 ⋄ Pn -supermagic labeling of edge corona product of cycle and path graph

NASA Astrophysics Data System (ADS)

Yulianto, R.; Martini, Titin S.

2018-04-01

A simple graph G = (V, E) admits a H-covering, where H is subgraph of G, if every edge in E belongs to a subgraph of G isomorphic to H. Graph G is H-magic if there is a total labeling f:V(G)\\cup E(G)\\to 1,2,\\ldots,|V(G)|+|E(G)|, such that each subgraph {H}{\\prime }=({V}{\\prime },{E}{\\prime }) of G isomorphic to H and satisfying f{({H}{\\prime })}=def{\\sum }\\upsilon \\in {V{\\prime }}f(\\upsilon )+{\\sum }e\\in {E{\\prime }}f(e)=m(f) where m(f) is a constant magic sum. Additionaly, G admits H-supermagic if f(V)=1,2,\\ldots,|V|. The edge corona {C}n \\diamond {P}n of Cn and Pn is defined as the graph obtained by taking one copy of Cn and n copies of Pn , and then joining two end-vertices of the i-th edge of Cn to every vertex in the i-th copy of Pn . This research aim is to find H-supermagic covering on an edge corona product of cycle and path graph {C}n \\diamond {P}n where H is {P}2 \\diamond {P}n. We use k-balanced multiset to solve our reserarch. Here, we find that an edge corona product of cycle and path graph {C}n \\diamond {P}n is {P}2 \\diamond {P}n supermagic for n > 3.

Super (a*, d*)-ℋ-antimagic total covering of second order of shackle graphs

NASA Astrophysics Data System (ADS)

Hesti Agustin, Ika; Dafik; Nisviasari, Rosanita; Prihandini, R. M.

2017-12-01

Let H be a simple and connected graph. A shackle of graph H, denoted by G = shack(H, v, n), is a graph G constructed by non-trivial graphs H 1, H 2, …, H n such that, for every 1 ≤ s, t ≤ n, H s and Ht have no a common vertex with |s - t| ≥ 2 and for every 1 ≤ i ≤ n - 1, Hi and H i+1 share exactly one common vertex v, called connecting vertex, and those k - 1 connecting vertices are all distinct. The graph G is said to be an (a*, d*)-H-antimagic total graph of second order if there exist a bijective function f : V(G) ∪ E(G) → {1, 2, …, |V(G)| + |E(G)|} such that for all subgraphs isomorphic to H, the total H-weights W(H)=\\displaystyle {\\sum }v\\in V(H)f(v)+\\displaystyle {\\sum }e\\in E(H)f(e) form an arithmetic sequence of second order of \\{a* ,a* +d* ,a* +3d* ,a* +6d* ,\\ldots ,a* +(\\frac{{n}2-n}{2})d* \\}, where a* and d* are positive integers and n is the number of all subgraphs isomorphic to H. An (a*, d*)-H-antimagic total labeling of second order f is called super if the smallest labels appear in the vertices. In this paper, we study a super (a*, d*)-H antimagic total labeling of second order of G = shack(H, v, n) by using a partition technique of second order.

Generalizing a categorization of students' interpretations of linear kinematics graphs

NASA Astrophysics Data System (ADS)

Bollen, Laurens; De Cock, Mieke; Zuza, Kristina; Guisasola, Jenaro; van Kampen, Paul

2016-06-01

We have investigated whether and how a categorization of responses to questions on linear distance-time graphs, based on a study of Irish students enrolled in an algebra-based course, could be adopted and adapted to responses from students enrolled in calculus-based physics courses at universities in Flanders, Belgium (KU Leuven) and the Basque Country, Spain (University of the Basque Country). We discuss how we adapted the categorization to accommodate a much more diverse student cohort and explain how the prior knowledge of students may account for many differences in the prevalence of approaches and success rates. Although calculus-based physics students make fewer mistakes than algebra-based physics students, they encounter similar difficulties that are often related to incorrectly dividing two coordinates. We verified that a qualitative understanding of kinematics is an important but not sufficient condition for students to determine a correct value for the speed. When comparing responses to questions on linear distance-time graphs with responses to isomorphic questions on linear water level versus time graphs, we observed that the context of a question influences the approach students use. Neither qualitative understanding nor an ability to find the slope of a context-free graph proved to be a reliable predictor for the approach students use when they determine the instantaneous speed.

Determining distinct circuit in complete graphs using permutation

NASA Astrophysics Data System (ADS)

Karim, Sharmila; Ibrahim, Haslinda; Darus, Maizon Mohd

2017-11-01

A Half Butterfly Method (HBM) is a method introduced to construct the distinct circuits in complete graphs where used the concept of isomorphism. The Half Butterfly Method was applied in the field of combinatorics such as in listing permutations of n elements. However the method of determining distinct circuit using HBM for n > 4 is become tedious. Thus, in this paper, we present the method of generating distinct circuit using permutation.

Identifying compromised systems through correlation of suspicious traffic from malware behavioral analysis

NASA Astrophysics Data System (ADS)

Camilo, Ana E. F.; Grégio, André; Santos, Rafael D. C.

2016-05-01

Malware detection may be accomplished through the analysis of their infection behavior. To do so, dynamic analysis systems run malware samples and extract their operating system activities and network traffic. This traffic may represent malware accessing external systems, either to steal sensitive data from victims or to fetch other malicious artifacts (configuration files, additional modules, commands). In this work, we propose the use of visualization as a tool to identify compromised systems based on correlating malware communications in the form of graphs and finding isomorphisms between them. We produced graphs from over 6 thousand distinct network traffic files captured during malware execution and analyzed the existing relationships among malware samples and IP addresses.

Power optimization in logic isomers

NASA Technical Reports Server (NTRS)

Panwar, Ramesh; Rennels, David; Alkalaj, Leon

1993-01-01

Logic isomers are labeled, 2-isomorphic graphs that implement the same logic function. Logic isomers may have significantly different power requirements even though they have the same number of transistors in the implementation. The power requirements of the isomers depend on the transition activity of the input signals. The power requirements of isomorphic graph isomers of n-input NAND and NOR gates are shown. Choosing the less power-consuming isomer instead of the others can yield significant power savings. Experimental results on a ripple-carry adder are presented to show that the implementation using the least power-consuming isomers requires approximately 10 percent less power than the implementation using the most power-consuming isomers. Simulations of other random logic designs also confirm that designs using less power-consuming isomers can reduce the logic power demand by approximately 10 percent as compared to designs using more power-consuming isomers.

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

Quantification of network structural dissimilarities.

PubMed

Schieber, Tiago A; Carpi, Laura; Díaz-Guilera, Albert; Pardalos, Panos M; Masoller, Cristina; Ravetti, Martín G

2017-01-09

Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.

The Ulam Index: Methods of Theoretical Computer Science Help in Identifying Chemical Substances

NASA Technical Reports Server (NTRS)

Beltran, Adriana; Salvador, James

1997-01-01

In this paper, we show how methods developed for solving a theoretical computer problem of graph isomorphism are used in structural chemistry. We also discuss potential applications of these methods to exobiology: the search for life outside Earth.

Isospectral discrete and quantum graphs with the same flip counts and nodal counts

NASA Astrophysics Data System (ADS)

Juul, Jonas S.; Joyner, Christopher H.

2018-06-01

The existence of non-isomorphic graphs which share the same Laplace spectrum (to be referred to as isospectral graphs) leads naturally to the following question: what additional information is required in order to resolve isospectral graphs? It was suggested by Band, Shapira and Smilansky that this might be achieved by either counting the number of nodal domains or the number of times the eigenfunctions change sign (the so-called flip count) (Band et al 2006 J. Phys. A: Math. Gen. 39 13999–4014 Band and Smilansky 2007 Eur. Phys. J. Spec. Top. 145 171–9). Recent examples of (discrete) isospectral graphs with the same flip count and nodal count have been constructed by Ammann by utilising Godsil–McKay switching (Ammann private communication). Here, we provide a simple alternative mechanism that produces systematic examples of both discrete and quantum isospectral graphs with the same flip and nodal counts.

Computing Role Assignments of Proper Interval Graphs in Polynomial Time

NASA Astrophysics Data System (ADS)

Heggernes, Pinar; van't Hof, Pim; Paulusma, Daniël

A homomorphism from a graph G to a graph R is locally surjective if its restriction to the neighborhood of each vertex of G is surjective. Such a homomorphism is also called an R-role assignment of G. Role assignments have applications in distributed computing, social network theory, and topological graph theory. The Role Assignment problem has as input a pair of graphs (G,R) and asks whether G has an R-role assignment. This problem is NP-complete already on input pairs (G,R) where R is a path on three vertices. So far, the only known non-trivial tractable case consists of input pairs (G,R) where G is a tree. We present a polynomial time algorithm that solves Role Assignment on all input pairs (G,R) where G is a proper interval graph. Thus we identify the first graph class other than trees on which the problem is tractable. As a complementary result, we show that the problem is Graph Isomorphism-hard on chordal graphs, a superclass of proper interval graphs and trees.

GenoLink: a graph-based querying and browsing system for investigating the function of genes and proteins.

PubMed

Durand, Patrick; Labarre, Laurent; Meil, Alain; Divo, Jean-Louis; Vandenbrouck, Yves; Viari, Alain; Wojcik, Jérôme

2006-01-17

A large variety of biological data can be represented by graphs. These graphs can be constructed from heterogeneous data coming from genomic and post-genomic technologies, but there is still need for tools aiming at exploring and analysing such graphs. This paper describes GenoLink, a software platform for the graphical querying and exploration of graphs. GenoLink provides a generic framework for representing and querying data graphs. This framework provides a graph data structure, a graph query engine, allowing to retrieve sub-graphs from the entire data graph, and several graphical interfaces to express such queries and to further explore their results. A query consists in a graph pattern with constraints attached to the vertices and edges. A query result is the set of all sub-graphs of the entire data graph that are isomorphic to the pattern and satisfy the constraints. The graph data structure does not rely upon any particular data model but can dynamically accommodate for any user-supplied data model. However, for genomic and post-genomic applications, we provide a default data model and several parsers for the most popular data sources. GenoLink does not require any programming skill since all operations on graphs and the analysis of the results can be carried out graphically through several dedicated graphical interfaces. GenoLink is a generic and interactive tool allowing biologists to graphically explore various sources of information. GenoLink is distributed either as a standalone application or as a component of the Genostar/Iogma platform. Both distributions are free for academic research and teaching purposes and can be requested at academy@genostar.com. A commercial licence form can be obtained for profit company at info@genostar.com. See also http://www.genostar.org.

GenoLink: a graph-based querying and browsing system for investigating the function of genes and proteins

PubMed Central

Durand, Patrick; Labarre, Laurent; Meil, Alain; Divo1, Jean-Louis; Vandenbrouck, Yves; Viari, Alain; Wojcik, Jérôme

2006-01-01

Background A large variety of biological data can be represented by graphs. These graphs can be constructed from heterogeneous data coming from genomic and post-genomic technologies, but there is still need for tools aiming at exploring and analysing such graphs. This paper describes GenoLink, a software platform for the graphical querying and exploration of graphs. Results GenoLink provides a generic framework for representing and querying data graphs. This framework provides a graph data structure, a graph query engine, allowing to retrieve sub-graphs from the entire data graph, and several graphical interfaces to express such queries and to further explore their results. A query consists in a graph pattern with constraints attached to the vertices and edges. A query result is the set of all sub-graphs of the entire data graph that are isomorphic to the pattern and satisfy the constraints. The graph data structure does not rely upon any particular data model but can dynamically accommodate for any user-supplied data model. However, for genomic and post-genomic applications, we provide a default data model and several parsers for the most popular data sources. GenoLink does not require any programming skill since all operations on graphs and the analysis of the results can be carried out graphically through several dedicated graphical interfaces. Conclusion GenoLink is a generic and interactive tool allowing biologists to graphically explore various sources of information. GenoLink is distributed either as a standalone application or as a component of the Genostar/Iogma platform. Both distributions are free for academic research and teaching purposes and can be requested at academy@genostar.com. A commercial licence form can be obtained for profit company at info@genostar.com. See also . PMID:16417636

QuateXelero: An Accelerated Exact Network Motif Detection Algorithm

PubMed Central

Khakabimamaghani, Sahand; Sharafuddin, Iman; Dichter, Norbert; Koch, Ina; Masoudi-Nejad, Ali

2013-01-01

Finding motifs in biological, social, technological, and other types of networks has become a widespread method to gain more knowledge about these networks’ structure and function. However, this task is very computationally demanding, because it is highly associated with the graph isomorphism which is an NP problem (not known to belong to P or NP-complete subsets yet). Accordingly, this research is endeavoring to decrease the need to call NAUTY isomorphism detection method, which is the most time-consuming step in many existing algorithms. The work provides an extremely fast motif detection algorithm called QuateXelero, which has a Quaternary Tree data structure in the heart. The proposed algorithm is based on the well-known ESU (FANMOD) motif detection algorithm. The results of experiments on some standard model networks approve the overal superiority of the proposed algorithm, namely QuateXelero, compared with two of the fastest existing algorithms, G-Tries and Kavosh. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network. PMID:23874498

Graph theory and the Virasoro master equation

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

Obers, N.A.J.

1991-04-01

A brief history of affine Lie algebra, the Virasoro algebra and its culmination in the Virasoro master equations is given. By studying ansaetze of the master equation, we obtain exact solutions and gain insight in the structure of large slices of affine-Virasoro space. We find an isomorphism between the constructions in the ansatz SO(n){sub diag}, which is a set of unitary, generically irrational affine-Virasoro constructions on SO(n), and the unlabelled graphs, while, conversely, a group-theoretic and conformal field-theoretic identification is obtained for every graph of graph theory. We also define a class of magic'' Lie group bases in which themore » Virasoro master equation admits a simple metric ansatz (gmetric), whose structure is visible in the high-level expansion. When a magic basis is real on compact g, the corresponding g{sub metric} is a large system of unitary, generically irrational conformal field theories. Examples in this class include the graph-theory ansatz SO(n){sub diag} in the Cartesian basis of SO(n), and the ansatz SU(n){sub metric} in the Pauli-like basis of SU(n). Finally, we define the sine-area graphs'' of SU(n), which label the conformal field theories of SU(n){sub metric}, and we note that, in similar fashion, each magic basis of g defines a generalized graph theory on g which labels the conformal field theories of g{sub metric}. 24 figs., 4 tabs.« less

Isomorphism of dimer configurations and spanning trees on finite square lattices

NASA Astrophysics Data System (ADS)

Brankov, J. G.

1995-09-01

One-to-one mappings of the close-packed dimer configurations on a finite square lattice with free boundaries L onto the spanning trees of a related graph (or two-graph) G are found. The graph (two-graph) G can be constructed from L by: (1) deleting all the vertices of L with arbitrarily fixed parity of the row and column numbers; (2) suppressing all the vertices of degree 2 except those of degree 2 in L; (3) merging all the vertices of degree 1 into a single vertex g. The matrix Kirchhoff theorem reduces the enumeration problem for the spanning trees on G to the eigenvalue problem for the discrete Laplacian on the square lattice L'=G g with mixed Dirichlet-Neumann boundary conditions in at least one direction. That fact explains some of the unusual finite-size properties of the dimer model.

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…

Graph theory and the Virasoro master equation

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

Obers, N.A.J.

1991-01-01

A brief history of affine Lie algebra, the Virasoro algebra and its culmination in the Virasoro master equation is given. By studying ansaetze of the master equation, the author obtains exact solutions and gains insight in the structure of large slices of affine-Virasoro space. He finds an isomorphism between the constructions in the ansatz SO(n){sub diag}, which is a set of unitary, generically irrational affine-Virasoro constructions on SO(n), and the unlabeled graphs of order n. On the one hand, the conformal constructions, are classified by the graphs, while, conversely, a group-theoretic and conformal field-theoretic identification is obtained for every graphmore » of graph theory. He also defines a class of magic Lie group bases in which the Virasoro master equation admits a simple metric ansatz {l brace}g{sub metric}{r brace}, whose structure is visible in the high-level expansion. When a magic basis is real on compact g, the corresponding g{sub metric} is a large system of unitary, generically irrational conformal field theories. Examples in this class include the graph-theory ansatz SO(n){sub diag} in the Cartesian basis of SO(n), and the ansatz SU(n){sub metric} in the Pauli-like basis of SU(n). Finally, he defines the sine-area graphs' of SU(n), which label the conformal field theories of SU(n){sub metric}, and he notes that, in similar fashion, each magic basis of g defines a generalized graph theory on g which labels the conformal field theories of g{sub metric}.« less

On Graph Isomorphism and the PageRank Algorithm

DTIC Science & Technology

2008-09-01

specifies the probability of visiting each node from any other node. The perturbed matrix satisfies the Perron - Frobenius theorem’s conditions. Therefore... Frobenius and Perron theorems establishes the matrix must yield the dominant eigenvalue, one. Normalizing the unique and associated dominant eigenvector...is constructed such that none of its entries equal zero. An arbitrary PageRank matrix, S, is irreducible and satisfies the Perron - Frobenius

Parameter space exploration within dynamic simulations of signaling networks.

PubMed

De Ambrosi, Cristina; Barla, Annalisa; Tortolina, Lorenzo; Castagnino, Nicoletta; Pesenti, Raffaele; Verri, Alessandro; Ballestrero, Alberto; Patrone, Franco; Parodi, Silvio

2013-02-01

We started offering an introduction to very basic aspects of molecular biology, for the reader coming from computer sciences, information technology, mathematics. Similarly we offered a minimum of information about pathways and networks in graph theory, for a reader coming from the bio-medical sector. At the crossover about the two different types of expertise, we offered some definition about Systems Biology. The core of the article deals with a Molecular Interaction Map (MIM), a network of biochemical interactions involved in a small signaling-network sub-region relevant in breast cancer. We explored robustness/sensitivity to random perturbations. It turns out that our MIM is a non-isomorphic directed graph. For non physiological directions of propagation of the signal the network is quite resistant to perturbations. The opposite happens for biologically significant directions of signal propagation. In these cases we can have no signal attenuation, and even signal amplification. Signal propagation along a given pathway is highly unidirectional, with the exception of signal-feedbacks, that again have a specific biological role and significance. In conclusion, even a relatively small network like our present MIM reveals the preponderance of specific biological functions over unspecific isomorphic behaviors. This is perhaps the consequence of hundreds of millions of years of biological evolution.

On the generality of the topological theory of visual shape perception.

PubMed

Kanbe, Fumio

2013-01-01

This study used a series of six closely related experiments to examine whether individuals use topological structures to discriminate figures. Strict control was exerted over the selection of stimuli, which were a specific type of randomly generated lined figures that can be classified using isomorphic sets defined by graph theory. Any two figures within an isomorphic set possessed the same topological structure. The experiments described here used a same/different discrimination task with simultaneously presented pairs of figures: (a) identical pairs (Id pairs), in which each pair of figures had the same topological and superficial properties; (b) nonidentical and isomorphic pairs (Iso pairs), in which each pair had the same topological but different superficial properties; and (c) nonidentical and nonisomorphic pairs (Noniso pairs), in which each pair had different topological properties. Within these experiments I varied the conditions related to the intersecting line segments, presentation of points defining each figure, figure complexity, stimulus aspect ratios, and the parity of the total line-segment lengths between the figures in each pair. These variations showed that the latencies for making accurate discriminations were shorter for Noniso pairs than for Iso pairs, suggesting that individuals are sensitive to topology when distinguishing figures.

The hypergraph regularity method and its applications

PubMed Central

Rödl, V.; Nagle, B.; Skokan, J.; Schacht, M.; Kohayakawa, Y.

2005-01-01

Szemerédi's regularity lemma asserts that every graph can be decomposed into relatively few random-like subgraphs. This random-like behavior enables one to find and enumerate subgraphs of a given isomorphism type, yielding the so-called counting lemma for graphs. The combined application of these two lemmas is known as the regularity method for graphs and has proved useful in graph theory, combinatorial geometry, combinatorial number theory, and theoretical computer science. Here, we report on recent advances in the regularity method for k-uniform hypergraphs, for arbitrary k ≥ 2. This method, purely combinatorial in nature, gives alternative proofs of density theorems originally due to E. Szemerédi, H. Furstenberg, and Y. Katznelson. Further results in extremal combinatorics also have been obtained with this approach. The two main components of the regularity method for k-uniform hypergraphs, the regularity lemma and the counting lemma, have been obtained recently: Rödl and Skokan (based on earlier work of Frankl and Rödl) generalized Szemerédi's regularity lemma to k-uniform hypergraphs, and Nagle, Rödl, and Schacht succeeded in proving a counting lemma accompanying the Rödl–Skokan hypergraph regularity lemma. The counting lemma is proved by reducing the counting problem to a simpler one previously investigated by Kohayakawa, Rödl, and Skokan. Similar results were obtained independently by W. T. Gowers, following a different approach. PMID:15919821

A multicolour graph as a complete topological invariant for \\Omega-stable flows without periodic trajectories on surfaces

NASA Astrophysics Data System (ADS)

Kruglov, V. E.; Malyshev, D. S.; Pochinka, O. V.

2018-01-01

Studying the dynamics of a flow on surfaces by partitioning the phase space into cells with the same limit behaviour of trajectories within a cell goes back to the classical papers of Andronov, Pontryagin, Leontovich and Maier. The types of cells (the number of which is finite) and how the cells adjoin one another completely determine the topological equivalence class of a flow with finitely many special trajectories. If one trajectory is chosen in every cell of a rough flow without periodic orbits, then the cells are partitioned into so-called triangular regions of the same type. A combinatorial description of such a partition gives rise to the three-colour Oshemkov-Sharko graph, the vertices of which correspond to the triangular regions, and the edges to separatrices connecting them. Oshemkov and Sharko proved that such flows are topologically equivalent if and only if the three-colour graphs of the flows are isomorphic, and described an algorithm of distinguishing three-colour graphs. But their algorithm is not efficient with respect to graph theory. In the present paper, we describe the dynamics of Ω-stable flows without periodic trajectories on surfaces in the language of four-colour graphs, present an efficient algorithm for distinguishing such graphs, and develop a realization of a flow from some abstract graph. Bibliography: 17 titles.

Student reasoning about graphs in different contexts

NASA Astrophysics Data System (ADS)

Ivanjek, Lana; Susac, Ana; Planinic, Maja; Andrasevic, Aneta; Milin-Sipus, Zeljka

2016-06-01

This study investigates university students' graph interpretation strategies and difficulties in mathematics, physics (kinematics), and contexts other than physics. Eight sets of parallel (isomorphic) mathematics, physics, and other context questions about graphs, which were developed by us, were administered to 385 first-year students at the Faculty of Science, University of Zagreb. Students were asked to provide explanations and/or mathematical procedures with their answers. Students' main strategies and difficulties identified through the analysis of those explanations and procedures are described. Student strategies of graph interpretation were found to be largely context dependent and domain specific. A small fraction of students have used the same strategy in all three domains (mathematics, physics, and other contexts) on most sets of parallel questions. Some students have shown indications of transfer of knowledge in the sense that they used techniques and strategies developed in physics for solving (or attempting to solve) other context problems. In physics, the preferred strategy was the use of formulas, which sometimes seemed to block the use of other, more productive strategies which students displayed in other domains. Students' answers indicated the presence of slope-height confusion and interval-point confusion in all three domains. Students generally better interpreted graph slope than the area under a graph, although the concept of slope still seemed to be quite vague for many. The interpretation of the concept of area under a graph needs more attention in both physics and mathematics teaching.

Optimizing graph-based patterns to extract biomedical events from the literature

PubMed Central

2015-01-01

In BioNLP-ST 2013 We participated in the BioNLP 2013 shared tasks on event extraction. Our extraction method is based on the search for an approximate subgraph isomorphism between key context dependencies of events and graphs of input sentences. Our system was able to address both the GENIA (GE) task focusing on 13 molecular biology related event types and the Cancer Genetics (CG) task targeting a challenging group of 40 cancer biology related event types with varying arguments concerning 18 kinds of biological entities. In addition to adapting our system to the two tasks, we also attempted to integrate semantics into the graph matching scheme using a distributional similarity model for more events, and evaluated the event extraction impact of using paths of all possible lengths as key context dependencies beyond using only the shortest paths in our system. We achieved a 46.38% F-score in the CG task (ranking 3rd) and a 48.93% F-score in the GE task (ranking 4th). After BioNLP-ST 2013 We explored three ways to further extend our event extraction system in our previously published work: (1) We allow non-essential nodes to be skipped, and incorporated a node skipping penalty into the subgraph distance function of our approximate subgraph matching algorithm. (2) Instead of assigning a unified subgraph distance threshold to all patterns of an event type, we learned a customized threshold for each pattern. (3) We implemented the well-known Empirical Risk Minimization (ERM) principle to optimize the event pattern set by balancing prediction errors on training data against regularization. When evaluated on the official GE task test data, these extensions help to improve the extraction precision from 62% to 65%. However, the overall F-score stays equivalent to the previous performance due to a 1% drop in recall. PMID:26551594

Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development.

PubMed

Bandyopadhyay, Deepak; Huan, Jun; Prins, Jan; Snoeyink, Jack; Wang, Wei; Tropsha, Alexander

2009-11-01

Protein function prediction is one of the central problems in computational biology. We present a novel automated protein structure-based function prediction method using libraries of local residue packing patterns that are common to most proteins in a known functional family. Critical to this approach is the representation of a protein structure as a graph where residue vertices (residue name used as a vertex label) are connected by geometrical proximity edges. The approach employs two steps. First, it uses a fast subgraph mining algorithm to find all occurrences of family-specific labeled subgraphs for all well characterized protein structural and functional families. Second, it queries a new structure for occurrences of a set of motifs characteristic of a known family, using a graph index to speed up Ullman's subgraph isomorphism algorithm. The confidence of function inference from structure depends on the number of family-specific motifs found in the query structure compared with their distribution in a large non-redundant database of proteins. This method can assign a new structure to a specific functional family in cases where sequence alignments, sequence patterns, structural superposition and active site templates fail to provide accurate annotation.

Image-guided automatic triggering of a fractional CO2 laser in aesthetic procedures.

PubMed

Wilczyński, Sławomir; Koprowski, Robert; Wiernek, Barbara K; Błońska-Fajfrowska, Barbara

2016-09-01

Laser procedures in dermatology and aesthetic medicine are associated with the need for manual laser triggering. This leads to pulse overlapping and side effects. Automatic laser triggering based on image analysis can provide a secure fit to each successive doses of radiation. A fractional CO2 laser was used in the study. 500 images of the human skin of healthy subjects were acquired. Automatic triggering was initiated by an application together with a camera which tracks and analyses the skin in visible light. The tracking algorithm uses the methods of image analysis to overlap images. After locating the characteristic points in analysed adjacent areas, the correspondence of graphs is found. The point coordinates derived from the images are the vertices of graphs with respect to which isomorphism is sought. When the correspondence of graphs is found, it is possible to overlap the neighbouring parts of the image. The proposed method of laser triggering owing to the automatic image fitting method allows for 100% repeatability. To meet this requirement, there must be at least 13 graph vertices obtained from the image. For this number of vertices, the time of analysis of a single image is less than 0.5s. The proposed method, applied in practice, may help reduce the number of side effects during dermatological laser procedures resulting from laser pulse overlapping. In addition, it reduces treatment time and enables to propose new techniques of treatment through controlled, precise laser pulse overlapping. Copyright © 2016 Elsevier Ltd. All rights reserved.

Tree decomposition based fast search of RNA structures including pseudoknots in genomes.

PubMed

Song, Yinglei; Liu, Chunmei; Malmberg, Russell; Pan, Fangfang; Cai, Liming

2005-01-01

Searching genomes for RNA secondary structure with computational methods has become an important approach to the annotation of non-coding RNAs. However, due to the lack of efficient algorithms for accurate RNA structure-sequence alignment, computer programs capable of fast and effectively searching genomes for RNA secondary structures have not been available. In this paper, a novel RNA structure profiling model is introduced based on the notion of a conformational graph to specify the consensus structure of an RNA family. Tree decomposition yields a small tree width t for such conformation graphs (e.g., t = 2 for stem loops and only a slight increase for pseudo-knots). Within this modelling framework, the optimal alignment of a sequence to the structure model corresponds to finding a maximum valued isomorphic subgraph and consequently can be accomplished through dynamic programming on the tree decomposition of the conformational graph in time O(k(t)N(2)), where k is a small parameter; and N is the size of the projiled RNA structure. Experiments show that the application of the alignment algorithm to search in genomes yields the same search accuracy as methods based on a Covariance model with a significant reduction in computation time. In particular; very accurate searches of tmRNAs in bacteria genomes and of telomerase RNAs in yeast genomes can be accomplished in days, as opposed to months required by other methods. The tree decomposition based searching tool is free upon request and can be downloaded at our site h t t p ://w.uga.edu/RNA-informatics/software/index.php.

North American Jumelage ’Type Systems’

DTIC Science & Technology

1993-10-20

Brian Howard bhoward~saul.cis.upenn.edu Doug Howe howegcs.cornell.edu Paul Jackson jacksongcs.cornell.edu Radhakrishnan Jagadeesan rj2Gdoc.imperial.ac.uk...Curry and Howard . They have shown that there exists an ’’isomorphism’’ between the terms of typed lambda calculus and the natural deduction proofs of...linear logic for computer science 3:05-3:30 Break 3:30-4:00 G. Bellin , Oxford University: Proof-nets without boxes and graphs with orientations 4:05-4:25 H

Hearing the shape of the Ising model with a programmable superconducting-flux annealer.

PubMed

Vinci, Walter; Markström, Klas; Boixo, Sergio; Roy, Aidan; Spedalieri, Federico M; Warburton, Paul A; Severini, Simone

2014-07-16

Two objects can be distinguished if they have different measurable properties. Thus, distinguishability depends on the Physics of the objects. In considering graphs, we revisit the Ising model as a framework to define physically meaningful spectral invariants. In this context, we introduce a family of refinements of the classical spectrum and consider the quantum partition function. We demonstrate that the energy spectrum of the quantum Ising Hamiltonian is a stronger invariant than the classical one without refinements. For the purpose of implementing the related physical systems, we perform experiments on a programmable annealer with superconducting flux technology. Departing from the paradigm of adiabatic computation, we take advantage of a noisy evolution of the device to generate statistics of low energy states. The graphs considered in the experiments have the same classical partition functions, but different quantum spectra. The data obtained from the annealer distinguish non-isomorphic graphs via information contained in the classical refinements of the functions but not via the differences in the quantum spectra.

Analyzing locomotion synthesis with feature-based motion graphs.

PubMed

Mahmudi, Mentar; Kallmann, Marcelo

2013-05-01

We propose feature-based motion graphs for realistic locomotion synthesis among obstacles. Among several advantages, feature-based motion graphs achieve improved results in search queries, eliminate the need of postprocessing for foot skating removal, and reduce the computational requirements in comparison to traditional motion graphs. Our contributions are threefold. First, we show that choosing transitions based on relevant features significantly reduces graph construction time and leads to improved search performances. Second, we employ a fast channel search method that confines the motion graph search to a free channel with guaranteed clearance among obstacles, achieving faster and improved results that avoid expensive collision checking. Lastly, we present a motion deformation model based on Inverse Kinematics applied over the transitions of a solution branch. Each transition is assigned a continuous deformation range that does not exceed the original transition cost threshold specified by the user for the graph construction. The obtained deformation improves the reachability of the feature-based motion graph and in turn also reduces the time spent during search. The results obtained by the proposed methods are evaluated and quantified, and they demonstrate significant improvements in comparison to traditional motion graph techniques.

A Statistical Method to Distinguish Functional Brain Networks

PubMed Central

Fujita, André; Vidal, Maciel C.; Takahashi, Daniel Y.

2017-01-01

One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism (p < 0.001). PMID:28261045

A Statistical Method to Distinguish Functional Brain Networks.

PubMed

Fujita, André; Vidal, Maciel C; Takahashi, Daniel Y

2017-01-01

One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism ( p < 0.001).

Path-integral isomorphic Hamiltonian for including nuclear quantum effects in non-adiabatic dynamics

NASA Astrophysics Data System (ADS)

Tao, Xuecheng; Shushkov, Philip; Miller, Thomas F.

2018-03-01

We describe a path-integral approach for including nuclear quantum effects in non-adiabatic chemical dynamics simulations. For a general physical system with multiple electronic energy levels, a corresponding isomorphic Hamiltonian is introduced such that Boltzmann sampling of the isomorphic Hamiltonian with classical nuclear degrees of freedom yields the exact quantum Boltzmann distribution for the original physical system. In the limit of a single electronic energy level, the isomorphic Hamiltonian reduces to the familiar cases of either ring polymer molecular dynamics (RPMD) or centroid molecular dynamics Hamiltonians, depending on the implementation. An advantage of the isomorphic Hamiltonian is that it can easily be combined with existing mixed quantum-classical dynamics methods, such as surface hopping or Ehrenfest dynamics, to enable the simulation of electronically non-adiabatic processes with nuclear quantum effects. We present numerical applications of the isomorphic Hamiltonian to model two- and three-level systems, with encouraging results that include improvement upon a previously reported combination of RPMD with surface hopping in the deep-tunneling regime.

SEQUOIA: significance enhanced network querying through context-sensitive random walk and minimization of network conductance.

PubMed

Jeong, Hyundoo; Yoon, Byung-Jun

2017-03-14

Network querying algorithms provide computational means to identify conserved network modules in large-scale biological networks that are similar to known functional modules, such as pathways or molecular complexes. Two main challenges for network querying algorithms are the high computational complexity of detecting potential isomorphism between the query and the target graphs and ensuring the biological significance of the query results. In this paper, we propose SEQUOIA, a novel network querying algorithm that effectively addresses these issues by utilizing a context-sensitive random walk (CSRW) model for network comparison and minimizing the network conductance of potential matches in the target network. The CSRW model, inspired by the pair hidden Markov model (pair-HMM) that has been widely used for sequence comparison and alignment, can accurately assess the node-to-node correspondence between different graphs by accounting for node insertions and deletions. The proposed algorithm identifies high-scoring network regions based on the CSRW scores, which are subsequently extended by maximally reducing the network conductance of the identified subnetworks. Performance assessment based on real PPI networks and known molecular complexes show that SEQUOIA outperforms existing methods and clearly enhances the biological significance of the query results. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/SEQUOIA .

Isomorphic pressures, institutional strategies, and knowledge creation in the health care sector.

PubMed

Yang, Chen-Wei; Fang, Shih-Chieh; Huang, Wei-Min

2007-01-01

Health care organizations are facing surprisingly complex challenges, including new treatment and diagnostic technologies, ongoing pressures for health care institutional reform, the emergence of new organizational governance structures, and knowledge creation for the health care system. To maintain legitimacy in demanding environments, organizations tend to copy practices of similar organizations, which lead to isomorphism, and to use internal strategies to accommodate changes. A concern is that a poor fit between isomorphic pressures and internal strategies can interfere with developmental processes, such as knowledge creation. The purposes of this article are to, first, develop a set of propositions, based on institutional theory, as a theoretical framework that might explain the influence of isomorphic pressures on institutional processes through which knowledge is created within the health care sector and, second, propose that a good fit between isomorphic pressures factors and health care organizations' institutional strategic choices will enhance the health care organizations' ability to create knowledge. To develop a theoretical framework, we developed a set of propositions based on literature pertaining to the institutional theory perspective of isomorphic pressures and the response of health care organizations to isomorphic pressures. Institutional theory perspectives of isomorphic pressures and institutional strategies may provide a new understanding for health care organizations seeking effective knowledge creation strategies within institutional environment of health care sector. First, the ability to identify three forces for isomorphic change is critical for managers. Second, the importance of a contingency approach by health care managers can lead to strategies tailoring to cope with uncertainties facing their organizations.

SDIA: A dynamic situation driven information fusion algorithm for cloud environment

NASA Astrophysics Data System (ADS)

Guo, Shuhang; Wang, Tong; Wang, Jian

2017-09-01

Information fusion is an important issue in information integration domain. In order to form an extensive information fusion technology under the complex and diverse situations, a new information fusion algorithm is proposed. Firstly, a fuzzy evaluation model of tag utility was proposed that can be used to count the tag entropy. Secondly, a ubiquitous situation tag tree model is proposed to define multidimensional structure of information situation. Thirdly, the similarity matching between the situation models is classified into three types: the tree inclusion, the tree embedding, and the tree compatibility. Next, in order to reduce the time complexity of the tree compatible matching algorithm, a fast and ordered tree matching algorithm is proposed based on the node entropy, which is used to support the information fusion by ubiquitous situation. Since the algorithm revolve from the graph theory of disordered tree matching algorithm, it can improve the information fusion present recall rate and precision rate in the situation. The information fusion algorithm is compared with the star and the random tree matching algorithm, and the difference between the three algorithms is analyzed in the view of isomorphism, which proves the innovation and applicability of the algorithm.

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.

Graph Theory and Ion and Molecular Aggregation in Aqueous Solutions.

PubMed

Choi, Jun-Ho; Lee, Hochan; Choi, Hyung Ran; Cho, Minhaeng

2018-04-20

In molecular and cellular biology, dissolved ions and molecules have decisive effects on chemical and biological reactions, conformational stabilities, and functions of small to large biomolecules. Despite major efforts, the current state of understanding of the effects of specific ions, osmolytes, and bioprotecting sugars on the structure and dynamics of water H-bonding networks and proteins is not yet satisfactory. Recently, to gain deeper insight into this subject, we studied various aggregation processes of ions and molecules in high-concentration salt, osmolyte, and sugar solutions with time-resolved vibrational spectroscopy and molecular dynamics simulation methods. It turns out that ions (or solute molecules) have a strong propensity to self-assemble into large and polydisperse aggregates that affect both local and long-range water H-bonding structures. In particular, we have shown that graph-theoretical approaches can be used to elucidate morphological characteristics of large aggregates in various aqueous salt, osmolyte, and sugar solutions. When ion and molecular aggregates in such aqueous solutions are treated as graphs, a variety of graph-theoretical properties, such as graph spectrum, degree distribution, clustering coefficient, minimum path length, and graph entropy, can be directly calculated by considering an ensemble of configurations taken from molecular dynamics trajectories. Here we show percolating behavior exhibited by ion and molecular aggregates upon increase in solute concentration in high solute concentrations and discuss compelling evidence of the isomorphic relation between percolation transitions of ion and molecular aggregates and water H-bonding networks. We anticipate that the combination of graph theory and molecular dynamics simulation methods will be of exceptional use in achieving a deeper understanding of the fundamental physical chemistry of dissolution and in describing the interplay between the self-aggregation of solute molecules and the structure and dynamics of water.

Graph Theory and Ion and Molecular Aggregation in Aqueous Solutions

NASA Astrophysics Data System (ADS)

Choi, Jun-Ho; Lee, Hochan; Choi, Hyung Ran; Cho, Minhaeng

2018-04-01

In molecular and cellular biology, dissolved ions and molecules have decisive effects on chemical and biological reactions, conformational stabilities, and functions of small to large biomolecules. Despite major efforts, the current state of understanding of the effects of specific ions, osmolytes, and bioprotecting sugars on the structure and dynamics of water H-bonding networks and proteins is not yet satisfactory. Recently, to gain deeper insight into this subject, we studied various aggregation processes of ions and molecules in high-concentration salt, osmolyte, and sugar solutions with time-resolved vibrational spectroscopy and molecular dynamics simulation methods. It turns out that ions (or solute molecules) have a strong propensity to self-assemble into large and polydisperse aggregates that affect both local and long-range water H-bonding structures. In particular, we have shown that graph-theoretical approaches can be used to elucidate morphological characteristics of large aggregates in various aqueous salt, osmolyte, and sugar solutions. When ion and molecular aggregates in such aqueous solutions are treated as graphs, a variety of graph-theoretical properties, such as graph spectrum, degree distribution, clustering coefficient, minimum path length, and graph entropy, can be directly calculated by considering an ensemble of configurations taken from molecular dynamics trajectories. Here we show percolating behavior exhibited by ion and molecular aggregates upon increase in solute concentration in high solute concentrations and discuss compelling evidence of the isomorphic relation between percolation transitions of ion and molecular aggregates and water H-bonding networks. We anticipate that the combination of graph theory and molecular dynamics simulation methods will be of exceptional use in achieving a deeper understanding of the fundamental physical chemistry of dissolution and in describing the interplay between the self-aggregation of solute molecules and the structure and dynamics of water.

Text categorization of biomedical data sets using graph kernels and a controlled vocabulary.

PubMed

Bleik, Said; Mishra, Meenakshi; Huan, Jun; Song, Min

2013-01-01

Recently, graph representations of text have been showing improved performance over conventional bag-of-words representations in text categorization applications. In this paper, we present a graph-based representation for biomedical articles and use graph kernels to classify those articles into high-level categories. In our representation, common biomedical concepts and semantic relationships are identified with the help of an existing ontology and are used to build a rich graph structure that provides a consistent feature set and preserves additional semantic information that could improve a classifier's performance. We attempt to classify the graphs using both a set-based graph kernel that is capable of dealing with the disconnected nature of the graphs and a simple linear kernel. Finally, we report the results comparing the classification performance of the kernel classifiers to common text-based classifiers.

Topological Isomorphisms of Human Brain and Financial Market Networks

PubMed Central

Vértes, Petra E.; Nicol, Ruth M.; Chapman, Sandra C.; Watkins, Nicholas W.; Robertson, Duncan A.; Bullmore, Edward T.

2011-01-01

Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets – the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties. Both the human brain and the market networks were non-random, small-world, modular, hierarchical systems with fat-tailed degree distributions indicating the presence of highly connected hubs. These properties could not be trivially explained by the univariate time series statistics of stock price returns. This degree of topological isomorphism suggests that brains and markets can be regarded broadly as members of the same family of networks. The two systems, however, were not topologically identical. The financial market was more efficient and more modular – more highly optimized for information processing – than the brain networks; but also less robust to systemic disintegration as a result of hub deletion. We conclude that the conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. There will be interesting scientific arbitrage opportunities in further work at the graph-theoretically mediated interface between systems neuroscience and the statistical physics of financial markets. PMID:22007161

Mapping Systemic Risk: Critical Degree and Failures Distribution in Financial Networks.

PubMed

Smerlak, Matteo; Stoll, Brady; Gupta, Agam; Magdanz, James S

2015-01-01

The financial crisis illustrated the need for a functional understanding of systemic risk in strongly interconnected financial structures. Dynamic processes on complex networks being intrinsically difficult to model analytically, most recent studies of this problem have relied on numerical simulations. Here we report analytical results in a network model of interbank lending based on directly relevant financial parameters, such as interest rates and leverage ratios. We obtain a closed-form formula for the "critical degree" (the number of creditors per bank below which an individual shock can propagate throughout the network), and relate failures distributions to network topologies, in particular scalefree ones. Our criterion for the onset of contagion turns out to be isomorphic to the condition for cooperation to evolve on graphs and social networks, as recently formulated in evolutionary game theory. This remarkable connection supports recent calls for a methodological rapprochement between finance and ecology.

Mapping Systemic Risk: Critical Degree and Failures Distribution in Financial Networks

PubMed Central

Smerlak, Matteo; Stoll, Brady; Gupta, Agam; Magdanz, James S.

2015-01-01

The financial crisis illustrated the need for a functional understanding of systemic risk in strongly interconnected financial structures. Dynamic processes on complex networks being intrinsically difficult to model analytically, most recent studies of this problem have relied on numerical simulations. Here we report analytical results in a network model of interbank lending based on directly relevant financial parameters, such as interest rates and leverage ratios. We obtain a closed-form formula for the “critical degree” (the number of creditors per bank below which an individual shock can propagate throughout the network), and relate failures distributions to network topologies, in particular scalefree ones. Our criterion for the onset of contagion turns out to be isomorphic to the condition for cooperation to evolve on graphs and social networks, as recently formulated in evolutionary game theory. This remarkable connection supports recent calls for a methodological rapprochement between finance and ecology. PMID:26207631

A VLSI decomposition of the deBruijn graph

NASA Technical Reports Server (NTRS)

Collins, O.; Dolinar, S.; Mceliece, R.; Pollara, F.

1990-01-01

A new Viterbi decoder for convolutional codes with constraint lengths up to 15, called the Big Viterbi Decoder, is under development for the Deep Space Network. It will be demonstrated by decoding data from the Galileo spacecraft, which has a rate 1/4, constraint-length 15 convolutional encoder on board. Here, the mathematical theory underlying the design of the very-large-scale-integrated (VLSI) chips that are being used to build this decoder is explained. The deBruijn graph B sub n describes the topology of a fully parallel, rate 1/v, constraint length n+2 Viterbi decoder, and it is shown that B sub n can be built by appropriately wiring together (i.e., connecting together with extra edges) many isomorphic copies of a fixed graph called a B sub n building block. The efficiency of such a building block is defined as the fraction of the edges in B sub n that are present in the copies of the building block. It is shown, among other things, that for any alpha less than 1, there exists a graph G which is a B sub n building block of efficiency greater than alpha for all sufficiently large n. These results are illustrated by describing a special hierarchical family of deBruijn building blocks, which has led to the design of the gate-array chips being used in the Big Viterbi Decoder.

Matching CCD images to a stellar catalog using locality-sensitive hashing

NASA Astrophysics Data System (ADS)

Liu, Bo; Yu, Jia-Zong; Peng, Qing-Yu

2018-02-01

The usage of a subset of observed stars in a CCD image to find their corresponding matched stars in a stellar catalog is an important issue in astronomical research. Subgraph isomorphic-based algorithms are the most widely used methods in star catalog matching. When more subgraph features are provided, the CCD images are recognized better. However, when the navigation feature database is large, the method requires more time to match the observing model. To solve this problem, this study investigates further and improves subgraph isomorphic matching algorithms. We present an algorithm based on a locality-sensitive hashing technique, which allocates quadrilateral models in the navigation feature database into different hash buckets and reduces the search range to the bucket in which the observed quadrilateral model is located. Experimental results indicate the effectivity of our method.

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…

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.

A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous Information.

PubMed

Wei, Wei; Gao, Bin; Liu, Tie-Yan; Wang, Taifeng; Li, Guohui; Li, Hang

2016-04-01

Graph-based ranking has been extensively studied and frequently applied in many applications, such as webpage ranking. It aims at mining potentially valuable information from the raw graph-structured data. Recently, with the proliferation of rich heterogeneous information (e.g., node/edge features and prior knowledge) available in many real-world graphs, how to effectively and efficiently leverage all information to improve the ranking performance becomes a new challenging problem. Previous methods only utilize part of such information and attempt to rank graph nodes according to link-based methods, of which the ranking performances are severely affected by several well-known issues, e.g., over-fitting or high computational complexity, especially when the scale of graph is very large. In this paper, we address the large-scale graph-based ranking problem and focus on how to effectively exploit rich heterogeneous information of the graph to improve the ranking performance. Specifically, we propose an innovative and effective semi-supervised PageRank (SSP) approach to parameterize the derived information within a unified semi-supervised learning framework (SSLF-GR), then simultaneously optimize the parameters and the ranking scores of graph nodes. Experiments on the real-world large-scale graphs demonstrate that our method significantly outperforms the algorithms that consider such graph information only partially.

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.

New methods for analyzing semantic graph based assessments in science education

NASA Astrophysics Data System (ADS)

Vikaros, Lance Steven

This research investigated how the scoring of semantic graphs (known by many as concept maps) could be improved and automated in order to address issues of inter-rater reliability and scalability. As part of the NSF funded SENSE-IT project to introduce secondary school science students to sensor networks (NSF Grant No. 0833440), semantic graphs illustrating how temperature change affects water ecology were collected from 221 students across 16 schools. The graphing task did not constrain students' use of terms, as is often done with semantic graph based assessment due to coding and scoring concerns. The graphing software used provided real-time feedback to help students learn how to construct graphs, stay on topic and effectively communicate ideas. The collected graphs were scored by human raters using assessment methods expected to boost reliability, which included adaptations of traditional holistic and propositional scoring methods, use of expert raters, topical rubrics, and criterion graphs. High levels of inter-rater reliability were achieved, demonstrating that vocabulary constraints may not be necessary after all. To investigate a new approach to automating the scoring of graphs, thirty-two different graph features characterizing graphs' structure, semantics, configuration and process of construction were then used to predict human raters' scoring of graphs in order to identify feature patterns correlated to raters' evaluations of graphs' topical accuracy and complexity. Results led to the development of a regression model able to predict raters' scoring with 77% accuracy, with 46% accuracy expected when used to score new sets of graphs, as estimated via cross-validation tests. Although such performance is comparable to other graph and essay based scoring systems, cross-context testing of the model and methods used to develop it would be needed before it could be recommended for widespread use. Still, the findings suggest techniques for improving the reliability and scalability of semantic graph based assessments without requiring constraint of how ideas are expressed.

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

Mutual proximity graphs for improved reachability in music recommendation.

PubMed

Flexer, Arthur; Stevens, Jeff

2018-01-01

This paper is concerned with the impact of hubness, a general problem of machine learning in high-dimensional spaces, on a real-world music recommendation system based on visualisation of a k-nearest neighbour (knn) graph. Due to a problem of measuring distances in high dimensions, hub objects are recommended over and over again while anti-hubs are nonexistent in recommendation lists, resulting in poor reachability of the music catalogue. We present mutual proximity graphs, which are an alternative to knn and mutual knn graphs, and are able to avoid hub vertices having abnormally high connectivity. We show that mutual proximity graphs yield much better graph connectivity resulting in improved reachability compared to knn graphs, mutual knn graphs and mutual knn graphs enhanced with minimum spanning trees, while simultaneously reducing the negative effects of hubness.

Mutual proximity graphs for improved reachability in music recommendation

PubMed Central

Flexer, Arthur; Stevens, Jeff

2018-01-01

This paper is concerned with the impact of hubness, a general problem of machine learning in high-dimensional spaces, on a real-world music recommendation system based on visualisation of a k-nearest neighbour (knn) graph. Due to a problem of measuring distances in high dimensions, hub objects are recommended over and over again while anti-hubs are nonexistent in recommendation lists, resulting in poor reachability of the music catalogue. We present mutual proximity graphs, which are an alternative to knn and mutual knn graphs, and are able to avoid hub vertices having abnormally high connectivity. We show that mutual proximity graphs yield much better graph connectivity resulting in improved reachability compared to knn graphs, mutual knn graphs and mutual knn graphs enhanced with minimum spanning trees, while simultaneously reducing the negative effects of hubness. PMID:29348779

An impatient evolutionary algorithm with probabilistic tabu search for unified solution of some NP-hard problems in graph and set theory via clique finding.

PubMed

Guturu, Parthasarathy; Dantu, Ram

2008-06-01

Many graph- and set-theoretic problems, because of their tremendous application potential and theoretical appeal, have been well investigated by the researchers in complexity theory and were found to be NP-hard. Since the combinatorial complexity of these problems does not permit exhaustive searches for optimal solutions, only near-optimal solutions can be explored using either various problem-specific heuristic strategies or metaheuristic global-optimization methods, such as simulated annealing, genetic algorithms, etc. In this paper, we propose a unified evolutionary algorithm (EA) to the problems of maximum clique finding, maximum independent set, minimum vertex cover, subgraph and double subgraph isomorphism, set packing, set partitioning, and set cover. In the proposed approach, we first map these problems onto the maximum clique-finding problem (MCP), which is later solved using an evolutionary strategy. The proposed impatient EA with probabilistic tabu search (IEA-PTS) for the MCP integrates the best features of earlier successful approaches with a number of new heuristics that we developed to yield a performance that advances the state of the art in EAs for the exploration of the maximum cliques in a graph. Results of experimentation with the 37 DIMACS benchmark graphs and comparative analyses with six state-of-the-art algorithms, including two from the smaller EA community and four from the larger metaheuristics community, indicate that the IEA-PTS outperforms the EAs with respect to a Pareto-lexicographic ranking criterion and offers competitive performance on some graph instances when individually compared to the other heuristic algorithms. It has also successfully set a new benchmark on one graph instance. On another benchmark suite called Benchmarks with Hidden Optimal Solutions, IEA-PTS ranks second, after a very recent algorithm called COVER, among its peers that have experimented with this suite.

Dim target detection method based on salient graph fusion

NASA Astrophysics Data System (ADS)

Hu, Ruo-lan; Shen, Yi-yan; Jiang, Jun

2018-02-01

Dim target detection is one key problem in digital image processing field. With development of multi-spectrum imaging sensor, it becomes a trend to improve the performance of dim target detection by fusing the information from different spectral images. In this paper, one dim target detection method based on salient graph fusion was proposed. In the method, Gabor filter with multi-direction and contrast filter with multi-scale were combined to construct salient graph from digital image. And then, the maximum salience fusion strategy was designed to fuse the salient graph from different spectral images. Top-hat filter was used to detect dim target from the fusion salient graph. Experimental results show that proposal method improved the probability of target detection and reduced the probability of false alarm on clutter background images.

Modification of the crystal habit of celecoxib for improved processability.

PubMed

Banga, Sheere; Chawla, Garima; Varandani, Deepak; Mehta, B R; Bansal, Arvind K

2007-01-01

Crystallization is often used in the pharmaceutical industry for purification and isolation of drugs, and also as a means of generating polymorphs or isomorphs. The aim of this study was to investigate the role of extrinsic crystallization parameters on the crystallized product, with special emphasis on improving the mechanical properties of acicular celecoxib. Celecoxib isomorphs were prepared using different techniques (solvent crystallization and vapour diffusion) and crystallization conditions (solvents, stirring, degree of supersaturation, crystallization temperature and seeding). Powder X-ray diffractometry, spectroscopic and thermal methods were used to investigate physical characteristics of crystals. Growth kinetics and aggregation dynamics of crystallization in polar and non-polar solvents were simulated using a dynamic light scattering method. The quick appearance of broad peaks over the range of 10-8000 nm in chloroform during crystallization simulation studies indicated faster aggregation in non-polar solvents. Aspect ratio, flow, compressibility and surface area of recrystallized products were also determined. Surface topography was determined by atomic force microscopy and the lath-shaped crystals (aspect ratio of 2-4) exhibited a roughness index of 1.79 in comparison with 2.92 for needles. Overall, the lath-shaped isomorphs exhibited improved flow and better compressibility.

L-GRAAL: Lagrangian graphlet-based network aligner.

PubMed

Malod-Dognin, Noël; Pržulj, Nataša

2015-07-01

Discovering and understanding patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. A few methods have been proposed for global PPI network alignments, but because of NP-completeness of underlying sub-graph isomorphism problem, producing topologically and biologically accurate alignments remains a challenge. We introduce a novel global network alignment tool, Lagrangian GRAphlet-based ALigner (L-GRAAL), which directly optimizes both the protein and the interaction functional conservations, using a novel alignment search heuristic based on integer programming and Lagrangian relaxation. We compare L-GRAAL with the state-of-the-art network aligners on the largest available PPI networks from BioGRID and observe that L-GRAAL uncovers the largest common sub-graphs between the networks, as measured by edge-correctness and symmetric sub-structures scores, which allow transferring more functional information across networks. We assess the biological quality of the protein mappings using the semantic similarity of their Gene Ontology annotations and observe that L-GRAAL best uncovers functionally conserved proteins. Furthermore, we introduce for the first time a measure of the semantic similarity of the mapped interactions and show that L-GRAAL also uncovers best functionally conserved interactions. In addition, we illustrate on the PPI networks of baker's yeast and human the ability of L-GRAAL to predict new PPIs. Finally, L-GRAAL's results are the first to show that topological information is more important than sequence information for uncovering functionally conserved interactions. L-GRAAL is coded in C++. Software is available at: http://bio-nets.doc.ic.ac.uk/L-GRAAL/. n.malod-dognin@imperial.ac.uk Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

NASA Astrophysics Data System (ADS)

Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

2014-12-01

Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

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

Force, Velocity, and Work: The Effects of Different Contexts on Students' Understanding of Vector Concepts Using Isomorphic Problems

ERIC Educational Resources Information Center

Barniol, Pablo; Zavala, Genaro

2014-01-01

In this article we compare students' understanding of vector concepts in problems with no physical context, and with three mechanics contexts: force, velocity, and work. Based on our "Test of Understanding of Vectors," a multiple-choice test presented elsewhere, we designed two isomorphic shorter versions of 12 items each: a test with no…

Edge connectivity and the spectral gap of combinatorial and quantum graphs

NASA Astrophysics Data System (ADS)

Berkolaiko, Gregory; Kennedy, James B.; Kurasov, Pavel; Mugnolo, Delio

2017-09-01

We derive a number of upper and lower bounds for the first nontrivial eigenvalue of Laplacians on combinatorial and quantum graph in terms of the edge connectivity, i.e. the minimal number of edges which need to be removed to make the graph disconnected. On combinatorial graphs, one of the bounds corresponds to a well-known inequality of Fiedler, of which we give a new variational proof. On quantum graphs, the corresponding bound generalizes a recent result of Band and Lévy. All proofs are general enough to yield corresponding estimates for the p-Laplacian and allow us to identify the minimizers. Based on the Betti number of the graph, we also derive upper and lower bounds on all eigenvalues which are ‘asymptotically correct’, i.e. agree with the Weyl asymptotics for the eigenvalues of the quantum graph. In particular, the lower bounds improve the bounds of Friedlander on any given graph for all but finitely many eigenvalues, while the upper bounds improve recent results of Ariturk. Our estimates are also used to derive bounds on the eigenvalues of the normalized Laplacian matrix that improve known bounds of spectral graph theory.

Streaming data analytics via message passing with application to graph algorithms

DOE PAGES

Plimpton, Steven J.; Shead, Tim

2014-05-06

The need to process streaming data, which arrives continuously at high-volume in real-time, arises in a variety of contexts including data produced by experiments, collections of environmental or network sensors, and running simulations. Streaming data can also be formulated as queries or transactions which operate on a large dynamic data store, e.g. a distributed database. We describe a lightweight, portable framework named PHISH which enables a set of independent processes to compute on a stream of data in a distributed-memory parallel manner. Datums are routed between processes in patterns defined by the application. PHISH can run on top of eithermore » message-passing via MPI or sockets via ZMQ. The former means streaming computations can be run on any parallel machine which supports MPI; the latter allows them to run on a heterogeneous, geographically dispersed network of machines. We illustrate how PHISH can support streaming MapReduce operations, and describe streaming versions of three algorithms for large, sparse graph analytics: triangle enumeration, subgraph isomorphism matching, and connected component finding. Lastly, we also provide benchmark timings for MPI versus socket performance of several kernel operations useful in streaming algorithms.« less

Improved segmentation of abnormal cervical nuclei using a graph-search based approach

NASA Astrophysics Data System (ADS)

Zhang, Ling; Liu, Shaoxiong; Wang, Tianfu; Chen, Siping; Sonka, Milan

2015-03-01

Reliable segmentation of abnormal nuclei in cervical cytology is of paramount importance in automation-assisted screening techniques. This paper presents a general method for improving the segmentation of abnormal nuclei using a graph-search based approach. More specifically, the proposed method focuses on the improvement of coarse (initial) segmentation. The improvement relies on a transform that maps round-like border in the Cartesian coordinate system into lines in the polar coordinate system. The costs consisting of nucleus-specific edge and region information are assigned to the nodes. The globally optimal path in the constructed graph is then identified by dynamic programming. We have tested the proposed method on abnormal nuclei from two cervical cell image datasets, Herlev and H and E stained liquid-based cytology (HELBC), and the comparative experiments with recent state-of-the-art approaches demonstrate the superior performance of the proposed method.

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.

The Importance of Isomorphism for Conclusions about Homology: A Bayesian Multilevel Structural Equation Modeling Approach with Ordinal Indicators.

PubMed

Guenole, Nigel

2016-01-01

We describe a Monte Carlo study examining the impact of assuming item isomorphism (i.e., equivalent construct meaning across levels of analysis) on conclusions about homology (i.e., equivalent structural relations across levels of analysis) under varying degrees of non-isomorphism in the context of ordinal indicator multilevel structural equation models (MSEMs). We focus on the condition where one or more loadings are higher on the between level than on the within level to show that while much past research on homology has ignored the issue of psychometric isomorphism, psychometric isomorphism is in fact critical to valid conclusions about homology. More specifically, when a measurement model with non-isomorphic items occupies an exogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the within level exogenous latent variance is under-estimated leading to over-estimation of the within level structural coefficient, while the between level exogenous latent variance is overestimated leading to underestimation of the between structural coefficient. When a measurement model with non-isomorphic items occupies an endogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the endogenous within level latent variance is under-estimated leading to under-estimation of the within level structural coefficient while the endogenous between level latent variance is over-estimated leading to over-estimation of the between level structural coefficient. The innovative aspect of this article is demonstrating that even minor violations of psychometric isomorphism render claims of homology untenable. We also show that posterior predictive p-values for ordinal indicator Bayesian MSEMs are insensitive to violations of isomorphism even when they lead to severely biased within and between level structural parameters. We highlight conditions where poor estimation of even correctly specified models rules out empirical examination of isomorphism and homology without taking precautions, for instance, larger Level-2 sample sizes, or using informative priors.

The Importance of Isomorphism for Conclusions about Homology: A Bayesian Multilevel Structural Equation Modeling Approach with Ordinal Indicators

PubMed Central

Guenole, Nigel

2016-01-01

We describe a Monte Carlo study examining the impact of assuming item isomorphism (i.e., equivalent construct meaning across levels of analysis) on conclusions about homology (i.e., equivalent structural relations across levels of analysis) under varying degrees of non-isomorphism in the context of ordinal indicator multilevel structural equation models (MSEMs). We focus on the condition where one or more loadings are higher on the between level than on the within level to show that while much past research on homology has ignored the issue of psychometric isomorphism, psychometric isomorphism is in fact critical to valid conclusions about homology. More specifically, when a measurement model with non-isomorphic items occupies an exogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the within level exogenous latent variance is under-estimated leading to over-estimation of the within level structural coefficient, while the between level exogenous latent variance is overestimated leading to underestimation of the between structural coefficient. When a measurement model with non-isomorphic items occupies an endogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the endogenous within level latent variance is under-estimated leading to under-estimation of the within level structural coefficient while the endogenous between level latent variance is over-estimated leading to over-estimation of the between level structural coefficient. The innovative aspect of this article is demonstrating that even minor violations of psychometric isomorphism render claims of homology untenable. We also show that posterior predictive p-values for ordinal indicator Bayesian MSEMs are insensitive to violations of isomorphism even when they lead to severely biased within and between level structural parameters. We highlight conditions where poor estimation of even correctly specified models rules out empirical examination of isomorphism and homology without taking precautions, for instance, larger Level-2 sample sizes, or using informative priors. PMID:26973580

Using isomorphic problems to learn introductory physics

NASA Astrophysics Data System (ADS)

Lin, Shih-Yin; Singh, Chandralekha

2011-12-01

In this study, we examine introductory physics students’ ability to perform analogical reasoning between two isomorphic problems which employ the same underlying physics principles but have different surface features. Three hundred sixty-two students from a calculus-based and an algebra-based introductory physics course were given a quiz in the recitation in which they had to first learn from a solved problem provided and take advantage of what they learned from it to solve another problem (which we call the quiz problem) which was isomorphic. Previous research suggests that the multiple-concept quiz problem is challenging for introductory students. Students in different recitation classes received different interventions in order to help them discern and exploit the underlying similarities of the isomorphic solved and quiz problems. We also conducted think-aloud interviews with four introductory students in order to understand in depth the difficulties they had and explore strategies to provide better scaffolding. We found that most students were able to learn from the solved problem to some extent with the scaffolding provided and invoke the relevant principles in the quiz problem. However, they were not necessarily able to apply the principles correctly. Research suggests that more scaffolding is needed to help students in applying these principles appropriately. We outline a few possible strategies for future investigation.

Time-reversal-based SU(2) x Sn scalar invariants as (Lie Algebraic) group measures: a structured overview of generalised democratic-recoupled, uniform non-Abelian [AX]n NMR spin systems, as abstract [Formula: see text] chain networks.

PubMed

Temme, F P

2004-03-01

The physics of dual group scalar invariants (SIs) as (Lie algebraic) group measures (L-GMs) and its significance to non-Abelian NMR spin systems motivates this overview of uniform general-2n [AX](2n) spin evolution, which represents an extensive addendum to Corio's earlier (essentially restricted) view of Abelian spin system SU(2)-based SI-cardinalities. The [Formula: see text] values in [J. Magn. Reson., 134 (1998) 131] arise from strictly linear recoupled time-reversal invariance (TRI) models. In contrast, here we discuss the physical significance of an alternative polyhedral combinatorics approach to democratic recoupling (DR), a property inherent in both the TRI and statistical sampling. Recognition of spin ensemble SIs as being L-GMs over isomorphic algebras is invaluable in many DR-based NMR problems. Various [AX]n model spin systems, including the [AX]3 bis odd-odd parity spin system, are examined as direct applications of these L-GM- and combinatorial-based SI ideas. Hence in place of /SI/=15 (implied by Corio's [Formula: see text] approach), the bis 3-fold spin system cardinality is seen now as constrained to a single invariant on an isomorphic product algebra under L-GMs, in accord with the subspectral analysis of Jones et al. [Canad. J. Chem., 43 (1965) 683]. The group projective ideas cited here for DR (as cf. to graph theoretic views) apply to highly degenerate non-Abelian problems. Over dual tensorial bases, they define models of spin dynamical evolution whose (SR) quasiparticle superboson carrier (sub)spaces are characterised by SIs acting as explicit auxiliary labels [Physica, A198 (1993) 245; J. Math. Chem., 31 (2002) 281]. A deeper [Formula: see text] network-based view of spin-alone space developed in Balasubramanian's work [J. Chem. Phys., 78 (1983) 6358] is especially important, (e.g.) in the study of spin waves [J. Math. Chem., 31 (2002) 363]. Beyond the specific NMR SIs derived here, there are DR applications where a sporadic, still higher, 2n-fold regular uniform spin ensemble exhibits a topological FG duality to some known modest /SI/(2i<2n) cardinality--in principle providing for the (sparce) existence of other /SI/(2n) DR-based values.

Enhancing Community Detection By Affinity-based Edge Weighting Scheme

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

Yoo, Andy; Sanders, Geoffrey; Henson, Van

Community detection refers to an important graph analytics problem of finding a set of densely-connected subgraphs in a graph and has gained a great deal of interest recently. The performance of current community detection algorithms is limited by an inherent constraint of unweighted graphs that offer very little information on their internal community structures. In this paper, we propose a new scheme to address this issue that weights the edges in a given graph based on recently proposed vertex affinity. The vertex affinity quantifies the proximity between two vertices in terms of their clustering strength, and therefore, it is idealmore » for graph analytics applications such as community detection. We also demonstrate that the affinity-based edge weighting scheme can improve the performance of community detection algorithms significantly.« less

Key-Node-Separated Graph Clustering and Layouts for Human Relationship Graph Visualization.

PubMed

Itoh, Takayuki; Klein, Karsten

2015-01-01

Many graph-drawing methods apply node-clustering techniques based on the density of edges to find tightly connected subgraphs and then hierarchically visualize the clustered graphs. However, users may want to focus on important nodes and their connections to groups of other nodes for some applications. For this purpose, it is effective to separately visualize the key nodes detected based on adjacency and attributes of the nodes. This article presents a graph visualization technique for attribute-embedded graphs that applies a graph-clustering algorithm that accounts for the combination of connections and attributes. The graph clustering step divides the nodes according to the commonality of connected nodes and similarity of feature value vectors. It then calculates the distances between arbitrary pairs of clusters according to the number of connecting edges and the similarity of feature value vectors and finally places the clusters based on the distances. Consequently, the technique separates important nodes that have connections to multiple large clusters and improves the visibility of such nodes' connections. To test this technique, this article presents examples with human relationship graph datasets, including a coauthorship and Twitter communication network dataset.

A graph-based approach for the retrieval of multi-modality medical images.

PubMed

Kumar, Ashnil; Kim, Jinman; Wen, Lingfeng; Fulham, Michael; Feng, Dagan

2014-02-01

In this paper, we address the retrieval of multi-modality medical volumes, which consist of two different imaging modalities, acquired sequentially, from the same scanner. One such example, positron emission tomography and computed tomography (PET-CT), provides physicians with complementary functional and anatomical features as well as spatial relationships and has led to improved cancer diagnosis, localisation, and staging. The challenge of multi-modality volume retrieval for cancer patients lies in representing the complementary geometric and topologic attributes between tumours and organs. These attributes and relationships, which are used for tumour staging and classification, can be formulated as a graph. It has been demonstrated that graph-based methods have high accuracy for retrieval by spatial similarity. However, naïvely representing all relationships on a complete graph obscures the structure of the tumour-anatomy relationships. We propose a new graph structure derived from complete graphs that structurally constrains the edges connected to tumour vertices based upon the spatial proximity of tumours and organs. This enables retrieval on the basis of tumour localisation. We also present a similarity matching algorithm that accounts for different feature sets for graph elements from different imaging modalities. Our method emphasises the relationships between a tumour and related organs, while still modelling patient-specific anatomical variations. Constraining tumours to related anatomical structures improves the discrimination potential of graphs, making it easier to retrieve similar images based on tumour location. We evaluated our retrieval methodology on a dataset of clinical PET-CT volumes. Our results showed that our method enabled the retrieval of multi-modality images using spatial features. Our graph-based retrieval algorithm achieved a higher precision than several other retrieval techniques: gray-level histograms as well as state-of-the-art methods such as visual words using the scale- invariant feature transform (SIFT) and relational matrices representing the spatial arrangements of objects. Copyright © 2013 Elsevier B.V. All rights reserved.

[A graph cuts-based interactive method for segmentation of magnetic resonance images of meningioma].

PubMed

Li, Shuan-qiang; Feng, Qian-jin; Chen, Wu-fan; Lin, Ya-zhong

2011-06-01

For accurate segmentation of the magnetic resonance (MR) images of meningioma, we propose a novel interactive segmentation method based on graph cuts. The high dimensional image features was extracted, and for each pixel, the probabilities of its origin, either the tumor or the background regions, were estimated by exploiting the weighted K-nearest neighborhood classifier. Based on these probabilities, a new energy function was proposed. Finally, a graph cut optimal framework was used for the solution of the energy function. The proposed method was evaluated by application in the segmentation of MR images of meningioma, and the results showed that the method significantly improved the segmentation accuracy compared with the gray level information-based graph cut method.

Graph Based Models for Unsupervised High Dimensional Data Clustering and Network Analysis

DTIC Science & Technology

2015-01-01

ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for...algorithms we proposed improve the time e ciency signi cantly for large scale datasets. In the last chapter, we also propose an incremental reseeding...plume detection in hyper-spectral video data. These graph based clustering algorithms we proposed improve the time efficiency significantly for large

BFV-Complex and Higher Homotopy Structures

NASA Astrophysics Data System (ADS)

Schätz, Florian

2009-03-01

We present a connection between the BFV-complex (abbreviation for Batalin-Fradkin-Vilkovisky complex) and the strong homotopy Lie algebroid associated to a coisotropic submanifold of a Poisson manifold. We prove that the latter structure can be derived from the BFV-complex by means of homotopy transfer along contractions. Consequently the BFV-complex and the strong homotopy Lie algebroid structure are L ∞ quasi-isomorphic and control the same formal deformation problem. However there is a gap between the non-formal information encoded in the BFV-complex and in the strong homotopy Lie algebroid respectively. We prove that there is a one-to-one correspondence between coisotropic submanifolds given by graphs of sections and equivalence classes of normalized Maurer-Cartan elemens of the BFV-complex. This does not hold if one uses the strong homotopy Lie algebroid instead.

A stochastic model for tumor geometry evolution during radiation therapy in cervical cancer

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

Liu, Yifang; Lee, Chi-Guhn; Chan, Timothy C. Y., E-mail: tcychan@mie.utoronto.ca

2014-02-15

Purpose: To develop mathematical models to predict the evolution of tumor geometry in cervical cancer undergoing radiation therapy. Methods: The authors develop two mathematical models to estimate tumor geometry change: a Markov model and an isomorphic shrinkage model. The Markov model describes tumor evolution by investigating the change in state (either tumor or nontumor) of voxels on the tumor surface. It assumes that the evolution follows a Markov process. Transition probabilities are obtained using maximum likelihood estimation and depend on the states of neighboring voxels. The isomorphic shrinkage model describes tumor shrinkage or growth in terms of layers of voxelsmore » on the tumor surface, instead of modeling individual voxels. The two proposed models were applied to data from 29 cervical cancer patients treated at Princess Margaret Cancer Centre and then compared to a constant volume approach. Model performance was measured using sensitivity and specificity. Results: The Markov model outperformed both the isomorphic shrinkage and constant volume models in terms of the trade-off between sensitivity (target coverage) and specificity (normal tissue sparing). Generally, the Markov model achieved a few percentage points in improvement in either sensitivity or specificity compared to the other models. The isomorphic shrinkage model was comparable to the Markov approach under certain parameter settings. Convex tumor shapes were easier to predict. Conclusions: By modeling tumor geometry change at the voxel level using a probabilistic model, improvements in target coverage and normal tissue sparing are possible. Our Markov model is flexible and has tunable parameters to adjust model performance to meet a range of criteria. Such a model may support the development of an adaptive paradigm for radiation therapy of cervical cancer.« less

Support Vector Machine Classification of Major Depressive Disorder Using Diffusion-Weighted Neuroimaging and Graph Theory

PubMed Central

Sacchet, Matthew D.; Prasad, Gautam; Foland-Ross, Lara C.; Thompson, Paul M.; Gotlib, Ian H.

2015-01-01

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on “support vector machines” to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities. PMID:25762941

Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.

PubMed

Sacchet, Matthew D; Prasad, Gautam; Foland-Ross, Lara C; Thompson, Paul M; Gotlib, Ian H

2015-01-01

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on "support vector machines" to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities.

The topology of metabolic isotope labeling networks.

PubMed

Weitzel, Michael; Wiechert, Wolfgang; Nöh, Katharina

2007-08-29

Metabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures. With a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs) and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of E. coli comprising 10390 isotopomer pools. Exploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the E. coli example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global topological analysis of ILNs allows to comprehensively describe and understand the general patterns of label flow in complex networks. This is an invaluable tool for the structural design of new experiments and the interpretation of measured data.

A Graph Based Backtracking Algorithm for Solving General CSPs

NASA Technical Reports Server (NTRS)

Pang, Wanlin; Goodwin, Scott D.

2003-01-01

Many AI tasks can be formalized as constraint satisfaction problems (CSPs), which involve finding values for variables subject to constraints. While solving a CSP is an NP-complete task in general, tractable classes of CSPs have been identified based on the structure of the underlying constraint graphs. Much effort has been spent on exploiting structural properties of the constraint graph to improve the efficiency of finding a solution. These efforts contributed to development of a class of CSP solving algorithms called decomposition algorithms. The strength of CSP decomposition is that its worst-case complexity depends on the structural properties of the constraint graph and is usually better than the worst-case complexity of search methods. Its practical application is limited, however, since it cannot be applied if the CSP is not decomposable. In this paper, we propose a graph based backtracking algorithm called omega-CDBT, which shares merits and overcomes the weaknesses of both decomposition and search approaches.

Improved visibility graph fractality with application for the diagnosis of Autism Spectrum Disorder

NASA Astrophysics Data System (ADS)

Ahmadlou, Mehran; Adeli, Hojjat; Adeli, Amir

2012-10-01

Recently, the visibility graph (VG) algorithm was proposed for mapping a time series to a graph to study complexity and fractality of the time series through investigation of the complexity of its graph. The visibility graph algorithm converts a fractal time series to a scale-free graph. VG has been used for the investigation of fractality in the dynamic behavior of both artificial and natural complex systems. However, robustness and performance of the power of scale-freeness of VG (PSVG) as an effective method for measuring fractality has not been investigated. Since noise is unavoidable in real life time series, the robustness of a fractality measure is of paramount importance. To improve the accuracy and robustness of PSVG to noise for measurement of fractality of time series in biological time-series, an improved PSVG is presented in this paper. The proposed method is evaluated using two examples: a synthetic benchmark time series and a complicated real life Electroencephalograms (EEG)-based diagnostic problem, that is distinguishing autistic children from non-autistic children. It is shown that the proposed improved PSVG is less sensitive to noise and therefore more robust compared with PSVG. Further, it is shown that using improved PSVG in the wavelet-chaos neural network model of Adeli and c-workers in place of the Katz fractality dimension results in a more accurate diagnosis of autism, a complicated neurological and psychiatric disorder.

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.

Multi-label literature classification based on the Gene Ontology graph.

PubMed

Jin, Bo; Muller, Brian; Zhai, Chengxiang; Lu, Xinghua

2008-12-08

The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification. In this research, we investigated the methods of enhancing automatic multi-label classification of biomedical literature by utilizing the structure of the Gene Ontology graph. We have studied three graph-based multi-label classification algorithms, including a novel stochastic algorithm and two top-down hierarchical classification methods for multi-label literature classification. We systematically evaluated and compared these graph-based classification algorithms to a conventional flat multi-label algorithm. The results indicate that, through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods can significantly improve predictions of the Gene Ontology terms implied by the analyzed text. Furthermore, the graph-based multi-label classifiers are capable of suggesting Gene Ontology annotations (to curators) that are closely related to the true annotations even if they fail to predict the true ones directly. A software package implementing the studied algorithms is available for the research community. Through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods have better potential than the conventional flat multi-label classification approach to facilitate protein annotation based on the literature.

A Visual Analytics Paradigm Enabling Trillion-Edge Graph Exploration

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

Wong, Pak C.; Haglin, David J.; Gillen, David S.

We present a visual analytics paradigm and a system prototype for exploring web-scale graphs. A web-scale graph is described as a graph with ~one trillion edges and ~50 billion vertices. While there is an aggressive R&D effort in processing and exploring web-scale graphs among internet vendors such as Facebook and Google, visualizing a graph of that scale still remains an underexplored R&D area. The paper describes a nontraditional peek-and-filter strategy that facilitates the exploration of a graph database of unprecedented size for visualization and analytics. We demonstrate that our system prototype can 1) preprocess a graph with ~25 billion edgesmore » in less than two hours and 2) support database query and visualization on the processed graph database afterward. Based on our computational performance results, we argue that we most likely will achieve the one trillion edge mark (a computational performance improvement of 40 times) for graph visual analytics in the near future.« less

High-Performance Data Analytics Beyond the Relational and Graph Data Models with GEMS

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

Castellana, Vito G.; Minutoli, Marco; Bhatt, Shreyansh

Graphs represent an increasingly popular data model for data-analytics, since they can naturally represent relationships and interactions between entities. Relational databases and their pure table-based data model are not well suitable to store and process sparse data. Consequently, graph databases have gained interest in the last few years and the Resource Description Framework (RDF) became the standard data model for graph data. Nevertheless, while RDF is well suited to analyze the relationships between the entities, it is not efficient in representing their attributes and properties. In this work we propose the adoption of a new hybrid data model, based onmore » attributed graphs, that aims at overcoming the limitations of the pure relational and graph data models. We present how we have re-designed the GEMS data-analytics framework to fully take advantage of the proposed hybrid data model. To improve analysts productivity, in addition to a C++ API for applications development, we adopt GraQL as input query language. We validate our approach implementing a set of queries on net-flow data and we compare our framework performance against Neo4j. Experimental results show significant performance improvement over Neo4j, up to several orders of magnitude when increasing the size of the input data.« less

Overlapping community detection based on link graph using distance dynamics

NASA Astrophysics Data System (ADS)

Chen, Lei; Zhang, Jing; Cai, Li-Jun

2018-01-01

The distance dynamics model was recently proposed to detect the disjoint community of a complex network. To identify the overlapping structure of a network using the distance dynamics model, an overlapping community detection algorithm, called L-Attractor, is proposed in this paper. The process of L-Attractor mainly consists of three phases. In the first phase, L-Attractor transforms the original graph to a link graph (a new edge graph) to assure that one node has multiple distances. In the second phase, using the improved distance dynamics model, a dynamic interaction process is introduced to simulate the distance dynamics (shrink or stretch). Through the dynamic interaction process, all distances converge, and the disjoint community structure of the link graph naturally manifests itself. In the third phase, a recovery method is designed to convert the disjoint community structure of the link graph to the overlapping community structure of the original graph. Extensive experiments are conducted on the LFR benchmark networks as well as real-world networks. Based on the results, our algorithm demonstrates higher accuracy and quality than other state-of-the-art algorithms.

Explaining the accreditation process from the institutional isomorphism perspective: a case study of Jordanian primary healthcare centers.

PubMed

Alyahya, Mohammad; Hijazi, Heba; Harvey, Heather

2018-01-01

While the main focus of accreditation initiatives has been on hospitals, the implementation of these programs is a relatively new notion among other types of healthcare facilities. Correspondingly, this study aims to understand how accreditation is perceived among primary public healthcare centers using an isomorphic institutional theory. Semi-structured, in-depth interviews were conducted with 56 healthcare professionals and administrative staff from seven non-profit healthcare centers in Jordan using an explanatory case-study approach. The informants' narratives revealed that all three components of institutional theory: coercive, mimetic, and normative pressure, were drivers for institutional change in seeking accreditation. There was an overlapping and blending between the three various types of pressure. While participants perceived that healthcare centers faced formal and informal pressures to achieve accreditation, health centers were reluctant about the time, amount of effort, and their ability to achieve the accreditation. Ambiguity and fear of failure forced them to model successful ones. Moreover, the findings revealed that normative values of health professionals enhanced institutional isomorphism and influenced the accreditation process. Identifying these isomorphic changes may help key stakeholders to develop plans, policies, and procedures that could improve the quality of healthcare and enhance accreditation as an organizational strategic plan. Moreover, the study provided explanations of why and how organizations move to adopt new interventions and grow over time. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Heuristics of Twelfth Graders Building Isomorphisms

ERIC Educational Resources Information Center

Powell, Arthur B.; Maher, Carolyn A.

2003-01-01

This report analyzes the discursive interactions of four students to understand what heuristic methods they develop as well as how and why they build isomorphisms to resolve a combinatorial problem set in a non-Euclidian context. The findings suggest that results of their heuristic actions lead them to build isomorphisms that in turn allow them to…

Time-reversal-based SU(2)× Sn scalar invariants as (Lie Algebraic) group measures: a structured overview of generalised democratic-recoupled, uniform non-Abelian [ AX] n NMR spin systems, as abstract Sn⊃ Sn-1../U n⊃U n-1.. chain networks

NASA Astrophysics Data System (ADS)

Temme, F. P.

2004-03-01

The physics of dual group scalar invariants (SIs) as (Lie algebraic) group measures (L-GMs) and its significance to non-Abelian NMR spin systems motivates this overview of uniform general-2 n [ AX] 2 n spin evolution, which represents an extensive addendum to Corio's earlier (essentially restricted) view of Abelian spin system SU(2)-based SI-cardinalities. The |D 0( U)|((⊗SU(2)) (2n))|SI| values in [J. Magn. Reson., 134 (1998) 131] arise from strictly linear recoupled time-reversal invariance (TRI) models. In contrast, here we discuss the physical significance of an alternative polyhedral combinatorics approach to democratic recoupling (DR), a property inherent in both the TRI and statistical sampling. Recognition of spin ensemble SIs as being L-GMs over isomorphic algebras is invaluable in many DR-based NMR problems. Various [ AX] n model spin systems, including the [ AX] 3bis odd-odd parity spin system, are examined as direct applications of these L-GM- and combinatorial-based SI ideas. Hence in place of | SI|=15 (implied by Corio's | D0|((⊗ SU(2)) 2 n) approach), the bis 3-fold spin system cardinality is seen now as constrained to a single invariant on an isomorphic product algebra under L-GMs, in accord with the subspectral analysis of Jones et al. [Canad. J. Chem., 43 (1965) 683]. The group projective ideas cited here for DR (as cf. to graph theoretic views) apply to highly degenerate non-Abelian problems. Over dual tensorial bases, they define models of spin dynamical evolution whose (SR) quasiparticle superboson carrier (sub)spaces are characterised by SIs acting as explicit auxiliary labels [Physica, A198 (1993) 245; J. Math. Chem., 31 (2002) 281]. A deeper S2n network-based view of spin-alone space developed in Balasubramanian's work [J. Chem. Phys., 78 (1983) 6358] is especially important, (e.g.) in the study of spin waves [J. Math. Chem., 31 (2002) 363]. Beyond the specific NMR SIs derived here, there are DR applications where a sporadic, still higher, 2 n-fold regular uniform spin ensemble exhibits a topological FG duality to some known modest | SI| (2 i<2 n) cardinality—in principle providing for the (sparce) existence of other | SI| (2 n) DR-based values.

Institutional Isomorphism and the Creation of the Unified National System of Higher Education in Australia: An Empirical Analysis

ERIC Educational Resources Information Center

Croucher, Gwilym; Woelert, Peter

2016-01-01

Previous research has highlighted the occurrence of isomorphic tendencies--convergences in terms of formal organizational structure--in higher education systems in times of uncertainty and under external pressure to change. It has been repeatedly claimed that the Australian university system largely followed a logic of isomorphic change in the…

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

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

Automatic classification of protein structures relying on similarities between alignments

PubMed Central

2012-01-01

Background Identification of protein structural cores requires isolation of sets of proteins all sharing a same subset of structural motifs. In the context of an ever growing number of available 3D protein structures, standard and automatic clustering algorithms require adaptations so as to allow for efficient identification of such sets of proteins. Results When considering a pair of 3D structures, they are stated as similar or not according to the local similarities of their matching substructures in a structural alignment. This binary relation can be represented in a graph of similarities where a node represents a 3D protein structure and an edge states that two 3D protein structures are similar. Therefore, classifying proteins into structural families can be viewed as a graph clustering task. Unfortunately, because such a graph encodes only pairwise similarity information, clustering algorithms may include in the same cluster a subset of 3D structures that do not share a common substructure. In order to overcome this drawback we first define a ternary similarity on a triple of 3D structures as a constraint to be satisfied by the graph of similarities. Such a ternary constraint takes into account similarities between pairwise alignments, so as to ensure that the three involved protein structures do have some common substructure. We propose hereunder a modification algorithm that eliminates edges from the original graph of similarities and gives a reduced graph in which no ternary constraints are violated. Our approach is then first to build a graph of similarities, then to reduce the graph according to the modification algorithm, and finally to apply to the reduced graph a standard graph clustering algorithm. Such method was used for classifying ASTRAL-40 non-redundant protein domains, identifying significant pairwise similarities with Yakusa, a program devised for rapid 3D structure alignments. Conclusions We show that filtering similarities prior to standard graph based clustering process by applying ternary similarity constraints i) improves the separation of proteins of different classes and consequently ii) improves the classification quality of standard graph based clustering algorithms according to the reference classification SCOP. PMID:22974051

A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images.

PubMed

Zheng, Qiang; Warner, Steven; Tasian, Gregory; Fan, Yong

2018-02-12

Automatic segmentation of kidneys in ultrasound (US) images remains a challenging task because of high speckle noise, low contrast, and large appearance variations of kidneys in US images. Because texture features may improve the US image segmentation performance, we propose a novel graph cuts method to segment kidney in US images by integrating image intensity information and texture feature maps. We develop a new graph cuts-based method to segment kidney US images by integrating original image intensity information and texture feature maps extracted using Gabor filters. To handle large appearance variation within kidney images and improve computational efficiency, we build a graph of image pixels close to kidney boundary instead of building a graph of the whole image. To make the kidney segmentation robust to weak boundaries, we adopt localized regional information to measure similarity between image pixels for computing edge weights to build the graph of image pixels. The localized graph is dynamically updated and the graph cuts-based segmentation iteratively progresses until convergence. Our method has been evaluated based on kidney US images of 85 subjects. The imaging data of 20 randomly selected subjects were used as training data to tune parameters of the image segmentation method, and the remaining data were used as testing data for validation. Experiment results demonstrated that the proposed method obtained promising segmentation results for bilateral kidneys (average Dice index = 0.9446, average mean distance = 2.2551, average specificity = 0.9971, average accuracy = 0.9919), better than other methods under comparison (P < .05, paired Wilcoxon rank sum tests). The proposed method achieved promising performance for segmenting kidneys in two-dimensional US images, better than segmentation methods built on any single channel of image information. This method will facilitate extraction of kidney characteristics that may predict important clinical outcomes such as progression of chronic kidney disease. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Optimized Graph Learning Using Partial Tags and Multiple Features for Image and Video Annotation.

PubMed

Song, Jingkuan; Gao, Lianli; Nie, Feiping; Shen, Heng Tao; Yan, Yan; Sebe, Nicu

2016-11-01

In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available. This is often done by adding a geometry-based regularization term in the objective function of a supervised learning model. In this case, a similarity graph is indispensable to exploit the geometrical relationships among the training data points, and the graph construction scheme essentially determines the performance of these graph-based learning algorithms. However, most of the existing works construct the graph empirically and are usually based on a single feature without using the label information. In this paper, we propose a semi-supervised annotation approach by learning an optimized graph (OGL) from multi-cues (i.e., partial tags and multiple features), which can more accurately embed the relationships among the data points. Since OGL is a transductive method and cannot deal with novel data points, we further extend our model to address the out-of-sample issue. Extensive experiments on image and video annotation show the consistent superiority of OGL over the state-of-the-art methods.

Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains.

PubMed

Trengove, Chris; Diesmann, Markus; van Leeuwen, Cees

2016-02-01

As a candidate mechanism of neural representation, large numbers of synfire chains can efficiently be embedded in a balanced recurrent cortical network model. Here we study a model in which multiple synfire chains of variable strength are randomly coupled together to form a recurrent system. The system can be implemented both as a large-scale network of integrate-and-fire neurons and as a reduced model. The latter has binary-state pools as basic units but is otherwise isomorphic to the large-scale model, and provides an efficient tool for studying its behavior. Both the large-scale system and its reduced counterpart are able to sustain ongoing endogenous activity in the form of synfire waves, the proliferation of which is regulated by negative feedback caused by collateral noise. Within this equilibrium, diverse repertoires of ongoing activity are observed, including meta-stability and multiple steady states. These states arise in concert with an effective connectivity structure (ECS). The ECS admits a family of effective connectivity graphs (ECGs), parametrized by the mean global activity level. Of these graphs, the strongly connected components and their associated out-components account to a large extent for the observed steady states of the system. These results imply a notion of dynamic effective connectivity as governing neural computation with synfire chains, and related forms of cortical circuitry with complex topologies.

graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture.

PubMed

Chung, Dongjun; Kim, Hang J; Zhao, Hongyu

2017-02-01

Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. However, identification of risk variants associated with complex diseases remains challenging as they are often affected by many genetic variants with small or moderate effects. There has been accumulating evidence suggesting that different complex traits share common risk basis, namely pleiotropy. Recently, several statistical methods have been developed to improve statistical power to identify risk variants for complex traits through a joint analysis of multiple GWAS datasets by leveraging pleiotropy. While these methods were shown to improve statistical power for association mapping compared to separate analyses, they are still limited in the number of phenotypes that can be integrated. In order to address this challenge, in this paper, we propose a novel statistical framework, graph-GPA, to integrate a large number of GWAS datasets for multiple phenotypes using a hidden Markov random field approach. Application of graph-GPA to a joint analysis of GWAS datasets for 12 phenotypes shows that graph-GPA improves statistical power to identify risk variants compared to statistical methods based on smaller number of GWAS datasets. In addition, graph-GPA also promotes better understanding of genetic mechanisms shared among phenotypes, which can potentially be useful for the development of improved diagnosis and therapeutics. The R implementation of graph-GPA is currently available at https://dongjunchung.github.io/GGPA/.

Plan-graph Based Heuristics for Conformant Probabilistic Planning

NASA Technical Reports Server (NTRS)

Ramakrishnan, Salesh; Pollack, Martha E.; Smith, David E.

2004-01-01

In this paper, we introduce plan-graph based heuristics to solve a variation of the conformant probabilistic planning (CPP) problem. In many real-world problems, it is the case that the sensors are unreliable or take too many resources to provide knowledge about the environment. These domains are better modeled as conformant planning problems. POMDP based techniques are currently the most successful approach for solving CPP but have the limitation of state- space explosion. Recent advances in deterministic and conformant planning have shown that plan-graphs can be used to enhance the performance significantly. We show that this enhancement can also be translated to CPP. We describe our process for developing the plan-graph heuristics and estimating the probability of a partial plan. We compare the performance of our planner PVHPOP when used with different heuristics. We also perform a comparison with a POMDP solver to show over a order of magnitude improvement in performance.

Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines

PubMed Central

Zhang, Kai; Lan, Liang; Kwok, James T.; Vucetic, Slobodan; Parvin, Bahram

2014-01-01

When the amount of labeled data are limited, semi-supervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximating this manifold as a weighted graph, such graph-based techniques can often achieve state-of-the-art performance. However, their high time and space complexities make them less attractive on large data sets. In this paper, we propose to scale up graph-based semisupervised learning using a set of sparse prototypes derived from the data. These prototypes serve as a small set of data representatives, which can be used to approximate the graph-based regularizer and to control model complexity. Consequently, both training and testing become much more efficient. Moreover, when the Gaussian kernel is used to define the graph affinity, a simple and principled method to select the prototypes can be obtained. Experiments on a number of real-world data sets demonstrate encouraging performance and scaling properties of the proposed approach. It also compares favorably with models learned via ℓ1-regularization at the same level of model sparsity. These results demonstrate the efficacy of the proposed approach in producing highly parsimonious and accurate models for semisupervised learning. PMID:25720002

Co-occurrence graphs for word sense disambiguation in the biomedical domain.

PubMed

Duque, Andres; Stevenson, Mark; Martinez-Romo, Juan; Araujo, Lourdes

2018-05-01

Word sense disambiguation is a key step for many natural language processing tasks (e.g. summarization, text classification, relation extraction) and presents a challenge to any system that aims to process documents from the biomedical domain. In this paper, we present a new graph-based unsupervised technique to address this problem. The knowledge base used in this work is a graph built with co-occurrence information from medical concepts found in scientific abstracts, and hence adapted to the specific domain. Unlike other unsupervised approaches based on static graphs such as UMLS, in this work the knowledge base takes the context of the ambiguous terms into account. Abstracts downloaded from PubMed are used for building the graph and disambiguation is performed using the personalized PageRank algorithm. Evaluation is carried out over two test datasets widely explored in the literature. Different parameters of the system are also evaluated to test robustness and scalability. Results show that the system is able to outperform state-of-the-art knowledge-based systems, obtaining more than 10% of accuracy improvement in some cases, while only requiring minimal external resources. Copyright © 2018 Elsevier B.V. All rights reserved.

Evidence That Calls-Based and Mobility Networks Are Isomorphic

PubMed Central

Coscia, Michele; Hausmann, Ricardo

2015-01-01

Social relations involve both face-to-face interaction as well as telecommunications. We can observe the geography of phone calls and of the mobility of cell phones in space. These two phenomena can be described as networks of connections between different points in space. We use a dataset that includes billions of phone calls made in Colombia during a six-month period. We draw the two networks and find that the call-based network resembles a higher order aggregation of the mobility network and that both are isomorphic except for a higher spatial decay coefficient of the mobility network relative to the call-based network: when we discount distance effects on the call connections with the same decay observed for mobility connections, the two networks are virtually indistinguishable. PMID:26713730

A Note on the Kirchhoff and Additive Degree-Kirchhoff Indices of Graphs

NASA Astrophysics Data System (ADS)

Yang, Yujun; Klein, Douglas J.

2015-06-01

Two resistance-distance-based graph invariants, namely, the Kirchhoff index and the additive degree-Kirchhoff index, are studied. A relation between them is established, with inequalities for the additive degree-Kirchhoff index arising via the Kirchhoff index along with minimum, maximum, and average degrees. Bounds for the Kirchhoff and additive degree-Kirchhoff indices are also determined, and extremal graphs are characterised. In addition, an upper bound for the additive degree-Kirchhoff index is established to improve a previously known result.

An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph

PubMed Central

Zeng, Qinghua; Chen, Weina; Liu, Jianye; Wang, Huizhe

2017-01-01

An integrated navigation system coupled with additional sensors can be used in the Micro Unmanned Aerial Vehicle (MUAV) applications because the multi-sensor information is redundant and complementary, which can markedly improve the system accuracy. How to deal with the information gathered from different sensors efficiently is an important problem. The fact that different sensors provide measurements asynchronously may complicate the processing of these measurements. In addition, the output signals of some sensors appear to have a non-linear character. In order to incorporate these measurements and calculate a navigation solution in real time, the multi-sensor fusion algorithm based on factor graph is proposed. The global optimum solution is factorized according to the chain structure of the factor graph, which allows for a more general form of the conditional probability density. It can convert the fusion matter into connecting factors defined by these measurements to the graph without considering the relationship between the sensor update frequency and the fusion period. An experimental MUAV system has been built and some experiments have been performed to prove the effectiveness of the proposed method. PMID:28335570

An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph.

PubMed

Zeng, Qinghua; Chen, Weina; Liu, Jianye; Wang, Huizhe

2017-03-21

An integrated navigation system coupled with additional sensors can be used in the Micro Unmanned Aerial Vehicle (MUAV) applications because the multi-sensor information is redundant and complementary, which can markedly improve the system accuracy. How to deal with the information gathered from different sensors efficiently is an important problem. The fact that different sensors provide measurements asynchronously may complicate the processing of these measurements. In addition, the output signals of some sensors appear to have a non-linear character. In order to incorporate these measurements and calculate a navigation solution in real time, the multi-sensor fusion algorithm based on factor graph is proposed. The global optimum solution is factorized according to the chain structure of the factor graph, which allows for a more general form of the conditional probability density. It can convert the fusion matter into connecting factors defined by these measurements to the graph without considering the relationship between the sensor update frequency and the fusion period. An experimental MUAV system has been built and some experiments have been performed to prove the effectiveness of the proposed method.

A Hybrid Parallel Strategy Based on String Graph Theory to Improve De Novo DNA Assembly on the TianHe-2 Supercomputer.

PubMed

Zhang, Feng; Liao, Xiangke; Peng, Shaoliang; Cui, Yingbo; Wang, Bingqiang; Zhu, Xiaoqian; Liu, Jie

2016-06-01

' The de novo assembly of DNA sequences is increasingly important for biological researches in the genomic era. After more than one decade since the Human Genome Project, some challenges still exist and new solutions are being explored to improve de novo assembly of genomes. String graph assembler (SGA), based on the string graph theory, is a new method/tool developed to address the challenges. In this paper, based on an in-depth analysis of SGA we prove that the SGA-based sequence de novo assembly is an NP-complete problem. According to our analysis, SGA outperforms other similar methods/tools in memory consumption, but costs much more time, of which 60-70 % is spent on the index construction. Upon this analysis, we introduce a hybrid parallel optimization algorithm and implement this algorithm in the TianHe-2's parallel framework. Simulations are performed with different datasets. For data of small size the optimized solution is 3.06 times faster than before, and for data of middle size it's 1.60 times. The results demonstrate an evident performance improvement, with the linear scalability for parallel FM-index construction. This results thus contribute significantly to improving the efficiency of de novo assembly of DNA sequences.

A mapping theory of agrammatic comprehension deficits.

PubMed

O'Grady, William; Lee, Miseon

2005-01-01

This paper offers evidence for the Isomorphic Mapping Hypothesis, which holds that individuals with agrammatic aphasia tend to have difficulty comprehending sentences in which the order of NPs is not aligned with the structure of the corresponding event. We begin by identifying a set of constructions in English and Korean for which the IMH makes predictions distinct from those of canonical order and trace-based theories of agrammatic comprehension. Then, drawing on data involving the interpretation of those patterns by English-speaking and Korean-speaking agrammatics, we argue for the conceptual and empirical superiority of the isomorphic mapping account.

Assessing Student Expertise in Introductory Physics with Isomorphic Problems. II. Effect of Some Potential Factors on Problem Solving and Transfer

ERIC Educational Resources Information Center

Chandralekha; Singh

2008-01-01

In this paper, we explore the use of isomorphic problem pairs (IPPs) to assess introductory physics students' ability to solve and successfully transfer problem-solving knowledge from one context to another in mechanics. We call the paired problems "isomorphic" because they require the same physics principle to solve them. We analyze written…

A Method to Determine of All Non-Isomorphic Groups of Order 16

ERIC Educational Resources Information Center

Valcan, Dumitru

2012-01-01

Many students or teachers ask themselves: Being given a natural number n, how many non-isomorphic groups of order n exists? The answer, generally, is not yet given. But, for certain values of the number n have answered this question. The present work gives a method to determine of all non-isomorphic groups of order 16 and gives descriptions of all…

Functional grouping of similar genes using eigenanalysis on minimum spanning tree based neighborhood graph.

PubMed

Jothi, R; Mohanty, Sraban Kumar; Ojha, Aparajita

2016-04-01

Gene expression data clustering is an important biological process in DNA microarray analysis. Although there have been many clustering algorithms for gene expression analysis, finding a suitable and effective clustering algorithm is always a challenging problem due to the heterogeneous nature of gene profiles. Minimum Spanning Tree (MST) based clustering algorithms have been successfully employed to detect clusters of varying shapes and sizes. This paper proposes a novel clustering algorithm using Eigenanalysis on Minimum Spanning Tree based neighborhood graph (E-MST). As MST of a set of points reflects the similarity of the points with their neighborhood, the proposed algorithm employs a similarity graph obtained from k(') rounds of MST (k(')-MST neighborhood graph). By studying the spectral properties of the similarity matrix obtained from k(')-MST graph, the proposed algorithm achieves improved clustering results. We demonstrate the efficacy of the proposed algorithm on 12 gene expression datasets. Experimental results show that the proposed algorithm performs better than the standard clustering algorithms. Copyright © 2016 Elsevier Ltd. All rights reserved.

Simplicity of condensed matter at its core: generic definition of a Roskilde-simple system.

PubMed

Schrøder, Thomas B; Dyre, Jeppe C

2014-11-28

The isomorph theory is reformulated by defining Roskilde-simple systems by the property that the order of the potential energies of configurations at one density is maintained when these are scaled uniformly to a different density. If the potential energy as a function of all particle coordinates is denoted by U(R), this requirement translates into U(Ra) < U(Rb) ⇒ U(λRa) < U(λRb). Isomorphs remain curves in the thermodynamic phase diagram along which structure, dynamics, and excess entropy are invariant, implying that the phase diagram is effectively one-dimensional with respect to many reduced-unit properties. In contrast to the original formulation of the isomorph theory, however, the density-scaling exponent is not exclusively a function of density and the isochoric heat capacity is not an exact isomorph invariant. A prediction is given for the latter quantity's variation along the isomorphs. Molecular dynamics simulations of the Lennard-Jones and Lennard-Jones Gaussian systems validate the new approach.

A Research Graph dataset for connecting research data repositories using RD-Switchboard.

PubMed

Aryani, Amir; Poblet, Marta; Unsworth, Kathryn; Wang, Jingbo; Evans, Ben; Devaraju, Anusuriya; Hausstein, Brigitte; Klas, Claus-Peter; Zapilko, Benjamin; Kaplun, Samuele

2018-05-29

This paper describes the open access graph dataset that shows the connections between Dryad, CERN, ANDS and other international data repositories to publications and grants across multiple research data infrastructures. The graph dataset was created using the Research Graph data model and the Research Data Switchboard (RD-Switchboard), a collaborative project by the Research Data Alliance DDRI Working Group (DDRI WG) with the aim to discover and connect the related research datasets based on publication co-authorship or jointly funded grants. The graph dataset allows researchers to trace and follow the paths to understanding a body of work. By mapping the links between research datasets and related resources, the graph dataset improves both their discovery and visibility, while avoiding duplicate efforts in data creation. Ultimately, the linked datasets may spur novel ideas, facilitate reproducibility and re-use in new applications, stimulate combinatorial creativity, and foster collaborations across institutions.

QSPR modeling: graph connectivity indices versus line graph connectivity indices

PubMed

Basak; Nikolic; Trinajstic; Amic; Beslo

2000-07-01

Five QSPR models of alkanes were reinvestigated. Properties considered were molecular surface-dependent properties (boiling points and gas chromatographic retention indices) and molecular volume-dependent properties (molar volumes and molar refractions). The vertex- and edge-connectivity indices were used as structural parameters. In each studied case we computed connectivity indices of alkane trees and alkane line graphs and searched for the optimum exponent. Models based on indices with an optimum exponent and on the standard value of the exponent were compared. Thus, for each property we generated six QSPR models (four for alkane trees and two for the corresponding line graphs). In all studied cases QSPR models based on connectivity indices with optimum exponents have better statistical characteristics than the models based on connectivity indices with the standard value of the exponent. The comparison between models based on vertex- and edge-connectivity indices gave in two cases (molar volumes and molar refractions) better models based on edge-connectivity indices and in three cases (boiling points for octanes and nonanes and gas chromatographic retention indices) better models based on vertex-connectivity indices. Thus, it appears that the edge-connectivity index is more appropriate to be used in the structure-molecular volume properties modeling and the vertex-connectivity index in the structure-molecular surface properties modeling. The use of line graphs did not improve the predictive power of the connectivity indices. Only in one case (boiling points of nonanes) a better model was obtained with the use of line graphs.

Isomorphic red blood cells using automated urine flow cytometry is a reliable method in diagnosis of bladder cancer.

PubMed

Muto, Satoru; Sugiura, Syo-Ichiro; Nakajima, Akiko; Horiuchi, Akira; Inoue, Masahiro; Saito, Keisuke; Isotani, Shuji; Yamaguchi, Raizo; Ide, Hisamitsu; Horie, Shigeo

2014-10-01

We aimed to identify patients with a chief complaint of hematuria who could safely avoid unnecessary radiation and instrumentation in the diagnosis of bladder cancer (BC), using automated urine flow cytometry to detect isomorphic red blood cells (RBCs) in urine. We acquired urine samples from 134 patients over the age of 35 years with a chief complaint of hematuria and a positive urine occult blood test or microhematuria. The data were analyzed using the UF-1000i (®) (Sysmex Co., Ltd., Kobe, Japan) automated urine flow cytometer to determine RBC morphology, which was classified as isomorphic or dysmorphic. The patients were divided into two groups (BC versus non-BC) for statistical analysis. Multivariate logistic regression analysis was used to determine the predictive value of flow cytometry versus urine cytology, the bladder tumor antigen test, occult blood in urine test, and microhematuria test. BC was confirmed in 26 of 134 patients (19.4 %). The area under the curve for RBC count using the automated urine flow cytometer was 0.94, representing the highest reference value obtained in this study. Isomorphic RBCs were detected in all patients in the BC group. On multivariate logistic regression analysis, only isomorphic RBC morphology was significantly predictive for BC (p < 0.001). Analytical parameters such as sensitivity, specificity, positive predictive value, and negative predictive value of isomorphic RBCs in urine were 100.0, 91.7, 74.3, and 100.0 %, respectively. Detection of urinary isomorphic RBCs using automated urine flow cytometry is a reliable method in the diagnosis of BC with hematuria.

Weighted graph based ordering techniques for preconditioned conjugate gradient methods

NASA Technical Reports Server (NTRS)

Clift, Simon S.; Tang, Wei-Pai

1994-01-01

We describe the basis of a matrix ordering heuristic for improving the incomplete factorization used in preconditioned conjugate gradient techniques applied to anisotropic PDE's. Several new matrix ordering techniques, derived from well-known algorithms in combinatorial graph theory, which attempt to implement this heuristic, are described. These ordering techniques are tested against a number of matrices arising from linear anisotropic PDE's, and compared with other matrix ordering techniques. A variation of RCM is shown to generally improve the quality of incomplete factorization preconditioners.

Automorphisms of Order Structures of Abelian Parts of Operator Algebras and Their Role in Quantum Theory

NASA Astrophysics Data System (ADS)

Hamhalter, Jan; Turilova, Ekaterina

2014-10-01

It is shown that any order isomorphism between the structures of unital associative JB subalgebras of JB algebras is given naturally by a partially linear Jordan isomorphism. The same holds for nonunital subalgebras and order isomorphisms preserving the unital subalgebra. Finally, we recover usual action of time evolution group on a von Neumann factor from group of automorphisms of the structure of Abelian subalgebras.

Atomic Layer Deposition of Metastable β-Fe 2 O 3 via Isomorphic Epitaxy for Photoassisted Water Oxidation

DOE PAGES

Emery, Jonathan D.; Schlepütz, Christian M.; Guo, Peijun; ...

2014-12-09

Here, we report the growth and photoelectrochemical (PEC) characterization of the uncommon bibyite phase of iron(III) oxide (β-Fe 2O 3) epitaxially stabilized via atomic layer deposition on an conductive, transparent, and isomorphic template (Sn-doped In 2O 3). Furthermore, as a photoanode, unoptimized β-Fe 2O 3 ultrathin films perform similarly to their ubiquitous α-phase (hematite) counterpart, but reveal a more ideal bandgap (1.8 eV), a ~0.1 V improved photocurrent onset potential, and longer wavelength (>600 nm) spectral response. Finally, stable operation under basic water oxidation justifies further exploration of this atypical phase and motivates the investigation of other unexplored metastable phasesmore » as new PEC materials.« less

Interference graph-based dynamic frequency reuse in optical attocell networks

NASA Astrophysics Data System (ADS)

Liu, Huanlin; Xia, Peijie; Chen, Yong; Wu, Lan

2017-11-01

Indoor optical attocell network may achieve higher capacity than radio frequency (RF) or Infrared (IR)-based wireless systems. It is proposed as a special type of visible light communication (VLC) system using Light Emitting Diodes (LEDs). However, the system spectral efficiency may be severely degraded owing to the inter-cell interference (ICI), particularly for dense deployment scenarios. To address these issues, we construct the spectral interference graph for indoor optical attocell network, and propose the Dynamic Frequency Reuse (DFR) and Weighted Dynamic Frequency Reuse (W-DFR) algorithms to decrease ICI and improve the spectral efficiency performance. The interference graph makes LEDs can transmit data without interference and select the minimum sub-bands needed for frequency reuse. Then, DFR algorithm reuses the system frequency equally across service-providing cells to mitigate spectrum interference. While W-DFR algorithm can reuse the system frequency by using the bandwidth weight (BW), which is defined based on the number of service users. Numerical results show that both of the proposed schemes can effectively improve the average spectral efficiency (ASE) of the system. Additionally, improvement of the user data rate is also obtained by analyzing its cumulative distribution function (CDF).

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.

Experimental Demonstration of Isomorphism.

ERIC Educational Resources Information Center

Kamenicek, J.; Melicharek, M.

2000-01-01

Describes some simple experiments related to the properties of crystals. Illustrates isomorphism using single crystals of alum. Presents experiments for determining how various mixture compositions affect the growth of salt crystals. (WRM)

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.

Multiple sclerosis lesion segmentation using an automatic multimodal graph cuts.

PubMed

García-Lorenzo, Daniel; Lecoeur, Jeremy; Arnold, Douglas L; Collins, D Louis; Barillot, Christian

2009-01-01

Graph Cuts have been shown as a powerful interactive segmentation technique in several medical domains. We propose to automate the Graph Cuts in order to automatically segment Multiple Sclerosis (MS) lesions in MRI. We replace the manual interaction with a robust EM-based approach in order to discriminate between MS lesions and the Normal Appearing Brain Tissues (NABT). Evaluation is performed in synthetic and real images showing good agreement between the automatic segmentation and the target segmentation. We compare our algorithm with the state of the art techniques and with several manual segmentations. An advantage of our algorithm over previously published ones is the possibility to semi-automatically improve the segmentation due to the Graph Cuts interactive feature.

Thinking graphically: Connecting vision and cognition during graph comprehension.

PubMed

Ratwani, Raj M; Trafton, J Gregory; Boehm-Davis, Deborah A

2008-03-01

Task analytic theories of graph comprehension account for the perceptual and conceptual processes required to extract specific information from graphs. Comparatively, the processes underlying information integration have received less attention. We propose a new framework for information integration that highlights visual integration and cognitive integration. During visual integration, pattern recognition processes are used to form visual clusters of information; these visual clusters are then used to reason about the graph during cognitive integration. In 3 experiments, the processes required to extract specific information and to integrate information were examined by collecting verbal protocol and eye movement data. Results supported the task analytic theories for specific information extraction and the processes of visual and cognitive integration for integrative questions. Further, the integrative processes scaled up as graph complexity increased, highlighting the importance of these processes for integration in more complex graphs. Finally, based on this framework, design principles to improve both visual and cognitive integration are described. PsycINFO Database Record (c) 2008 APA, all rights reserved

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

Graph-Based Semi-Supervised Hyperspectral Image Classification Using Spatial Information

NASA Astrophysics Data System (ADS)

Jamshidpour, N.; Homayouni, S.; Safari, A.

2017-09-01

Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

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.

F-RAG: Generating Atomic Coordinates from RNA Graphs by Fragment Assembly.

PubMed

Jain, Swati; Schlick, Tamar

2017-11-24

Coarse-grained models represent attractive approaches to analyze and simulate ribonucleic acid (RNA) molecules, for example, for structure prediction and design, as they simplify the RNA structure to reduce the conformational search space. Our structure prediction protocol RAGTOP (RNA-As-Graphs Topology Prediction) represents RNA structures as tree graphs and samples graph topologies to produce candidate graphs. However, for a more detailed study and analysis, construction of atomic from coarse-grained models is required. Here we present our graph-based fragment assembly algorithm (F-RAG) to convert candidate three-dimensional (3D) tree graph models, produced by RAGTOP into atomic structures. We use our related RAG-3D utilities to partition graphs into subgraphs and search for structurally similar atomic fragments in a data set of RNA 3D structures. The fragments are edited and superimposed using common residues, full atomic models are scored using RAGTOP's knowledge-based potential, and geometries of top scoring models is optimized. To evaluate our models, we assess all-atom RMSDs and Interaction Network Fidelity (a measure of residue interactions) with respect to experimentally solved structures and compare our results to other fragment assembly programs. For a set of 50 RNA structures, we obtain atomic models with reasonable geometries and interactions, particularly good for RNAs containing junctions. Additional improvements to our protocol and databases are outlined. These results provide a good foundation for further work on RNA structure prediction and design applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Graphing Calculator Mini Course

NASA Technical Reports Server (NTRS)

Karnawat, Sunil R.

1996-01-01

The "Graphing Calculator Mini Course" project provided a mathematically-intensive technologically-based summer enrichment workshop for teachers of American Indian students on the Turtle Mountain Indian Reservation. Eleven such teachers participated in the six-day workshop in summer of 1996 and three Sunday workshops in the academic year. The project aimed to improve science and mathematics education on the reservation by showing teachers effective ways to use high-end graphing calculators as teaching and learning tools in science and mathematics courses at all levels. In particular, the workshop concentrated on applying TI-82's user-friendly features to understand the various mathematical and scientific concepts.

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.

The effect of performance feedback on drivers' hazard perception ability and self-ratings.

PubMed

Horswill, Mark S; Garth, Megan; Hill, Andrew; Watson, Marcus O

2017-04-01

Drivers' hazard perception ability has been found to predict crash risk, and novice drivers appear to be particularly poor at this skill. This competency appears to develop only slowly with experience, and this could partially be a result of poor quality performance feedback. We report an experiment in which we provided high-quality artificial feedback on individual drivers' performance in a validated video-based hazard perception test via either: (1) a graph-based comparison of hazard perception response times between the test-taker, the average driver, and an expert driver; (2) a video-based comparison between the same groups; or (3) both. All three types of feedback resulted in both an improvement in hazard perception performance and a reduction in self-rated hazard perception skill, compared with a no-feedback control group. Video-based and graph-based feedback combined resulted in a greater improvement in hazard perception performance than either of the individual components, which did not differ from one another. All three types of feedback eliminated participants' self-enhancement bias for hazard perception skill. Participants judged both interventions involving video feedback to be significantly more likely to improve their real-world driving than the no feedback control group. While all three forms of feedback had some value, the combined video and graph feedback intervention appeared to be the most effective across all outcome measures. Copyright © 2017 Elsevier Ltd. All rights reserved.

A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.

PubMed

Yan, Kang K; Zhao, Hongyu; Pang, Herbert

2017-12-06

High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking. In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.

Hypnosis in sport: an Isomorphic Model.

PubMed

Robazza, C; Bortoli, L

1994-10-01

Hypnosis in sport can be applied according to an Isomorphic Model. Active-alert hypnosis is induced before or during practice whereas traditional hypnosis is induced after practice to establish connections between the two experiences. The fundamental goals are to (a) develop mental skills important to both motor and hypnotic performance, (b) supply a wide range of motor and hypnotic bodily experiences important to performance, and (c) induce alert hypnosis before or during performance. The model is based on the assumption that hypnosis and motor performance share common skills modifiable through training. Similarities between hypnosis and peak performance in the model are also considered. Some predictions are important from theoretical and practical points of view.

Liquid state isomorphism, Rosenfeld-Tarazona temperature scaling, and Riemannian thermodynamic geometry.

PubMed

Mausbach, Peter; Köster, Andreas; Vrabec, Jadran

2018-05-01

Aspects of isomorph theory, Rosenfeld-Tarazona temperature scaling, and thermodynamic geometry are comparatively discussed on the basis of the Lennard-Jones potential. The first two approaches approximate the high-density fluid state well when the repulsive interparticle interactions become dominant, which is typically the case close to the freezing line. However, previous studies of Rosenfeld-Tarazona scaling for the isochoric heat capacity and its relation to isomorph theory reveal deviations for the temperature dependence. It turns out that a definition of a state region in which repulsive interactions dominate is required for achieving consistent results. The Riemannian thermodynamic scalar curvature R allows for such a classification, indicating predominantly repulsive interactions by R>0. An analysis of the isomorphic character of the freezing line and the validity of Rosenfeld-Tarazona temperature scaling show that these approaches are consistent only in a small state region.

Efficient solution for finding Hamilton cycles in undirected graphs.

PubMed

Alhalabi, Wadee; Kitanneh, Omar; Alharbi, Amira; Balfakih, Zain; Sarirete, Akila

2016-01-01

The Hamilton cycle problem is closely related to a series of famous problems and puzzles (traveling salesman problem, Icosian game) and, due to the fact that it is NP-complete, it was extensively studied with different algorithms to solve it. The most efficient algorithm is not known. In this paper, a necessary condition for an arbitrary un-directed graph to have Hamilton cycle is proposed. Based on this condition, a mathematical solution for this problem is developed and several proofs and an algorithmic approach are introduced. The algorithm is successfully implemented on many Hamiltonian and non-Hamiltonian graphs. This provides a new effective approach to solve a problem that is fundamental in graph theory and can influence the manner in which the existing applications are used and improved.

Graph-based urban scene analysis using symbolic data

NASA Astrophysics Data System (ADS)

Moissinac, Henri; Maitre, Henri; Bloch, Isabelle

1995-07-01

A framework is presented for the interpretation of a urban landscape based on the analysis of aerial pictures. This method has been designed for the use of a priori knowledge provided by a geographic map in order to improve the image analysis stage. A coherent final interpretation of the studied area is proposed. It relies on a graph based data structure to modelize the urban landscape, and on a global uncertainty management to evaluate the final confidence we can have in the results presented. This structure and uncertainty management tend to reflect the hierarchy of the available data and the interpretation levels.

Detecting labor using graph theory on connectivity matrices of uterine EMG.

PubMed

Al-Omar, S; Diab, A; Nader, N; Khalil, M; Karlsson, B; Marque, C

2015-08-01

Premature labor is one of the most serious health problems in the developed world. One of the main reasons for this is that no good way exists to distinguish true labor from normal pregnancy contractions. The aim of this paper is to investigate if the application of graph theory techniques to multi-electrode uterine EMG signals can improve the discrimination between pregnancy contractions and labor. To test our methods we first applied them to synthetic graphs where we detected some differences in the parameters results and changes in the graph model from pregnancy-like graphs to labor-like graphs. Then, we applied the same methods to real signals. We obtained the best differentiation between pregnancy and labor through the same parameters. Major improvements in differentiating between pregnancy and labor were obtained using a low pass windowing preprocessing step. Results show that real graphs generally became more organized when moving from pregnancy, where the graph showed random characteristics, to labor where the graph became a more small-world like graph.

Efficient Extraction of High Centrality Vertices in Distributed Graphs

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

Kumbhare, Alok; Frincu, Marc; Raghavendra, Cauligi S.

2014-09-09

Betweenness centrality (BC) is an important measure for identifying high value or critical vertices in graphs, in variety of domains such as communication networks, road networks, and social graphs. However, calculating betweenness values is prohibitively expensive and, more often, domain experts are interested only in the vertices with the highest centrality values. In this paper, we first propose a partition-centric algorithm (MS-BC) to calculate BC for a large distributed graph that optimizes resource utilization and improves overall performance. Further, we extend the notion of approximate BC by pruning the graph and removing a subset of edges and vertices that contributemore » the least to the betweenness values of other vertices (MSL-BC), which further improves the runtime performance. We evaluate the proposed algorithms using a mix of real-world and synthetic graphs on an HPC cluster and analyze its strengths and weaknesses. The experimental results show an improvement in performance of upto 12x for large sparse graphs as compared to the state-of-the-art, and at the same time highlights the need for better partitioning methods to enable a balanced workload across partitions for unbalanced graphs such as small-world or power-law graphs.« less

Using an isomorphic problem pair to learn introductory physics: Transferring from a two-step problem to a three-step problem

NASA Astrophysics Data System (ADS)

Lin, Shih-Yin; Singh, Chandralekha

2013-12-01

In this study, we examine introductory physics students’ ability to perform analogical reasoning between two isomorphic problems which employ the same underlying physics principles but have different surface features. 382 students from a calculus-based and an algebra-based introductory physics course were administered a quiz in the recitation in which they had to learn from a solved problem provided and take advantage of what they learned from it to solve another isomorphic problem (which we call the quiz problem). The solved problem provided has two subproblems while the quiz problem has three subproblems, which is known from previous research to be challenging for introductory students. In addition to the solved problem, students also received extra scaffolding supports that were intended to help them discern and exploit the underlying similarities of the isomorphic solved and quiz problems. The data analysis suggests that students had great difficulty in transferring what they learned from a two-step problem to a three-step problem. Although most students were able to learn from the solved problem to some extent with the scaffolding provided and invoke the relevant principles in the quiz problem, they were not necessarily able to apply the principles correctly. We also conducted think-aloud interviews with six introductory students in order to understand in depth the difficulties they had and explore strategies to provide better scaffolding. The interviews suggest that students often superficially mapped the principles employed in the solved problem to the quiz problem without necessarily understanding the governing conditions underlying each principle and examining the applicability of the principle in the new situation in an in-depth manner. Findings suggest that more scaffolding is needed to help students in transferring from a two-step problem to a three-step problem and applying the physics principles appropriately. We outline a few possible strategies for future investigation.

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.

Immobilization of actinides in stable mineral type and ceramic materials (high temperature synthesis)

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

Starkov, O.; Konovalov, E.

1996-05-01

Alternative vitrification technologies are being developed in the world for the immobilization of high radioactive waste in materials with improved thermodynamic stability, as well as improved chemical and thermal stability and stability to radiation. Oxides, synthesized in the form of analogs to rock-forming minerals and ceramics, are among those materials that have highly stable properties and are compatible with the environment. In choosing the appropriate material, we need to be guided by its geometric stability, the minimal number of cations in the structure of the material and the presence of structural elements in the mineral that are isomorphs of uraniummore » and thorium, actinoids found in nature. Rare earth elements, yttrium, zirconium and calcium are therefore suitable. The minerals listed in the table (with the exception of the zircon) are pegatites by origin, i.e. they are formed towards the end of the magma crystallization of silicates form the residual melt, enriched with Ta, Nb, Ti, Zr, Ce, Y, U and Th. Uranium and thorium in the form of isomorphic admixtures form part of the lattice of the mineral. These minerals, which are rather simple in composition and structure and are formed under high temperatures, may be viewed as natural physio-chemical systems that are stable and long-lived in natural environments. The similarity of the properties of actinoids and lanthanoids plays an important role in the geochemistry of uranium and thorium; however, uranium (IV) is closer to the {open_quotes}heavy{close_quotes} group of lanthanoids (the yttrium group) while thorium (IV) is closer to the {open_quotes}light{close_quotes} group (the cerium group). That is why rare earth minerals contain uranium and thorium in the form of isomorphic admixtures.« less

OpenMP Parallelization and Optimization of Graph-Based Machine Learning Algorithms

DOE PAGES

Meng, Zhaoyi; Koniges, Alice; He, Yun Helen; ...

2016-09-21

In this paper, we investigate the OpenMP parallelization and optimization of two novel data classification algorithms. The new algorithms are based on graph and PDE solution techniques and provide significant accuracy and performance advantages over traditional data classification algorithms in serial mode. The methods leverage the Nystrom extension to calculate eigenvalue/eigenvectors of the graph Laplacian and this is a self-contained module that can be used in conjunction with other graph-Laplacian based methods such as spectral clustering. We use performance tools to collect the hotspots and memory access of the serial codes and use OpenMP as the parallelization language to parallelizemore » the most time-consuming parts. Where possible, we also use library routines. We then optimize the OpenMP implementations and detail the performance on traditional supercomputer nodes (in our case a Cray XC30), and test the optimization steps on emerging testbed systems based on Intel’s Knights Corner and Landing processors. We show both performance improvement and strong scaling behavior. Finally, a large number of optimization techniques and analyses are necessary before the algorithm reaches almost ideal scaling.« less

Does Guiding Toward Task-Relevant Information Help Improve Graph Processing and Graph Comprehension of Individuals with Low or High Numeracy? An Eye-Tracker Experiment.

PubMed

Keller, Carmen; Junghans, Alex

2017-11-01

Individuals with low numeracy have difficulties with understanding complex graphs. Combining the information-processing approach to numeracy with graph comprehension and information-reduction theories, we examined whether high numerates' better comprehension might be explained by their closer attention to task-relevant graphical elements, from which they would expect numerical information to understand the graph. Furthermore, we investigated whether participants could be trained in improving their attention to task-relevant information and graph comprehension. In an eye-tracker experiment ( N = 110) involving a sample from the general population, we presented participants with 2 hypothetical scenarios (stomach cancer, leukemia) showing survival curves for 2 treatments. In the training condition, participants received written instructions on how to read the graph. In the control condition, participants received another text. We tracked participants' eye movements while they answered 9 knowledge questions. The sum constituted graph comprehension. We analyzed visual attention to task-relevant graphical elements by using relative fixation durations and relative fixation counts. The mediation analysis revealed a significant ( P < 0.05) indirect effect of numeracy on graph comprehension through visual attention to task-relevant information, which did not differ between the 2 conditions. Training had a significant main effect on visual attention ( P < 0.05) but not on graph comprehension ( P < 0.07). Individuals with high numeracy have better graph comprehension due to their greater attention to task-relevant graphical elements than individuals with low numeracy. With appropriate instructions, both groups can be trained to improve their graph-processing efficiency. Future research should examine (e.g., motivational) mediators between visual attention and graph comprehension to develop appropriate instructions that also result in higher graph comprehension.

Reactome graph database: Efficient access to complex pathway data

PubMed Central

Korninger, Florian; Viteri, Guilherme; Marin-Garcia, Pablo; Ping, Peipei; Wu, Guanming; Stein, Lincoln; D’Eustachio, Peter

2018-01-01

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types. PMID:29377902

Reactome graph database: Efficient access to complex pathway data.

PubMed

Fabregat, Antonio; Korninger, Florian; Viteri, Guilherme; Sidiropoulos, Konstantinos; Marin-Garcia, Pablo; Ping, Peipei; Wu, Guanming; Stein, Lincoln; D'Eustachio, Peter; Hermjakob, Henning

2018-01-01

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types.

Path Integral Metadynamics.

PubMed

Quhe, Ruge; Nava, Marco; Tiwary, Pratyush; Parrinello, Michele

2015-04-14

We develop a new efficient approach for the simulation of static properties of quantum systems using path integral molecular dynamics in combination with metadynamics. We use the isomorphism between a quantum system and a classical one in which a quantum particle is mapped into a ring polymer. A history dependent biasing potential is built as a function of the elastic energy of the isomorphic polymer. This enhances fluctuations in the shape and size of the necklace in a controllable manner and allows escaping deep energy minima in a limited computer time. In this way, we are able to sample high free energy regions and cross barriers, which would otherwise be insurmountable with unbiased methods. This substantially improves the ability of finding the global free energy minimum as well as exploring other metastable states. The performance of the new technique is demonstrated by illustrative applications on model potentials of varying complexity.

Combination of graph heuristics in producing initial solution of curriculum based course timetabling problem

NASA Astrophysics Data System (ADS)

Wahid, Juliana; Hussin, Naimah Mohd

2016-08-01

The construction of population of initial solution is a crucial task in population-based metaheuristic approach for solving curriculum-based university course timetabling problem because it can affect the convergence speed and also the quality of the final solution. This paper presents an exploration on combination of graph heuristics in construction approach in curriculum based course timetabling problem to produce a population of initial solutions. The graph heuristics were set as single and combination of two heuristics. In addition, several ways of assigning courses into room and timeslot are implemented. All settings of heuristics are then tested on the same curriculum based course timetabling problem instances and are compared with each other in terms of number of population produced. The result shows that combination of saturation degree followed by largest degree heuristic produce the highest number of population of initial solutions. The results from this study can be used in the improvement phase of algorithm that uses population of initial solutions.

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.

Graphs, matrices, and the GraphBLAS: Seven good reasons

DOE PAGES

Kepner, Jeremy; Bader, David; Buluç, Aydın; ...

2015-01-01

The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istcbigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implementmore » a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.« less

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.

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.

Towards Scalable Graph Computation on Mobile Devices.

PubMed

Chen, Yiqi; Lin, Zhiyuan; Pienta, Robert; Kahng, Minsuk; Chau, Duen Horng

2014-10-01

Mobile devices have become increasingly central to our everyday activities, due to their portability, multi-touch capabilities, and ever-improving computational power. Such attractive features have spurred research interest in leveraging mobile devices for computation. We explore a novel approach that aims to use a single mobile device to perform scalable graph computation on large graphs that do not fit in the device's limited main memory, opening up the possibility of performing on-device analysis of large datasets, without relying on the cloud. Based on the familiar memory mapping capability provided by today's mobile operating systems, our approach to scale up computation is powerful and intentionally kept simple to maximize its applicability across the iOS and Android platforms. Our experiments demonstrate that an iPad mini can perform fast computation on large real graphs with as many as 272 million edges (Google+ social graph), at a speed that is only a few times slower than a 13″ Macbook Pro. Through creating a real world iOS app with this technique, we demonstrate the strong potential application for scalable graph computation on a single mobile device using our approach.

Towards Scalable Graph Computation on Mobile Devices

PubMed Central

Chen, Yiqi; Lin, Zhiyuan; Pienta, Robert; Kahng, Minsuk; Chau, Duen Horng

2015-01-01

Mobile devices have become increasingly central to our everyday activities, due to their portability, multi-touch capabilities, and ever-improving computational power. Such attractive features have spurred research interest in leveraging mobile devices for computation. We explore a novel approach that aims to use a single mobile device to perform scalable graph computation on large graphs that do not fit in the device's limited main memory, opening up the possibility of performing on-device analysis of large datasets, without relying on the cloud. Based on the familiar memory mapping capability provided by today's mobile operating systems, our approach to scale up computation is powerful and intentionally kept simple to maximize its applicability across the iOS and Android platforms. Our experiments demonstrate that an iPad mini can perform fast computation on large real graphs with as many as 272 million edges (Google+ social graph), at a speed that is only a few times slower than a 13″ Macbook Pro. Through creating a real world iOS app with this technique, we demonstrate the strong potential application for scalable graph computation on a single mobile device using our approach. PMID:25859564

GBS 1.0

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

2010-09-30

The Umbra gbs (Graph-Based Search) library provides implementations of graph-based search/planning algorithms that can be applied to legacy graph data structures. Unlike some other graph algorithm libraries, this one does not require your graph class to inherit from a specific base class. Implementations of Dijkstra's Algorithm and A-Star search are included and can be used with graphs that are lazily-constructed.

A Weight-Adaptive Laplacian Embedding for Graph-Based Clustering.

PubMed

Cheng, De; Nie, Feiping; Sun, Jiande; Gong, Yihong

2017-07-01

Graph-based clustering methods perform clustering on a fixed input data graph. Thus such clustering results are sensitive to the particular graph construction. If this initial construction is of low quality, the resulting clustering may also be of low quality. We address this drawback by allowing the data graph itself to be adaptively adjusted in the clustering procedure. In particular, our proposed weight adaptive Laplacian (WAL) method learns a new data similarity matrix that can adaptively adjust the initial graph according to the similarity weight in the input data graph. We develop three versions of these methods based on the L2-norm, fuzzy entropy regularizer, and another exponential-based weight strategy, that yield three new graph-based clustering objectives. We derive optimization algorithms to solve these objectives. Experimental results on synthetic data sets and real-world benchmark data sets exhibit the effectiveness of these new graph-based clustering methods.

Adjusting protein graphs based on graph entropy.

PubMed

Peng, Sheng-Lung; Tsay, Yu-Wei

2014-01-01

Measuring protein structural similarity attempts to establish a relationship of equivalence between polymer structures based on their conformations. In several recent studies, researchers have explored protein-graph remodeling, instead of looking a minimum superimposition for pairwise proteins. When graphs are used to represent structured objects, the problem of measuring object similarity become one of computing the similarity between graphs. Graph theory provides an alternative perspective as well as efficiency. Once a protein graph has been created, its structural stability must be verified. Therefore, a criterion is needed to determine if a protein graph can be used for structural comparison. In this paper, we propose a measurement for protein graph remodeling based on graph entropy. We extend the concept of graph entropy to determine whether a graph is suitable for representing a protein. The experimental results suggest that when applied, graph entropy helps a conformational on protein graph modeling. Furthermore, it indirectly contributes to protein structural comparison if a protein graph is solid.

Adjusting protein graphs based on graph entropy

PubMed Central

2014-01-01

Measuring protein structural similarity attempts to establish a relationship of equivalence between polymer structures based on their conformations. In several recent studies, researchers have explored protein-graph remodeling, instead of looking a minimum superimposition for pairwise proteins. When graphs are used to represent structured objects, the problem of measuring object similarity become one of computing the similarity between graphs. Graph theory provides an alternative perspective as well as efficiency. Once a protein graph has been created, its structural stability must be verified. Therefore, a criterion is needed to determine if a protein graph can be used for structural comparison. In this paper, we propose a measurement for protein graph remodeling based on graph entropy. We extend the concept of graph entropy to determine whether a graph is suitable for representing a protein. The experimental results suggest that when applied, graph entropy helps a conformational on protein graph modeling. Furthermore, it indirectly contributes to protein structural comparison if a protein graph is solid. PMID:25474347

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.

The PANTHER User Experience

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

Coram, Jamie L.; Morrow, James D.; Perkins, David Nikolaus

2015-09-01

This document describes the PANTHER R&D Application, a proof-of-concept user interface application developed under the PANTHER Grand Challenge LDRD. The purpose of the application is to explore interaction models for graph analytics, drive algorithmic improvements from an end-user point of view, and support demonstration of PANTHER technologies to potential customers. The R&D Application implements a graph-centric interaction model that exposes analysts to the algorithms contained within the GeoGraphy graph analytics library. Users define geospatial-temporal semantic graph queries by constructing search templates based on nodes, edges, and the constraints among them. Users then analyze the results of the queries using bothmore » geo-spatial and temporal visualizations. Development of this application has made user experience an explicit driver for project and algorithmic level decisions that will affect how analysts one day make use of PANTHER technologies.« less

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.

A Combined Approach to Cartographic Displacement for Buildings Based on Skeleton and Improved Elastic Beam Algorithm

PubMed Central

Liu, Yuangang; Guo, Qingsheng; Sun, Yageng; Ma, Xiaoya

2014-01-01

Scale reduction from source to target maps inevitably leads to conflicts of map symbols in cartography and geographic information systems (GIS). Displacement is one of the most important map generalization operators and it can be used to resolve the problems that arise from conflict among two or more map objects. In this paper, we propose a combined approach based on constraint Delaunay triangulation (CDT) skeleton and improved elastic beam algorithm for automated building displacement. In this approach, map data sets are first partitioned. Then the displacement operation is conducted in each partition as a cyclic and iterative process of conflict detection and resolution. In the iteration, the skeleton of the gap spaces is extracted using CDT. It then serves as an enhanced data model to detect conflicts and construct the proximity graph. Then, the proximity graph is adjusted using local grouping information. Under the action of forces derived from the detected conflicts, the proximity graph is deformed using the improved elastic beam algorithm. In this way, buildings are displaced to find an optimal compromise between related cartographic constraints. To validate this approach, two topographic map data sets (i.e., urban and suburban areas) were tested. The results were reasonable with respect to each constraint when the density of the map was not extremely high. In summary, the improvements include (1) an automated parameter-setting method for elastic beams, (2) explicit enforcement regarding the positional accuracy constraint, added by introducing drag forces, (3) preservation of local building groups through displacement over an adjusted proximity graph, and (4) an iterative strategy that is more likely to resolve the proximity conflicts than the one used in the existing elastic beam algorithm. PMID:25470727

Similarities and Differences in the Organizational Characteristics of Charter Schools: A Test of Institutional Isomorphism and Strategy Positioning Theories

ERIC Educational Resources Information Center

Sweet, Thomas

2013-01-01

Charter schools, independent public schools which operate through a system of contracts with public agencies, are heavily promoted as a means of improving the nation's traditional public education system. Managed as private enterprises, these schools compete for students through the implementation of market-style practices. Popularly viewed as…

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.

Efficient Graph Based Assembly of Short-Read Sequences on Hybrid Core Architecture

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

Sczyrba, Alex; Pratap, Abhishek; Canon, Shane

2011-03-22

Advanced architectures can deliver dramatically increased throughput for genomics and proteomics applications, reducing time-to-completion in some cases from days to minutes. One such architecture, hybrid-core computing, marries a traditional x86 environment with a reconfigurable coprocessor, based on field programmable gate array (FPGA) technology. In addition to higher throughput, increased performance can fundamentally improve research quality by allowing more accurate, previously impractical approaches. We will discuss the approach used by Convey?s de Bruijn graph constructor for short-read, de-novo assembly. Bioinformatics applications that have random access patterns to large memory spaces, such as graph-based algorithms, experience memory performance limitations on cache-based x86more » servers. Convey?s highly parallel memory subsystem allows application-specific logic to simultaneously access 8192 individual words in memory, significantly increasing effective memory bandwidth over cache-based memory systems. Many algorithms, such as Velvet and other de Bruijn graph based, short-read, de-novo assemblers, can greatly benefit from this type of memory architecture. Furthermore, small data type operations (four nucleotides can be represented in two bits) make more efficient use of logic gates than the data types dictated by conventional programming models.JGI is comparing the performance of Convey?s graph constructor and Velvet on both synthetic and real data. We will present preliminary results on memory usage and run time metrics for various data sets with different sizes, from small microbial and fungal genomes to very large cow rumen metagenome. For genomes with references we will also present assembly quality comparisons between the two assemblers.« less

SAR-based change detection using hypothesis testing and Markov random field modelling

NASA Astrophysics Data System (ADS)

Cao, W.; Martinis, S.

2015-04-01

The objective of this study is to automatically detect changed areas caused by natural disasters from bi-temporal co-registered and calibrated TerraSAR-X data. The technique in this paper consists of two steps: Firstly, an automatic coarse detection step is applied based on a statistical hypothesis test for initializing the classification. The original analytical formula as proposed in the constant false alarm rate (CFAR) edge detector is reviewed and rewritten in a compact form of the incomplete beta function, which is a builtin routine in commercial scientific software such as MATLAB and IDL. Secondly, a post-classification step is introduced to optimize the noisy classification result in the previous step. Generally, an optimization problem can be formulated as a Markov random field (MRF) on which the quality of a classification is measured by an energy function. The optimal classification based on the MRF is related to the lowest energy value. Previous studies provide methods for the optimization problem using MRFs, such as the iterated conditional modes (ICM) algorithm. Recently, a novel algorithm was presented based on graph-cut theory. This method transforms a MRF to an equivalent graph and solves the optimization problem by a max-flow/min-cut algorithm on the graph. In this study this graph-cut algorithm is applied iteratively to improve the coarse classification. At each iteration the parameters of the energy function for the current classification are set by the logarithmic probability density function (PDF). The relevant parameters are estimated by the method of logarithmic cumulants (MoLC). Experiments are performed using two flood events in Germany and Australia in 2011 and a forest fire on La Palma in 2009 using pre- and post-event TerraSAR-X data. The results show convincing coarse classifications and considerable improvement by the graph-cut post-classification step.

A mechanism of institutional isomorphism in referral networks among hospitals in Seoul, South Korea.

PubMed

Jung, Minsoo; Choi, Mankyu

2010-01-01

Hospitals engage in medical referral system relations voluntarily, by virtue of their own service capacities. These capacities include medical technology, equipment supply, and patient management, which are assessed individually by medical institutions in efforts to control costs and maintain efficiency in tertiary hospitals. This study assessed referral networks according to the institutional isomorphism theory of new economic sociology. As a result, the referral networks were shown to exhibit emergent structural hierarchy via cumulative clustering by established year and were not affected by attributive variables such as region, bed number, and year of establishment. In particular, the networks evidenced institutional isomorphism with certain central hospitals. As a consequence, personal indices were shown to decrease in accordance with its period, and only the structural index increased. Normative pressures cause organizations to become hierarchically homogenized, in accordance with the principle of organizational learning in specialized fields. Therefore, normative isomorphism on the basis of public domains should be considered an inherent factor in the development of referral networks.

Quantum-chemical modeling of smectite clays

NASA Technical Reports Server (NTRS)

Aronowitz, S.; Coyne, L.; Lawless, J.; Rishpon, J.

1982-01-01

A self-consistent charge extended Hueckel program is used in modeling isomorphic substitution of Al(3+) by Na(+), K(+), Mg(2+), Fe(2+), and Fe(3+) in the octahedral layer of a dioctahedral smectite clay, such as montmorillonite. Upon comparison of the energies involved in the isomorphic substitution, it is found that the order for successful substitution is as follows: Al(3+), Fe(3+), Mg(2+), Fe(2+), Na(+), which is equivalent to Ca(2+), and then K(+). This ordering is found to be consistent with experimental observation. The calculations also make it possible to determine the possible penetration of metal ions into the clay's 2:1 crystalline layer. For the cases studied, this type of penetration can occur at elevated temperatures into regions where isomorphic substitution has occurred with metal ions that bear a formal charge of less than 3+. The computed behavior of the electronic structure in the presence of isomorphic substitution is found to be similar to behavior associated with semiconductors.

Second look at the spread of epidemics on networks

NASA Astrophysics Data System (ADS)

Kenah, Eben; Robins, James M.

2007-09-01

In an important paper, Newman [Phys. Rev. E66, 016128 (2002)] claimed that a general network-based stochastic Susceptible-Infectious-Removed (SIR) epidemic model is isomorphic to a bond percolation model, where the bonds are the edges of the contact network and the bond occupation probability is equal to the marginal probability of transmission from an infected node to a susceptible neighbor. In this paper, we show that this isomorphism is incorrect and define a semidirected random network we call the epidemic percolation network that is exactly isomorphic to the SIR epidemic model in any finite population. In the limit of a large population, (i) the distribution of (self-limited) outbreak sizes is identical to the size distribution of (small) out-components, (ii) the epidemic threshold corresponds to the phase transition where a giant strongly connected component appears, (iii) the probability of a large epidemic is equal to the probability that an initial infection occurs in the giant in-component, and (iv) the relative final size of an epidemic is equal to the proportion of the network contained in the giant out-component. For the SIR model considered by Newman, we show that the epidemic percolation network predicts the same mean outbreak size below the epidemic threshold, the same epidemic threshold, and the same final size of an epidemic as the bond percolation model. However, the bond percolation model fails to predict the correct outbreak size distribution and probability of an epidemic when there is a nondegenerate infectious period distribution. We confirm our findings by comparing predictions from percolation networks and bond percolation models to the results of simulations. In the Appendix, we show that an isomorphism to an epidemic percolation network can be defined for any time-homogeneous stochastic SIR model.

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.

A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method.

PubMed

Yoo, Illhoi; Hu, Xiaohua; Song, Il-Yeol

2007-11-27

A huge amount of biomedical textual information has been produced and collected in MEDLINE for decades. In order to easily utilize biomedical information in the free text, document clustering and text summarization together are used as a solution for text information overload problem. In this paper, we introduce a coherent graph-based semantic clustering and summarization approach for biomedical literature. Our extensive experimental results show the approach shows 45% cluster quality improvement and 72% clustering reliability improvement, in terms of misclassification index, over Bisecting K-means as a leading document clustering approach. In addition, our approach provides concise but rich text summary in key concepts and sentences. Our coherent biomedical literature clustering and summarization approach that takes advantage of ontology-enriched graphical representations significantly improves the quality of document clusters and understandability of documents through summaries.

A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method

PubMed Central

Yoo, Illhoi; Hu, Xiaohua; Song, Il-Yeol

2007-01-01

Background A huge amount of biomedical textual information has been produced and collected in MEDLINE for decades. In order to easily utilize biomedical information in the free text, document clustering and text summarization together are used as a solution for text information overload problem. In this paper, we introduce a coherent graph-based semantic clustering and summarization approach for biomedical literature. Results Our extensive experimental results show the approach shows 45% cluster quality improvement and 72% clustering reliability improvement, in terms of misclassification index, over Bisecting K-means as a leading document clustering approach. In addition, our approach provides concise but rich text summary in key concepts and sentences. Conclusion Our coherent biomedical literature clustering and summarization approach that takes advantage of ontology-enriched graphical representations significantly improves the quality of document clusters and understandability of documents through summaries. PMID:18047705

Dynamic airspace configuration algorithms for next generation air transportation system

NASA Astrophysics Data System (ADS)

Wei, Jian

The National Airspace System (NAS) is under great pressure to safely and efficiently handle the record-high air traffic volume nowadays, and will face even greater challenge to keep pace with the steady increase of future air travel demand, since the air travel demand is projected to increase to two to three times the current level by 2025. The inefficiency of traffic flow management initiatives causes severe airspace congestion and frequent flight delays, which cost billions of economic losses every year. To address the increasingly severe airspace congestion and delays, the Next Generation Air Transportation System (NextGen) is proposed to transform the current static and rigid radar based system to a dynamic and flexible satellite based system. New operational concepts such as Dynamic Airspace Configuration (DAC) have been under development to allow more flexibility required to mitigate the demand-capacity imbalances in order to increase the throughput of the entire NAS. In this dissertation, we address the DAC problem in the en route and terminal airspace under the framework of NextGen. We develop a series of algorithms to facilitate the implementation of innovative concepts relevant with DAC in both the en route and terminal airspace. We also develop a performance evaluation framework for comprehensive benefit analyses on different aspects of future sector design algorithms. First, we complete a graph based sectorization algorithm for DAC in the en route airspace, which models the underlying air route network with a weighted graph, converts the sectorization problem into the graph partition problem, partitions the weighted graph with an iterative spectral bipartition method, and constructs the sectors from the partitioned graph. The algorithm uses a graph model to accurately capture the complex traffic patterns of the real flights, and generates sectors with high efficiency while evenly distributing the workload among the generated sectors. We further improve the robustness and efficiency of the graph based DAC algorithm by incorporating the Multilevel Graph Partitioning (MGP) method into the graph model, and develop a MGP based sectorization algorithm for DAC in the en route airspace. In a comprehensive benefit analysis, the performance of the proposed algorithms are tested in numerical simulations with Enhanced Traffic Management System (ETMS) data. Simulation results demonstrate that the algorithmically generated sectorizations outperform the current sectorizations in different sectors for different time periods. Secondly, based on our experience with DAC in the en route airspace, we further study the sectorization problem for DAC in the terminal airspace. The differences between the en route and terminal airspace are identified, and their influence on the terminal sectorization is analyzed. After adjusting the graph model to better capture the unique characteristics of the terminal airspace and the requirements of terminal sectorization, we develop a graph based geometric sectorization algorithm for DAC in the terminal airspace. Moreover, the graph based model is combined with the region based sector design method to better handle the complicated geometric and operational constraints in the terminal sectorization problem. In the benefit analysis, we identify the contributing factors to terminal controller workload, define evaluation metrics, and develop a bebefit analysis framework for terminal sectorization evaluation. With the evaluation framework developed, we demonstrate the improvements on the current sectorizations with real traffic data collected from several major international airports in the U.S., and conduct a detailed analysis on the potential benefits of dynamic reconfiguration in the terminal airspace. Finally, in addition to the research on the macroscopic behavior of a large number of aircraft, we also study the dynamical behavior of individual aircraft from the perspective of traffic flow management. We formulate the mode-confusion problem as hybrid estimation problem, and develop a state estimation algorithm for the linear hybrid system with continuous-state-dependent transitions based on sparse observations. We also develop an estimated time of arrival prediction algorithm based on the state-dependent transition hybrid estimation algorithm, whose performance is demonstrated with simulations on the landing procedure following the Continuous Descend Approach (CDA) profile.

The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images

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

Korsager, Anne Sofie, E-mail: asko@hst.aau.dk; Østergaard, Lasse Riis; Fortunati, Valerio

2015-04-15

Purpose: An automatic method for 3D prostate segmentation in magnetic resonance (MR) images is presented for planning image-guided radiotherapy treatment of prostate cancer. Methods: A spatial prior based on intersubject atlas registration is combined with organ-specific intensity information in a graph cut segmentation framework. The segmentation is tested on 67 axial T{sub 2}-weighted MR images in a leave-one-out cross validation experiment and compared with both manual reference segmentations and with multiatlas-based segmentations using majority voting atlas fusion. The impact of atlas selection is investigated in both the traditional atlas-based segmentation and the new graph cut method that combines atlas andmore » intensity information in order to improve the segmentation accuracy. Best results were achieved using the method that combines intensity information, shape information, and atlas selection in the graph cut framework. Results: A mean Dice similarity coefficient (DSC) of 0.88 and a mean surface distance (MSD) of 1.45 mm with respect to the manual delineation were achieved. Conclusions: This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.« less

Folding and unfolding phylogenetic trees and networks.

PubMed

Huber, Katharina T; Moulton, Vincent; Steel, Mike; Wu, Taoyang

2016-12-01

Phylogenetic networks are rooted, labelled directed acyclic graphswhich are commonly used to represent reticulate evolution. There is a close relationship between phylogenetic networks and multi-labelled trees (MUL-trees). Indeed, any phylogenetic network N can be "unfolded" to obtain a MUL-tree U(N) and, conversely, a MUL-tree T can in certain circumstances be "folded" to obtain aphylogenetic network F(T) that exhibits T. In this paper, we study properties of the operations U and F in more detail. In particular, we introduce the class of stable networks, phylogenetic networks N for which F(U(N)) is isomorphic to N, characterise such networks, and show that they are related to the well-known class of tree-sibling networks. We also explore how the concept of displaying a tree in a network N can be related to displaying the tree in the MUL-tree U(N). To do this, we develop aphylogenetic analogue of graph fibrations. This allows us to view U(N) as the analogue of the universal cover of a digraph, and to establish a close connection between displaying trees in U(N) and reconciling phylogenetic trees with networks.

Students Building Isomorphisms

ERIC Educational Resources Information Center

Uptegrove, Elizabeth B.; Maher, Carolyn A.

2004-01-01

This paper reports on five students' explorations of structural relationships between problem situations that they worked on over several years as participants in a long-term study. In particular, we describe the case of students who recognized isomorphisms between and among two problem situations and who used particular features of the problems…

A Constraint-Based Planner for Data Production

NASA Technical Reports Server (NTRS)

Pang, Wanlin; Golden, Keith

2005-01-01

This paper presents a graph-based backtracking algorithm designed to support constrain-tbased planning in data production domains. This algorithm performs backtracking at two nested levels: the outer- backtracking following the structure of the planning graph to select planner subgoals and actions to achieve them and the inner-backtracking inside a subproblem associated with a selected action to find action parameter values. We show this algorithm works well in a planner applied to automating data production in an ecological forecasting system. We also discuss how the idea of multi-level backtracking may improve efficiency of solving semi-structured constraint problems.

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 normalization and whitening for non-linear data analysis.

PubMed

Aaron, Catherine

2006-01-01

In this paper we construct a graph-based normalization algorithm for non-linear data analysis. The principle of this algorithm is to get a spherical average neighborhood with unit radius. First we present a class of global dispersion measures used for "global normalization"; we then adapt these measures using a weighted graph to build a local normalization called "graph-based" normalization. Then we give details of the graph-based normalization algorithm and illustrate some results. In the second part we present a graph-based whitening algorithm built by analogy between the "global" and the "local" problem.

'You'll See That Everywhere': Institutional Isomorphism in Secondary School Subject Departments

ERIC Educational Resources Information Center

Puttick, Steven

2017-01-01

This paper asks why spatially separated school departments might exhibit, in different ways, very similar practices. Data from an ethnographic study of three secondary school geography departments in England are discussed through a concept of "isomorphism" (homogenising forces), drawn from neo-institutional theory. Similarities across…

EClerize: A customized force-directed graph drawing algorithm for biological graphs with EC attributes.

PubMed

Danaci, Hasan Fehmi; Cetin-Atalay, Rengul; Atalay, Volkan

2018-03-26

Visualizing large-scale data produced by the high throughput experiments as a biological graph leads to better understanding and analysis. This study describes a customized force-directed layout algorithm, EClerize, for biological graphs that represent pathways in which the nodes are associated with Enzyme Commission (EC) attributes. The nodes with the same EC class numbers are treated as members of the same cluster. Positions of nodes are then determined based on both the biological similarity and the connection structure. EClerize minimizes the intra-cluster distance, that is the distance between the nodes of the same EC cluster and maximizes the inter-cluster distance, that is the distance between two distinct EC clusters. EClerize is tested on a number of biological pathways and the improvement brought in is presented with respect to the original algorithm. EClerize is available as a plug-in to cytoscape ( http://apps.cytoscape.org/apps/eclerize ).

Entrepreneurial organizations: the driving force for improving quality in the healthcare industry.

PubMed

Borkowski, Nancy; Gordon, Jean

2006-01-01

Using DiMaggio and Powell's concept of institutional isomorphism, the authors explain why healthcare lags behind other industries in innovating new production functions that address quality. Healthcare finns can "learn" to be entrepreneurial organizations within Stevenson's 'entrepreneurial versus administrative behavior' framework and Covin and Slevin's model of an entrepreneurial organization's required culture and behavioral support structure.

Genome alignment with graph data structures: a comparison

PubMed Central

2014-01-01

Background Recent advances in rapid, low-cost sequencing have opened up the opportunity to study complete genome sequences. The computational approach of multiple genome alignment allows investigation of evolutionarily related genomes in an integrated fashion, providing a basis for downstream analyses such as rearrangement studies and phylogenetic inference. Graphs have proven to be a powerful tool for coping with the complexity of genome-scale sequence alignments. The potential of graphs to intuitively represent all aspects of genome alignments led to the development of graph-based approaches for genome alignment. These approaches construct a graph from a set of local alignments, and derive a genome alignment through identification and removal of graph substructures that indicate errors in the alignment. Results We compare the structures of commonly used graphs in terms of their abilities to represent alignment information. We describe how the graphs can be transformed into each other, and identify and classify graph substructures common to one or more graphs. Based on previous approaches, we compile a list of modifications that remove these substructures. Conclusion We show that crucial pieces of alignment information, associated with inversions and duplications, are not visible in the structure of all graphs. If we neglect vertex or edge labels, the graphs differ in their information content. Still, many ideas are shared among all graph-based approaches. Based on these findings, we outline a conceptual framework for graph-based genome alignment that can assist in the development of future genome alignment tools. PMID:24712884

Multiscale Analysis of Time Irreversibility Based on Phase-Space Reconstruction and Horizontal Visibility Graph Approach

NASA Astrophysics Data System (ADS)

Zhang, Yongping; Shang, Pengjian; Xiong, Hui; Xia, Jianan

Time irreversibility is an important property of nonequilibrium dynamic systems. A visibility graph approach was recently proposed, and this approach is generally effective to measure time irreversibility of time series. However, its result may be unreliable when dealing with high-dimensional systems. In this work, we consider the joint concept of time irreversibility and adopt the phase-space reconstruction technique to improve this visibility graph approach. Compared with the previous approach, the improved approach gives a more accurate estimate for the irreversibility of time series, and is more effective to distinguish irreversible and reversible stochastic processes. We also use this approach to extract the multiscale irreversibility to account for the multiple inherent dynamics of time series. Finally, we apply the approach to detect the multiscale irreversibility of financial time series, and succeed to distinguish the time of financial crisis and the plateau. In addition, Asian stock indexes away from other indexes are clearly visible in higher time scales. Simulations and real data support the effectiveness of the improved approach when detecting time irreversibility.

Preserving Differential Privacy in Degree-Correlation based Graph Generation

PubMed Central

Wang, Yue; Wu, Xintao

2014-01-01

Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as cluster coefficient often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data. In this paper, we study the problem of enforcing edge differential privacy in graph generation. The idea is to enforce differential privacy on graph model parameters learned from the original network and then generate the graphs for releasing using the graph model with the private parameters. In particular, we develop a differential privacy preserving graph generator based on the dK-graph generation model. We first derive from the original graph various parameters (i.e., degree correlations) used in the dK-graph model, then enforce edge differential privacy on the learned parameters, and finally use the dK-graph model with the perturbed parameters to generate graphs. For the 2K-graph model, we enforce the edge differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We conduct experiments on four real networks and compare the performance of our private dK-graph models with the stochastic Kronecker graph generation model in terms of utility and privacy tradeoff. Empirical evaluations show the developed private dK-graph generation models significantly outperform the approach based on the stochastic Kronecker generation model. PMID:24723987

Information visualisation based on graph models

NASA Astrophysics Data System (ADS)

Kasyanov, V. N.; Kasyanova, E. V.

2013-05-01

Information visualisation is a key component of support tools for many applications in science and engineering. A graph is an abstract structure that is widely used to model information for its visualisation. In this paper, we consider practical and general graph formalism called hierarchical graphs and present the Higres and Visual Graph systems aimed at supporting information visualisation on the base of hierarchical graph models.

Super-elite plasma rings and the orbits of planets and satellites isomorphic to the orbits of electrons in the Bohr's model of the hydrogen atom

NASA Astrophysics Data System (ADS)

Rabinovich, B. I.

2007-10-01

This paper continues the series of papers [1 5] and generalizes the previous results to a proto-ring of magnetized plasma whose density decreases in the radial direction. The problem of quantization of the sector and orbital velocities, and of the radii and periods of revolution of elite plasma rings is considered. A new concept of super-elite rings is introduced. Their isomorphism with the orbits of the planets and planetary satellites in the Solar System is proved. This isomorphism also extends to the orbits of electrons in the Bohr’s model of the hydrogen atom.

A square-plate piezoelectric linear motor operating in two orthogonal and isomorphic face-diagonal-bending modes.

PubMed

Ci, Penghong; Chen, Zhijiang; Liu, Guoxi; Dong, Shuxiang

2014-01-01

We report a piezoelectric linear motor made of a single Pb(Zr,Ti)O3 square-plate, which operates in two orthogonal and isomorphic face-diagonal-bending modes to produce precision linear motion. A 15 × 15 × 2 mm prototype was fabricated, and the motor generated a driving force of up to 1.8 N and a speed of 170 mm/s under an applied voltage of 100 Vpp at the resonance frequency of 136.5 kHz. The motor shows such advantages as large driving force under relatively low driving voltage, simple structure, and stable motion because of its isomorphic face-diagonal-bending mode.

An Isomorphism between Lyapunov Exponents and Shannon's Channel Capacity

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

Friedland, Gerald; Metere, Alfredo

We demonstrate that discrete Lyapunov exponents are isomorphic to numeric overflows of the capacity of an arbitrary noiseless and memoryless channel in a Shannon communication model with feedback. The isomorphism allows the understanding of Lyapunov exponents in terms of Information Theory, rather than the traditional definitions in chaos theory. The result also implies alternative approaches to the calculation of related quantities, such as the Kolmogorov Sinai entropy which has been linked to thermodynamic entropy. This work provides a bridge between fundamental physics and information theory. It suggests, among other things, that machine learning and other information theory methods can bemore » employed at the core of physics simulations.« less

Multi-Centrality Graph Spectral Decompositions and Their Application to Cyber Intrusion Detection

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

Chen, Pin-Yu; Choudhury, Sutanay; Hero, Alfred

Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful. Distinct from previous graph decomposition approaches based on subspace projection of a single topological feature, e.g., the centered graph adjacency matrix (graph Laplacian), we propose spectral decomposition approaches to graph PCA and graph dictionary learning that integrate multiple features, including graph walk statistics, centrality measures and graph distances to reference nodes. In this paper we propose a new PCA method for single graph analysis, called multi-centrality graph PCA (MC-GPCA), and a new dictionary learning method for ensembles ofmore » graphs, called multi-centrality graph dictionary learning (MC-GDL), both based on spectral decomposition of multi-centrality matrices. As an application to cyber intrusion detection, MC-GPCA can be an effective indicator of anomalous connectivity pattern and MC-GDL can provide discriminative basis for attack classification.« less

Compacting de Bruijn graphs from sequencing data quickly and in low memory.

PubMed

Chikhi, Rayan; Limasset, Antoine; Medvedev, Paul

2016-06-15

As the quantity of data per sequencing experiment increases, the challenges of fragment assembly are becoming increasingly computational. The de Bruijn graph is a widely used data structure in fragment assembly algorithms, used to represent the information from a set of reads. Compaction is an important data reduction step in most de Bruijn graph based algorithms where long simple paths are compacted into single vertices. Compaction has recently become the bottleneck in assembly pipelines, and improving its running time and memory usage is an important problem. We present an algorithm and a tool bcalm 2 for the compaction of de Bruijn graphs. bcalm 2 is a parallel algorithm that distributes the input based on a minimizer hashing technique, allowing for good balance of memory usage throughout its execution. For human sequencing data, bcalm 2 reduces the computational burden of compacting the de Bruijn graph to roughly an hour and 3 GB of memory. We also applied bcalm 2 to the 22 Gbp loblolly pine and 20 Gbp white spruce sequencing datasets. Compacted graphs were constructed from raw reads in less than 2 days and 40 GB of memory on a single machine. Hence, bcalm 2 is at least an order of magnitude more efficient than other available methods. Source code of bcalm 2 is freely available at: https://github.com/GATB/bcalm rayan.chikhi@univ-lille1.fr. © The Author 2016. Published by Oxford University Press.

Effectiveness of metaphoric facilitation techniques in a challenge course program on the empowerment of women participants

Treesearch

Penny A. James; Lynn Anderson; Anderson Young

2007-01-01

Research has shown support for the efficacy of differing processing techniques, particularly isomorphic framing. Feminist practitioners contend this methodology disempowers participants. Proponents argue this could result only from improper implementation. This experiment employed a facilitation technique (control, derived, isomorphic) between subjects design with time...

Testing after Worked Example Study Does Not Enhance Delayed Problem-Solving Performance Compared to Restudy

ERIC Educational Resources Information Center

van Gog, Tamara; Kester, Liesbeth; Dirkx, Kim; Hoogerheide, Vincent; Boerboom, Joris; Verkoeijen, Peter P. J. L.

2015-01-01

Four experiments investigated whether the testing effect also applies to the acquisition of problem-solving skills from worked examples. Experiment 1 (n?=?120) showed no beneficial effects of testing consisting of "isomorphic" problem solving or "example recall" on final test performance, which consisted of isomorphic problem…

Parallel Process and Isomorphism: A Model for Decision Making in the Supervisory Triad

ERIC Educational Resources Information Center

Koltz, Rebecca L.; Odegard, Melissa A.; Feit, Stephen S.; Provost, Kent; Smith, Travis

2012-01-01

Parallel process and isomorphism are two supervisory concepts that are often discussed independently but rarely discussed in connection with each other. These two concepts, philosophically, have different historical roots, as well as different implications for interventions with regard to the supervisory triad. The authors examine the difference…

Surface-region context in optimal multi-object graph-based segmentation: robust delineation of pulmonary tumors.

PubMed

Song, Qi; Chen, Mingqing; Bai, Junjie; Sonka, Milan; Wu, Xiaodong

2011-01-01

Multi-object segmentation with mutual interaction is a challenging task in medical image analysis. We report a novel solution to a segmentation problem, in which target objects of arbitrary shape mutually interact with terrain-like surfaces, which widely exists in the medical imaging field. The approach incorporates context information used during simultaneous segmentation of multiple objects. The object-surface interaction information is encoded by adding weighted inter-graph arcs to our graph model. A globally optimal solution is achieved by solving a single maximum flow problem in a low-order polynomial time. The performance of the method was evaluated in robust delineation of lung tumors in megavoltage cone-beam CT images in comparison with an expert-defined independent standard. The evaluation showed that our method generated highly accurate tumor segmentations. Compared with the conventional graph-cut method, our new approach provided significantly better results (p < 0.001). The Dice coefficient obtained by the conventional graph-cut approach (0.76 +/- 0.10) was improved to 0.84 +/- 0.05 when employing our new method for pulmonary tumor segmentation.

Graph pyramids for protein function prediction

PubMed Central

2015-01-01

Background Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Methods Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Results Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data. PMID:26044522

Graph pyramids for protein function prediction.

PubMed

Sandhan, Tushar; Yoo, Youngjun; Choi, Jin; Kim, Sun

2015-01-01

Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data.

Can Comparison of Contrastive Examples Facilitate Graph Understanding?

ERIC Educational Resources Information Center

Smith, Linsey A.; Gentner, Dedre

2011-01-01

The authors explore the role of comparison in improving graph fluency. The ability to use graphs fluently is crucial for STEM achievement, but graphs are challenging to interpret and produce because they often involve integration of multiple variables, continuous change in variables over time, and omission of certain details in order to highlight…

Graphs in Kinematics--A Need for Adherence to Principles of Algebraic Functions

ERIC Educational Resources Information Center

Sokolowski, Andrzej

2017-01-01

Graphs in physics are central to the analysis of phenomena and to learning about a system's behavior. The ways students handle graphs are frequently researched. Students' misconceptions are highlighted, and methods of improvement suggested. While kinematics graphs are to represent a real motion, they are also algebraic entities that must satisfy…

Distributed Sensing and Processing: A Graphical Model Approach

DTIC Science & Technology

2005-11-30

that Ramanujan graph toplogies maximize the convergence rate of distributed detection consensus algorithms, improving over three orders of...small world type network designs. 14. SUBJECT TERMS Ramanujan graphs, sensor network topology, sensor network...that Ramanujan graphs, for which there are explicit algebraic constructions, have large eigenratios, converging much faster than structured graphs

MPI Runtime Error Detection with MUST: Advances in Deadlock Detection

DOE PAGES

Hilbrich, Tobias; Protze, Joachim; Schulz, Martin; ...

2013-01-01

The widely used Message Passing Interface (MPI) is complex and rich. As a result, application developers require automated tools to avoid and to detect MPI programming errors. We present the Marmot Umpire Scalable Tool (MUST) that detects such errors with significantly increased scalability. We present improvements to our graph-based deadlock detection approach for MPI, which cover future MPI extensions. Our enhancements also check complex MPI constructs that no previous graph-based detection approach handled correctly. Finally, we present optimizations for the processing of MPI operations that reduce runtime deadlock detection overheads. Existing approaches often require ( p ) analysis time permore » MPI operation, for p processes. We empirically observe that our improvements lead to sub-linear or better analysis time per operation for a wide range of real world applications.« less

A Graph Based Interface for Representing Volume Visualization Results

NASA Technical Reports Server (NTRS)

Patten, James M.; Ma, Kwan-Liu

1998-01-01

This paper discusses a graph based user interface for representing the results of the volume visualization process. As images are rendered, they are connected to other images in a graph based on their rendering parameters. The user can take advantage of the information in this graph to understand how certain rendering parameter changes affect a dataset, making the visualization process more efficient. Because the graph contains more information than is contained in an unstructured history of images, the image graph is also helpful for collaborative visualization and animation.

An Improved Graph Model for Conflict Resolution Based on Option Prioritization and Its Application

PubMed Central

Yin, Kedong; Li, Xuemei

2017-01-01

In order to quantitatively depict differences regarding the preferences of decision makers for different states, a score function is proposed. As a foundation, coalition motivation and real-coalition analysis are discussed when external circumstance or opportunity costs are considering. On the basis of a confidence-level function, we establish the score function using a “preference tree”. We not only measure the preference for each state, but we also build a collation improvement function to measure coalition motivation and to construct a coordinate system in which to analyze real-coalition stability. All of these developments enhance the applicability of the graph model for conflict resolution (GMCR). Finally, an improved GMCR is applied in the “Changzhou Conflict” to demonstrate how it can be conveniently utilized in practice. PMID:29077049

An Improved Graph Model for Conflict Resolution Based on Option Prioritization and Its Application.

PubMed

Yin, Kedong; Yu, Li; Li, Xuemei

2017-10-27

In order to quantitatively depict differences regarding the preferences of decision makers for different states, a score function is proposed. As a foundation, coalition motivation and real-coalition analysis are discussed when external circumstance or opportunity costs are considering. On the basis of a confidence-level function, we establish the score function using a "preference tree". We not only measure the preference for each state, but we also build a collation improvement function to measure coalition motivation and to construct a coordinate system in which to analyze real-coalition stability. All of these developments enhance the applicability of the graph model for conflict resolution (GMCR). Finally, an improved GMCR is applied in the "Changzhou Conflict" to demonstrate how it can be conveniently utilized in practice.

Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning.

PubMed

Dong, Pei; Guo, Yangrong; Gao, Yue; Liang, Peipeng; Shi, Yonghong; Wang, Qian; Shen, Dinggang; Wu, Guorong

2016-10-01

Accurate segmentation of brainstem nuclei (red nucleus and substantia nigra) is very important in various neuroimaging applications such as deep brain stimulation and the investigation of imaging biomarkers for Parkinson's disease (PD). Due to iron deposition during aging, image contrast in the brainstem is very low in Magnetic Resonance (MR) images. Hence, the ambiguity of patch-wise similarity makes the recently successful multi-atlas patch-based label fusion methods have difficulty to perform as competitive as segmenting cortical and sub-cortical regions from MR images. To address this challenge, we propose a novel multi-atlas brainstem nuclei segmentation method using deep hyper-graph learning. Specifically, we achieve this goal in three-fold. First , we employ hyper-graph to combine the advantage of maintaining spatial coherence from graph-based segmentation approaches and the benefit of harnessing population priors from multi-atlas based framework. Second , besides using low-level image appearance, we also extract high-level context features to measure the complex patch-wise relationship. Since the context features are calculated on a tentatively estimated label probability map, we eventually turn our hyper-graph learning based label propagation into a deep and self-refining model. Third , since anatomical labels on some voxels (usually located in uniform regions) can be identified much more reliably than other voxels (usually located at the boundary between two regions), we allow these reliable voxels to propagate their labels to the nearby difficult-to-label voxels. Such hierarchical strategy makes our proposed label fusion method deep and dynamic. We evaluate our proposed label fusion method in segmenting substantia nigra (SN) and red nucleus (RN) from 3.0 T MR images, where our proposed method achieves significant improvement over the state-of-the-art label fusion methods.

Automatic segmentation of colon glands using object-graphs.

PubMed

Gunduz-Demir, Cigdem; Kandemir, Melih; Tosun, Akif Burak; Sokmensuer, Cenk

2010-02-01

Gland segmentation is an important step to automate the analysis of biopsies that contain glandular structures. However, this remains a challenging problem as the variation in staining, fixation, and sectioning procedures lead to a considerable amount of artifacts and variances in tissue sections, which may result in huge variances in gland appearances. In this work, we report a new approach for gland segmentation. This approach decomposes the tissue image into a set of primitive objects and segments glands making use of the organizational properties of these objects, which are quantified with the definition of object-graphs. As opposed to the previous literature, the proposed approach employs the object-based information for the gland segmentation problem, instead of using the pixel-based information alone. Working with the images of colon tissues, our experiments demonstrate that the proposed object-graph approach yields high segmentation accuracies for the training and test sets and significantly improves the segmentation performance of its pixel-based counterparts. The experiments also show that the object-based structure of the proposed approach provides more tolerance to artifacts and variances in tissues.

The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cooke, diane J.; Ketkar, Nikhil; Aksoy, Selim

2008-01-01

Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

Developing and evaluating Quilts for the depiction of large layered graphs.

PubMed

Bae, Juhee; Watson, Ben

2011-12-01

Traditional layered graph depictions such as flow charts are in wide use. Yet as graphs grow more complex, these depictions can become difficult to understand. Quilts are matrix-based depictions for layered graphs designed to address this problem. In this research, we first improve Quilts by developing three design alternatives, and then compare the best of these alternatives to better-known node-link and matrix depictions. A primary weakness in Quilts is their depiction of skip links, links that do not simply connect to a succeeding layer. Therefore in our first study, we compare Quilts using color-only, text-only, and mixed (color and text) skip link depictions, finding that path finding with the color-only depiction is significantly slower and less accurate, and that in certain cases, the mixed depiction offers an advantage over the text-only depiction. In our second study, we compare Quilts using the mixed depiction to node-link diagrams and centered matrices. Overall results show that users can find paths through graphs significantly faster with Quilts (46.6 secs) than with node-link (58.3 secs) or matrix (71.2 secs) diagrams. This speed advantage is still greater in large graphs (e.g. in 200 node graphs, 55.4 secs vs. 71.1 secs for node-link and 84.2 secs for matrix depictions). © 2011 IEEE

Reinventing the Concepts of Group and Isomorphism: The Case of Jessica and Sandra

ERIC Educational Resources Information Center

Larsen, Sean

2009-01-01

The purpose of this paper is to describe the process by which a pair of undergraduate students, participating in a teaching experiment, reinvented (with guidance) the concepts of group and isomorphism beginning with an exploration of the symmetries of an equilateral triangle. The intent of this description is to highlight some important insights…

A Case of Mimetic Isomorphism: A Short-Cut to Increasing Loyalty to Academia

ERIC Educational Resources Information Center

Orkodashvili, Mariam

2008-01-01

The paper discusses the process of shortening career path to leadership positions in academia that could serve as an example of mimetic isomorphism, where university tries to apply business-like quick result-oriented strategies. This strategy incentivizes young faculty to stay in universities and keep loyalty to academia. This process could also…

Children's Construction of Mathematical Knowledge in Solving Novel Isomorphic Problems in Concrete and Written Form.

ERIC Educational Resources Information Center

English, Lyn D.

1996-01-01

Presents case study data of low- and high-achieving nine-year olds focusing on construction and analogical transfer of mathematical knowledge during novel problem solving, as reflected in strategies for dealing with isomorphic combinatorial problems presented in hands-on and written form. Results showed that achievement level does not predict…

Are Phonological Representations of Printed and Spoken Language Isomorphic? Evidence from the Restrictions on Unattested Onsets

ERIC Educational Resources Information Center

Berent, Iris

2008-01-01

Are the phonological representations of printed and spoken words isomorphic? This question is addressed by investigating the restrictions on onsets. Cross-linguistic research suggests that onsets of rising sonority are preferred to sonority plateaus, which, in turn, are preferred to sonority falls (e.g., bnif, bdif, lbif). Of interest is whether…

GraphReduce: Processing Large-Scale Graphs on Accelerator-Based Systems

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

Sengupta, Dipanjan; Song, Shuaiwen; Agarwal, Kapil

2015-11-15

Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host andmore » device.« less

Reflections on High School Students' Graphing Skills and Their Conceptual Understanding of Drawing Chemistry Graphs

ERIC Educational Resources Information Center

Gültepe, Nejla

2016-01-01

Graphing subjects in chemistry has been used to provide alternatives to verbal and algorithmic descriptions of a subject by handing students another way of improving their manipulation of concepts. Teachers should therefore know the level of students' graphing skills. Studies have identified that students have difficulty making connections with…

Software reliability studies

NASA Technical Reports Server (NTRS)

Hoppa, Mary Ann; Wilson, Larry W.

1994-01-01

There are many software reliability models which try to predict future performance of software based on data generated by the debugging process. Our research has shown that by improving the quality of the data one can greatly improve the predictions. We are working on methodologies which control some of the randomness inherent in the standard data generation processes in order to improve the accuracy of predictions. Our contribution is twofold in that we describe an experimental methodology using a data structure called the debugging graph and apply this methodology to assess the robustness of existing models. The debugging graph is used to analyze the effects of various fault recovery orders on the predictive accuracy of several well-known software reliability algorithms. We found that, along a particular debugging path in the graph, the predictive performance of different models can vary greatly. Similarly, just because a model 'fits' a given path's data well does not guarantee that the model would perform well on a different path. Further we observed bug interactions and noted their potential effects on the predictive process. We saw that not only do different faults fail at different rates, but that those rates can be affected by the particular debugging stage at which the rates are evaluated. Based on our experiment, we conjecture that the accuracy of a reliability prediction is affected by the fault recovery order as well as by fault interaction.

A Hybrid Task Graph Scheduler for High Performance Image Processing Workflows.

PubMed

Blattner, Timothy; Keyrouz, Walid; Bhattacharyya, Shuvra S; Halem, Milton; Brady, Mary

2017-12-01

Designing applications for scalability is key to improving their performance in hybrid and cluster computing. Scheduling code to utilize parallelism is difficult, particularly when dealing with data dependencies, memory management, data motion, and processor occupancy. The Hybrid Task Graph Scheduler (HTGS) improves programmer productivity when implementing hybrid workflows for multi-core and multi-GPU systems. The Hybrid Task Graph Scheduler (HTGS) is an abstract execution model, framework, and API that increases programmer productivity when implementing hybrid workflows for such systems. HTGS manages dependencies between tasks, represents CPU and GPU memories independently, overlaps computations with disk I/O and memory transfers, keeps multiple GPUs occupied, and uses all available compute resources. Through these abstractions, data motion and memory are explicit; this makes data locality decisions more accessible. To demonstrate the HTGS application program interface (API), we present implementations of two example algorithms: (1) a matrix multiplication that shows how easily task graphs can be used; and (2) a hybrid implementation of microscopy image stitching that reduces code size by ≈ 43% compared to a manually coded hybrid workflow implementation and showcases the minimal overhead of task graphs in HTGS. Both of the HTGS-based implementations show good performance. In image stitching the HTGS implementation achieves similar performance to the hybrid workflow implementation. Matrix multiplication with HTGS achieves 1.3× and 1.8× speedup over the multi-threaded OpenBLAS library for 16k × 16k and 32k × 32k size matrices, respectively.

SAGE: String-overlap Assembly of GEnomes.

PubMed

Ilie, Lucian; Haider, Bahlul; Molnar, Michael; Solis-Oba, Roberto

2014-09-15

De novo genome assembly of next-generation sequencing data is one of the most important current problems in bioinformatics, essential in many biological applications. In spite of significant amount of work in this area, better solutions are still very much needed. We present a new program, SAGE, for de novo genome assembly. As opposed to most assemblers, which are de Bruijn graph based, SAGE uses the string-overlap graph. SAGE builds upon great existing work on string-overlap graph and maximum likelihood assembly, bringing an important number of new ideas, such as the efficient computation of the transitive reduction of the string overlap graph, the use of (generalized) edge multiplicity statistics for more accurate estimation of read copy counts, and the improved use of mate pairs and min-cost flow for supporting edge merging. The assemblies produced by SAGE for several short and medium-size genomes compared favourably with those of existing leading assemblers. SAGE benefits from innovations in almost every aspect of the assembly process: error correction of input reads, string-overlap graph construction, read copy counts estimation, overlap graph analysis and reduction, contig extraction, and scaffolding. We hope that these new ideas will help advance the current state-of-the-art in an essential area of research in genomics.

Word sense disambiguation in the clinical domain: a comparison of knowledge-rich and knowledge-poor unsupervised methods

PubMed Central

Chasin, Rachel; Rumshisky, Anna; Uzuner, Ozlem; Szolovits, Peter

2014-01-01

Objective To evaluate state-of-the-art unsupervised methods on the word sense disambiguation (WSD) task in the clinical domain. In particular, to compare graph-based approaches relying on a clinical knowledge base with bottom-up topic-modeling-based approaches. We investigate several enhancements to the topic-modeling techniques that use domain-specific knowledge sources. Materials and methods The graph-based methods use variations of PageRank and distance-based similarity metrics, operating over the Unified Medical Language System (UMLS). Topic-modeling methods use unlabeled data from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) database to derive models for each ambiguous word. We investigate the impact of using different linguistic features for topic models, including UMLS-based and syntactic features. We use a sense-tagged clinical dataset from the Mayo Clinic for evaluation. Results The topic-modeling methods achieve 66.9% accuracy on a subset of the Mayo Clinic's data, while the graph-based methods only reach the 40–50% range, with a most-frequent-sense baseline of 56.5%. Features derived from the UMLS semantic type and concept hierarchies do not produce a gain over bag-of-words features in the topic models, but identifying phrases from UMLS and using syntax does help. Discussion Although topic models outperform graph-based methods, semantic features derived from the UMLS prove too noisy to improve performance beyond bag-of-words. Conclusions Topic modeling for WSD provides superior results in the clinical domain; however, integration of knowledge remains to be effectively exploited. PMID:24441986

The Edge-Disjoint Path Problem on Random Graphs by Message-Passing.

PubMed

Altarelli, Fabrizio; Braunstein, Alfredo; Dall'Asta, Luca; De Bacco, Caterina; Franz, Silvio

2015-01-01

We present a message-passing algorithm to solve a series of edge-disjoint path problems on graphs based on the zero-temperature cavity equations. Edge-disjoint paths problems are important in the general context of routing, that can be defined by incorporating under a unique framework both traffic optimization and total path length minimization. The computation of the cavity equations can be performed efficiently by exploiting a mapping of a generalized edge-disjoint path problem on a star graph onto a weighted maximum matching problem. We perform extensive numerical simulations on random graphs of various types to test the performance both in terms of path length minimization and maximization of the number of accommodated paths. In addition, we test the performance on benchmark instances on various graphs by comparison with state-of-the-art algorithms and results found in the literature. Our message-passing algorithm always outperforms the others in terms of the number of accommodated paths when considering non trivial instances (otherwise it gives the same trivial results). Remarkably, the largest improvement in performance with respect to the other methods employed is found in the case of benchmarks with meshes, where the validity hypothesis behind message-passing is expected to worsen. In these cases, even though the exact message-passing equations do not converge, by introducing a reinforcement parameter to force convergence towards a sub optimal solution, we were able to always outperform the other algorithms with a peak of 27% performance improvement in terms of accommodated paths. On random graphs, we numerically observe two separated regimes: one in which all paths can be accommodated and one in which this is not possible. We also investigate the behavior of both the number of paths to be accommodated and their minimum total length.

The Edge-Disjoint Path Problem on Random Graphs by Message-Passing

PubMed Central

2015-01-01

We present a message-passing algorithm to solve a series of edge-disjoint path problems on graphs based on the zero-temperature cavity equations. Edge-disjoint paths problems are important in the general context of routing, that can be defined by incorporating under a unique framework both traffic optimization and total path length minimization. The computation of the cavity equations can be performed efficiently by exploiting a mapping of a generalized edge-disjoint path problem on a star graph onto a weighted maximum matching problem. We perform extensive numerical simulations on random graphs of various types to test the performance both in terms of path length minimization and maximization of the number of accommodated paths. In addition, we test the performance on benchmark instances on various graphs by comparison with state-of-the-art algorithms and results found in the literature. Our message-passing algorithm always outperforms the others in terms of the number of accommodated paths when considering non trivial instances (otherwise it gives the same trivial results). Remarkably, the largest improvement in performance with respect to the other methods employed is found in the case of benchmarks with meshes, where the validity hypothesis behind message-passing is expected to worsen. In these cases, even though the exact message-passing equations do not converge, by introducing a reinforcement parameter to force convergence towards a sub optimal solution, we were able to always outperform the other algorithms with a peak of 27% performance improvement in terms of accommodated paths. On random graphs, we numerically observe two separated regimes: one in which all paths can be accommodated and one in which this is not possible. We also investigate the behavior of both the number of paths to be accommodated and their minimum total length. PMID:26710102

GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics1

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

Simmhan, Yogesh; Kumbhare, Alok; Wickramaarachchi, Charith

2014-08-25

Large scale graph processing is a major research area for Big Data exploration. Vertex centric programming models like Pregel are gaining traction due to their simple abstraction that allows for scalable execution on distributed systems naturally. However, there are limitations to this approach which cause vertex centric algorithms to under-perform due to poor compute to communication overhead ratio and slow convergence of iterative superstep. In this paper we introduce GoFFish a scalable sub-graph centric framework co-designed with a distributed persistent graph storage for large scale graph analytics on commodity clusters. We introduce a sub-graph centric programming abstraction that combines themore » scalability of a vertex centric approach with the flexibility of shared memory sub-graph computation. We map Connected Components, SSSP and PageRank algorithms to this model to illustrate its flexibility. Further, we empirically analyze GoFFish using several real world graphs and demonstrate its significant performance improvement, orders of magnitude in some cases, compared to Apache Giraph, the leading open source vertex centric implementation. We map Connected Components, SSSP and PageRank algorithms to this model to illustrate its flexibility. Further, we empirically analyze GoFFish using several real world graphs and demonstrate its significant performance improvement, orders of magnitude in some cases, compared to Apache Giraph, the leading open source vertex centric implementation.« less

GraphCrunch 2: Software tool for network modeling, alignment and clustering.

PubMed

Kuchaiev, Oleksii; Stevanović, Aleksandar; Hayes, Wayne; Pržulj, Nataša

2011-01-19

Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI) data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL") for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other existing tool. Finally, GraphCruch 2 implements an algorithm for clustering nodes within a network based solely on their topological similarities. Using GraphCrunch 2, we demonstrate that eukaryotic and viral PPI networks may belong to different graph model families and show that topology-based clustering can reveal important functional similarities between proteins within yeast and human PPI networks. GraphCrunch 2 is a software tool that implements the latest research on biological network analysis. It parallelizes computationally intensive tasks to fully utilize the potential of modern multi-core CPUs. It is open-source and freely available for research use. It runs under the Windows and Linux platforms.

Geographic information modeling of Econet of Northwestern Federal District territory on graph theory basis

NASA Astrophysics Data System (ADS)

Kopylova, N. S.; Bykova, A. A.; Beregovoy, D. N.

2018-05-01

Based on the landscape-geographical approach, a structural and logical scheme for the Northwestern Federal District Econet has been developed, which can be integrated into the federal and world ecological network in order to improve the environmental infrastructure of the region. The method of Northwestern Federal District Econet organization on the basis of graph theory by means of the Quantum GIS geographic information system is proposed as an effective mean of preserving and recreating the unique biodiversity of landscapes, regulation of the sphere of environmental protection.

GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems

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

Sengupta, Dipanjan; Agarwal, Kapil; Song, Shuaiwen

2015-09-30

Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of both edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the hostmore » and the device.« less

Optimal seasonal schedules and the relative dominance of heteromorphic and isomorphic life cycles in macroalgae.

PubMed

Bessho, Kazuhiro; Iwasa, Yoh

2010-11-21

Marine macroalgae (seaweed) show diverse life cycles. Species with a heteromorphic life cycle have a large multicellular algal body in one generation but have a very small body in the second generation of the same year. In contrast, the diploid and haploid life forms of isomorphic species have similar morphology, and these species often have more than two generations in a year. Here, we first study the optimal life cycle schedule of marine macroalgae when daily mortality changes seasonally, and then we discuss the conditions for coexistence and relative dominance of different life cycles. According to the optimal life cycle schedule, heteromorphic species tend to have a generation with a large algal body when mortality is low, and a microscopic-sized generation when mortality is high. In contrast, isomorphic species tend to mature when body size reaches a threshold value that is the same for different generations. We then examine the coexistence of the two life cycles when growth rate decreases with biomass. The model predicts that (1) at high latitudes (i.e., in strongly seasonal environments), heteromorphic species are likely to dominate over isomorphic species, and (2) species with a heteromorphic life cycle should dominate in the supratidal and upper intertidal zones where macroalgae tend to suffer high mortality, and also in the subtidal zone, where mortality is low, whereas isomorphic species are likely to be more successful when mortality is intermediate. These predictions are consistent with the observed distribution patterns of the two life cycles in macroalgae. Copyright © 2010 Elsevier Ltd. All rights reserved.

Delay discounting of food by rhesus monkeys: Cocaine and food choice in isomorphic and allomorphic situations.

PubMed

Huskinson, Sally L; Woolverton, William L; Green, Leonard; Myerson, Joel; Freeman, Kevin B

2015-06-01

Research on delay discounting has focused largely on nondrug reinforcers in an isomorphic context in which choice is between alternatives that involve the same type of reinforcer. Less often, delay discounting has been studied with drug reinforcers in a more ecologically valid allomorphic context where choice is between alternatives involving different types of reinforcers. The present experiment is the first to examine discounting of drug and nondrug reinforcers in both isomorphic and allomorphic situations using a theoretical model (i.e., the hyperbolic discounting function) that allows for comparisons of discounting rates between reinforcer types and amounts. The goal of the current experiment was to examine discounting of a delayed, nondrug reinforcer (food) by male rhesus monkeys when the immediate alternative was either food (isomorphic situation) or cocaine (allomorphic situation). In addition, we sought to determine whether there was a magnitude effect with delayed food in the allomorphic situation. Choice of immediate food and immediate cocaine increased with amount and dose, respectively. Choice functions for immediate food and cocaine generally shifted leftward as delay increased. Compared to isomorphic situations in which food was the immediate alternative, delayed food was discounted more steeply in allomorphic situations where cocaine was the immediate alternative. Notably, discounting was not affected by the magnitude of the delayed reinforcer. These data indicate that how steeply a delayed nondrug reinforcer is discounted may depend more on the qualitative characteristics of the immediate reinforcer and less on the magnitude of the delayed one. (c) 2015 APA, all rights reserved).

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

Improving the Accuracy of Attribute Extraction using the Relatedness between Attribute Values

NASA Astrophysics Data System (ADS)

Bollegala, Danushka; Tani, Naoki; Ishizuka, Mitsuru

Extracting attribute-values related to entities from web texts is an important step in numerous web related tasks such as information retrieval, information extraction, and entity disambiguation (namesake disambiguation). For example, for a search query that contains a personal name, we can not only return documents that contain that personal name, but if we have attribute-values such as the organization for which that person works, we can also suggest documents that contain information related to that organization, thereby improving the user's search experience. Despite numerous potential applications of attribute extraction, it remains a challenging task due to the inherent noise in web data -- often a single web page contains multiple entities and attributes. We propose a graph-based approach to select the correct attribute-values from a set of candidate attribute-values extracted for a particular entity. First, we build an undirected weighted graph in which, attribute-values are represented by nodes, and the edge that connects two nodes in the graph represents the degree of relatedness between the corresponding attribute-values. Next, we find the maximum spanning tree of this graph that connects exactly one attribute-value for each attribute-type. The proposed method outperforms previously proposed attribute extraction methods on a dataset that contains 5000 web pages.

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.

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.

Improving Child Outcomes with Data-Based Decision Making: Graphing Data

ERIC Educational Resources Information Center

Hojnoski, Robin L.; Gischlar, Karen L.; Missall, Kristen N.

2009-01-01

Child performance data collected frequently in natural settings are important for monitoring progress toward short-term and long-term goals and are an essential component of instruction designed to improve outcomes for children. Organizing child performance data into graphic displays can promote the systematic use of data in educational decision…

Mathematical foundations of the GraphBLAS

DOE PAGES

Kepner, Jeremy; Aaltonen, Peter; Bader, David; ...

2016-12-01

The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix-based graph algorithms to the broadest possible audience. Mathematically, the GraphBLAS defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This study provides an introduction to the mathematics of the GraphBLAS. Graphs represent connections between vertices with edges. Matrices can represent a wide range of graphs using adjacency matrices or incidence matrices. Adjacency matrices are often easier to analyze while incidence matrices are often better for representing data. Fortunately, themore » two are easily connected by matrix multiplication. A key feature of matrix mathematics is that a very small number of matrix operations can be used to manipulate a very wide range of graphs. This composability of a small number of operations is the foundation of the GraphBLAS. A standard such as the GraphBLAS can only be effective if it has low performance overhead. Finally, performance measurements of prototype GraphBLAS implementations indicate that the overhead is low.« less

Lung tumor segmentation in PET images using graph cuts.

PubMed

Ballangan, Cherry; Wang, Xiuying; Fulham, Michael; Eberl, Stefan; Feng, David Dagan

2013-03-01

The aim of segmentation of tumor regions in positron emission tomography (PET) is to provide more accurate measurements of tumor size and extension into adjacent structures, than is possible with visual assessment alone and hence improve patient management decisions. We propose a segmentation energy function for the graph cuts technique to improve lung tumor segmentation with PET. Our segmentation energy is based on an analysis of the tumor voxels in PET images combined with a standardized uptake value (SUV) cost function and a monotonic downhill SUV feature. The monotonic downhill feature avoids segmentation leakage into surrounding tissues with similar or higher PET tracer uptake than the tumor and the SUV cost function improves the boundary definition and also addresses situations where the lung tumor is heterogeneous. We evaluated the method in 42 clinical PET volumes from patients with non-small cell lung cancer (NSCLC). Our method improves segmentation and performs better than region growing approaches, the watershed technique, fuzzy-c-means, region-based active contour and tumor customized downhill. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

A Categorification of the Crystal Isomorphism B 1,1 B + B(Lambda i) = B(Lambdasigma (i) and a Graphical Calculus for the Shifted Symmetric Functions

NASA Astrophysics Data System (ADS)

Kvinge, Henry

We prove two results at the intersection of Lie theory and the representation theory of symmetric groups, Hecke algebras, and their generalizations. The first is a categorification of the crystal isomorphism B. (1,1) tensor B1,1 ⊕ B(Lambdai ) ≅ B(Lambdasigma (i)). Here B(Lambdai and B(Lambda sigma(i)) are two affine type highest weight crystals of weight Lambdai and Lambdasigma (i) respectively, sigma is a specific map from the Dynkin indexing set I to itself, and B1,1 is a Kirillov-Reshetikhin crystal. We show that this crystal isomorphism is in fact the shadow of a richer module-theoretic phenomenon in the representation theory of Khovanov-Lauda-Rouquier algebras of classical affine type. Our second result identifies the center EndH'( 1) of Khovanov's Heisenberg category H', as the algebra of shifted symmetric functions Lambda* of Okounkov and Olshanski, i.e. End H'(1) ≅ Lambda*. This isomorphism provides us with a graphical calculus for Lambda*. It also allows us to describe EndH'(1) in terms of the transition and co-transition measure of Kerov and the noncommutative probability spaces of Biane.

Effect of Inoculant Alloy Selection and Particle Size on Efficiency of Isomorphic Inoculation of Ti-Al

PubMed Central

Rouat, Bernard; Daloz, Dominique; Bouzy, Emmanuel

2018-01-01

The process of isomorphic inoculation relies on precise selection of inoculant alloys for a given system. Three alloys, Ti-10Al-25Nb, Ti-25Al-10Ta, and Ti-47Ta (at %) were selected as potential isomorphic inoculants for a Ti-46Al alloy. The binary Ti-Ta alloy selected was found to be ineffective as an inoculant due to its large density difference with the melt, causing the particles to settle. Both ternary alloys were successfully implemented as isomorphic inoculants that decreased the equiaxed grain size and increased the equiaxed fraction in their ingots. The degree of grain refinement obtained was found to be dependent on the number of particles introduced to the melt. Also, more new grains were formed than particles added to the melt. The grains/particle efficiency varied from greater than one to nearly twenty as the size of the particle increased. This is attributed to the breaking up of particles into smaller particles by dissolution in the melt. For a given particle size, Ti-Al-Ta and Ti-Al-Nb particles were found to have a roughly similar grain/particle efficiency. PMID:29693591

Investigating Psychometric Isomorphism for Traditional and Performance-Based Assessment

ERIC Educational Resources Information Center

Fay, Derek M.; Levy, Roy; Mehta, Vandhana

2018-01-01

A common practice in educational assessment is to construct multiple forms of an assessment that consists of tasks with similar psychometric properties. This study utilizes a Bayesian multilevel item response model and descriptive graphical representations to evaluate the psychometric similarity of variations of the same task. These approaches for…

Graph Kernels for Molecular Similarity.

PubMed

Rupp, Matthias; Schneider, Gisbert

2010-04-12

Molecular similarity measures are important for many cheminformatics applications like ligand-based virtual screening and quantitative structure-property relationships. Graph kernels are formal similarity measures defined directly on graphs, such as the (annotated) molecular structure graph. Graph kernels are positive semi-definite functions, i.e., they correspond to inner products. This property makes them suitable for use with kernel-based machine learning algorithms such as support vector machines and Gaussian processes. We review the major types of kernels between graphs (based on random walks, subgraphs, and optimal assignments, respectively), and discuss their advantages, limitations, and successful applications in cheminformatics. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Using Psychometric Technology in Educational Assessment: The Case of a Schema-Based Isomorphic Approach to the Automatic Generation of Quantitative Reasoning Items

ERIC Educational Resources Information Center

Arendasy, Martin; Sommer, Markus

2007-01-01

This article deals with the investigation of the psychometric quality and constructs validity of algebra word problems generated by means of a schema-based version of the automatic min-max approach. Based on review of the research literature in algebra word problem solving and automatic item generation this new approach is introduced as a…

An Ada inference engine for expert systems

NASA Technical Reports Server (NTRS)

Lavallee, David B.

1986-01-01

The purpose is to investigate the feasibility of using Ada for rule-based expert systems with real-time performance requirements. This includes exploring the Ada features which give improved performance to expert systems as well as optimizing the tradeoffs or workarounds that the use of Ada may require. A prototype inference engine was built using Ada, and rule firing rates in excess of 500 per second were demonstrated on a single MC68000 processor. The knowledge base uses a directed acyclic graph to represent production lines. The graph allows the use of AND, OR, and NOT logical operators. The inference engine uses a combination of both forward and backward chaining in order to reach goals as quickly as possible. Future efforts will include additional investigation of multiprocessing to improve performance and creating a user interface allowing rule input in an Ada-like syntax. Investigation of multitasking and alternate knowledge base representations will help to analyze some of the performance issues as they relate to larger problems.

On Effective Graphic Communication of Health Inequality: Considerations for Health Policy Researchers.

PubMed

Asada, Yukiko; Abel, Hannah; Skedgel, Chris; Warner, Grace

2017-12-01

Policy Points: Effective graphs can be a powerful tool in communicating health inequality. The choice of graphs is often based on preferences and familiarity rather than science. According to the literature on graph perception, effective graphs allow human brains to decode visual cues easily. Dot charts are easier to decode than bar charts, and thus they are more effective. Dot charts are a flexible and versatile way to display information about health inequality. Consistent with the health risk communication literature, the captions accompanying health inequality graphs should provide a numerical, explicitly calculated description of health inequality, expressed in absolute and relative terms, from carefully thought-out perspectives. Graphs are an essential tool for communicating health inequality, a key health policy concern. The choice of graphs is often driven by personal preferences and familiarity. Our article is aimed at health policy researchers developing health inequality graphs for policy and scientific audiences and seeks to (1) raise awareness of the effective use of graphs in communicating health inequality; (2) advocate for a particular type of graph (ie, dot charts) to depict health inequality; and (3) suggest key considerations for the captions accompanying health inequality graphs. Using composite review methods, we selected the prevailing recommendations for improving graphs in scientific reporting. To find the origins of these recommendations, we reviewed the literature on graph perception and then applied what we learned to the context of health inequality. In addition, drawing from the numeracy literature in health risk communication, we examined numeric and verbal formats to explain health inequality graphs. Many disciplines offer commonsense recommendations for visually presenting quantitative data. The literature on graph perception, which defines effective graphs as those allowing the easy decoding of visual cues in human brains, shows that with their more accurate and easier-to-decode visual cues, dot charts are more effective than bar charts. Dot charts can flexibly present a large amount of information in limited space. They also can easily accommodate typical health inequality information to describe a health variable (eg, life expectancy) by an inequality domain (eg, income) with domain groups (eg, poor and rich) in a population (eg, Canada) over time periods (eg, 2010 and 2017). The numeracy literature suggests that a health inequality graph's caption should provide a numerical, explicitly calculated description of health inequality expressed in absolute and relative terms, from carefully thought-out perspectives. Given the ubiquity of graphs, the health inequality field should learn from the vibrant multidisciplinary literature how to construct effective graphic communications, especially by considering to use dot charts. © 2017 Milbank Memorial Fund.

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.

Topic Model for Graph Mining.

PubMed

Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Luo, Xiangfeng

2015-12-01

Graph mining has been a popular research area because of its numerous application scenarios. Many unstructured and structured data can be represented as graphs, such as, documents, chemical molecular structures, and images. However, an issue in relation to current research on graphs is that they cannot adequately discover the topics hidden in graph-structured data which can be beneficial for both the unsupervised learning and supervised learning of the graphs. Although topic models have proved to be very successful in discovering latent topics, the standard topic models cannot be directly applied to graph-structured data due to the "bag-of-word" assumption. In this paper, an innovative graph topic model (GTM) is proposed to address this issue, which uses Bernoulli distributions to model the edges between nodes in a graph. It can, therefore, make the edges in a graph contribute to latent topic discovery and further improve the accuracy of the supervised and unsupervised learning of graphs. The experimental results on two different types of graph datasets show that the proposed GTM outperforms the latent Dirichlet allocation on classification by using the unveiled topics of these two models to represent graphs.

GrouseFlocks: steerable exploration of graph hierarchy space.

PubMed

Archambault, Daniel; Munzner, Tamara; Auber, David

2008-01-01

Several previous systems allow users to interactively explore a large input graph through cuts of a superimposed hierarchy. This hierarchy is often created using clustering algorithms or topological features present in the graph. However, many graphs have domain-specific attributes associated with the nodes and edges, which could be used to create many possible hierarchies providing unique views of the input graph. GrouseFlocks is a system for the exploration of this graph hierarchy space. By allowing users to see several different possible hierarchies on the same graph, the system helps users investigate graph hierarchy space instead of a single fixed hierarchy. GrouseFlocks provides a simple set of operations so that users can create and modify their graph hierarchies based on selections. These selections can be made manually or based on patterns in the attribute data provided with the graph. It provides feedback to the user within seconds, allowing interactive exploration of this space.

How accurate are interpretations of curriculum-based measurement progress monitoring data? Visual analysis versus decision rules.

PubMed

Van Norman, Ethan R; Christ, Theodore J

2016-10-01

Curriculum based measurement of oral reading (CBM-R) is used to monitor the effects of academic interventions for individual students. Decisions to continue, modify, or terminate these interventions are made by interpreting time series CBM-R data. Such interpretation is founded upon visual analysis or the application of decision rules. The purpose of this study was to compare the accuracy of visual analysis and decision rules. Visual analysts interpreted 108 CBM-R progress monitoring graphs one of three ways: (a) without graphic aids, (b) with a goal line, or (c) with a goal line and a trend line. Graphs differed along three dimensions, including trend magnitude, variability of observations, and duration of data collection. Automated trend line and data point decision rules were also applied to each graph. Inferential analyses permitted the estimation of the probability of a correct decision (i.e., the student is improving - continue the intervention, or the student is not improving - discontinue the intervention) for each evaluation method as a function of trend magnitude, variability of observations, and duration of data collection. All evaluation methods performed better when students made adequate progress. Visual analysis and decision rules performed similarly when observations were less variable. Results suggest that educators should collect data for more than six weeks, take steps to control measurement error, and visually analyze graphs when data are variable. Implications for practice and research are discussed. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Simple nuclear C*-algebras not isomorphic to their opposites

PubMed Central

Hirshberg, Ilan

2017-01-01

We show that it is consistent with Zermelo–Fraenkel set theory with the axiom of choice (ZFC) that there is a simple nuclear nonseparable C∗-algebra, which is not isomorphic to its opposite algebra. We can furthermore guarantee that this example is an inductive limit of unital copies of the Cuntz algebra O2 or of the canonical anticommutation relations (CAR) algebra. PMID:28559339

Iterative cross section sequence graph for handwritten character segmentation.

PubMed

Dawoud, Amer

2007-08-01

The iterative cross section sequence graph (ICSSG) is an algorithm for handwritten character segmentation. It expands the cross section sequence graph concept by applying it iteratively at equally spaced thresholds. The iterative thresholding reduces the effect of information loss associated with image binarization. ICSSG preserves the characters' skeletal structure by preventing the interference of pixels that causes flooding of adjacent characters' segments. Improving the structural quality of the characters' skeleton facilitates better feature extraction and classification, which improves the overall performance of optical character recognition (OCR). Experimental results showed significant improvements in OCR recognition rates compared to other well-established segmentation algorithms.

A user-friendly, menu-driven, language-free laser characteristics curves graphing program for desk-top IBM PC compatible computers

NASA Technical Reports Server (NTRS)

Klutz, Glenn

1989-01-01

A facility was established that uses collected data and feeds it into mathematical models that generate improved data arrays by correcting for various losses, base line drift, and conversion to unity scaling. These developed data arrays have headers and other identifying information affixed and are subsequently stored in a Laser Materials and Characteristics data base which is accessible to various users. The two part data base: absorption - emission spectra and tabulated data, is developed around twelve laser models. The tabulated section of the data base is divided into several parts: crystalline, optical, mechanical, and thermal properties; aborption and emission spectra information; chemical name and formulas; and miscellaneous. A menu-driven, language-free graphing program will reduce and/or remove the requirement that users become competent FORTRAN programmers and the concomitant requirement that they also spend several days to a few weeks becoming conversant with the GEOGRAF library and sequence of calls and the continual refreshers of both. The work included becoming thoroughly conversant with or at least very familiar with GEOGRAF by GEOCOMP Corp. The development of the graphing program involved trial runs of the various callable library routines on dummy data in order to become familiar with actual implementation and sequencing. This was followed by trial runs with actual data base files and some additional data from current research that was not in the data base but currently needed graphs. After successful runs, with dummy and real data, using actual FORTRAN instructions steps were undertaken to develop the menu-driven language-free implementation of a program which would require the user only know how to use microcomputers. The user would simply be responding to items displayed on the video screen. To assist the user in arriving at the optimum values needed for a specific graph, a paper, and pencil check list was made available to use on the trial runs.

The assembly of two isomorphous coordination compounds based on 1,4-cyclohexanedicarboxylic acid and 2,4-diamino-6-phenyl-1,3,5-triazine

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

Li, Xue-Fei; Wang, Xiao; Lun, Hui-Jie

The compounds [Co(e,a-cis-1,4-chdc)(phdat)]{sub n} (1) and [Cd(e,a-cis-1,4-chdc)(phdat)]{sub n} (2) have been synthesized under hydrothermal method by using 1,4-cyclohexanedicarboxylic acid (1,4-H{sub 2}chdc), 2,4-diamino-6-phenyl-1,3,5-triazine (phdat) as well as CoCl{sub 2}·6H{sub 2}O, CdCl{sub 2}·2.5H{sub 2}O respectively and characterized by IR spectra, X-ray single-crystal diffraction, powder X-ray single-crystal diffraction (PXRD), elemental analyses and thermogravimetric analyses (TGA). The results show the compounds 1 and 2 are isomorphous and exhibit paddle-wheel dinuclear Co{sub 2}(CO{sub 2}){sub 4}/Cd{sub 2}(CO{sub 2}){sub 4} units, which are further connected to 1D chain structures by μ{sub 4}:η{sup 1}:η{sup 1}:η{sup 1}:η{sup 1} 1,4-chdc{sup 2–} ligands and extended into a 3D structures via differentmore » hydrogen bonding and π…π stacking interactions. Furthermore, compound 1 exhibits antiferromagnetic behavior and compound 2 displays luminescent behavior at solid state. - Graphical abstract: Two isomorphous coordination compounds 1–2 have been synthesized and characterized by XRD, IR spectra and TGA etc. Compound 1 and 2 display antiferromagnetic behavior and luminescent behavior respectively. - Highlights: • Two novel polymers based on 1,4-cyclohexanedicarboxylic acid have been synthesized. • Compounds 1 and 2 feather 1D chain structure built up from paddle-wheel SBUs. • The magnetism of 2 is investigated. • The electrochemical property and luminescent property of 1 are investigated.« less

A Factor Graph Approach to Automated GO Annotation

PubMed Central

Spetale, Flavio E.; Tapia, Elizabeth; Krsticevic, Flavia; Roda, Fernando; Bulacio, Pilar

2016-01-01

As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (GO) annotation methods based on hierarchical ensemble classification techniques are particularly interesting when interpretability of annotation results is a main concern. In these methods, raw GO-term predictions computed by base binary classifiers are leveraged by checking the consistency of predefined GO relationships. Both formal leveraging strategies, with main focus on annotation precision, and heuristic alternatives, with main focus on scalability issues, have been described in literature. In this contribution, a factor graph approach to the hierarchical ensemble formulation of the automated GO annotation problem is presented. In this formal framework, a core factor graph is first built based on the GO structure and then enriched to take into account the noisy nature of GO-term predictions. Hence, starting from raw GO-term predictions, an iterative message passing algorithm between nodes of the factor graph is used to compute marginal probabilities of target GO-terms. Evaluations on Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster protein sequences from the GO Molecular Function domain showed significant improvements over competing approaches, even when protein sequences were naively characterized by their physicochemical and secondary structure properties or when loose noisy annotation datasets were considered. Based on these promising results and using Arabidopsis thaliana annotation data, we extend our approach to the identification of most promising molecular function annotations for a set of proteins of unknown function in Solanum lycopersicum. PMID:26771463

A Factor Graph Approach to Automated GO Annotation.

PubMed

Spetale, Flavio E; Tapia, Elizabeth; Krsticevic, Flavia; Roda, Fernando; Bulacio, Pilar

2016-01-01

As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (GO) annotation methods based on hierarchical ensemble classification techniques are particularly interesting when interpretability of annotation results is a main concern. In these methods, raw GO-term predictions computed by base binary classifiers are leveraged by checking the consistency of predefined GO relationships. Both formal leveraging strategies, with main focus on annotation precision, and heuristic alternatives, with main focus on scalability issues, have been described in literature. In this contribution, a factor graph approach to the hierarchical ensemble formulation of the automated GO annotation problem is presented. In this formal framework, a core factor graph is first built based on the GO structure and then enriched to take into account the noisy nature of GO-term predictions. Hence, starting from raw GO-term predictions, an iterative message passing algorithm between nodes of the factor graph is used to compute marginal probabilities of target GO-terms. Evaluations on Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster protein sequences from the GO Molecular Function domain showed significant improvements over competing approaches, even when protein sequences were naively characterized by their physicochemical and secondary structure properties or when loose noisy annotation datasets were considered. Based on these promising results and using Arabidopsis thaliana annotation data, we extend our approach to the identification of most promising molecular function annotations for a set of proteins of unknown function in Solanum lycopersicum.

Biometric sample extraction using Mahalanobis distance in Cardioid based graph using electrocardiogram signals.

PubMed

Sidek, Khairul; Khali, Ibrahim

2012-01-01

In this paper, a person identification mechanism implemented with Cardioid based graph using electrocardiogram (ECG) is presented. Cardioid based graph has given a reasonably good classification accuracy in terms of differentiating between individuals. However, the current feature extraction method using Euclidean distance could be further improved by using Mahalanobis distance measurement producing extracted coefficients which takes into account the correlations of the data set. Identification is then done by applying these extracted features to Radial Basis Function Network. A total of 30 ECG data from MITBIH Normal Sinus Rhythm database (NSRDB) and MITBIH Arrhythmia database (MITDB) were used for development and evaluation purposes. Our experimentation results suggest that the proposed feature extraction method has significantly increased the classification performance of subjects in both databases with accuracy from 97.50% to 99.80% in NSRDB and 96.50% to 99.40% in MITDB. High sensitivity, specificity and positive predictive value of 99.17%, 99.91% and 99.23% for NSRDB and 99.30%, 99.90% and 99.40% for MITDB also validates the proposed method. This result also indicates that the right feature extraction technique plays a vital role in determining the persistency of the classification accuracy for Cardioid based person identification mechanism.

Generalized graph states based on Hadamard matrices

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

Cui, Shawn X.; Yu, Nengkun; Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario N1G 2W1

2015-07-15

Graph states are widely used in quantum information theory, including entanglement theory, quantum error correction, and one-way quantum computing. Graph states have a nice structure related to a certain graph, which is given by either a stabilizer group or an encoding circuit, both can be directly given by the graph. To generalize graph states, whose stabilizer groups are abelian subgroups of the Pauli group, one approach taken is to study non-abelian stabilizers. In this work, we propose to generalize graph states based on the encoding circuit, which is completely determined by the graph and a Hadamard matrix. We study themore » entanglement structures of these generalized graph states and show that they are all maximally mixed locally. We also explore the relationship between the equivalence of Hadamard matrices and local equivalence of the corresponding generalized graph states. This leads to a natural generalization of the Pauli (X, Z) pairs, which characterizes the local symmetries of these generalized graph states. Our approach is also naturally generalized to construct graph quantum codes which are beyond stabilizer codes.« less

Shortest-path constraints for 3D multiobject semiautomatic segmentation via clustering and Graph Cut.

PubMed

Kéchichian, Razmig; Valette, Sébastien; Desvignes, Michel; Prost, Rémy

2013-11-01

We derive shortest-path constraints from graph models of structure adjacency relations and introduce them in a joint centroidal Voronoi image clustering and Graph Cut multiobject semiautomatic segmentation framework. The vicinity prior model thus defined is a piecewise-constant model incurring multiple levels of penalization capturing the spatial configuration of structures in multiobject segmentation. Qualitative and quantitative analyses and comparison with a Potts prior-based approach and our previous contribution on synthetic, simulated, and real medical images show that the vicinity prior allows for the correct segmentation of distinct structures having identical intensity profiles and improves the precision of segmentation boundary placement while being fairly robust to clustering resolution. The clustering approach we take to simplify images prior to segmentation strikes a good balance between boundary adaptivity and cluster compactness criteria furthermore allowing to control the trade-off. Compared with a direct application of segmentation on voxels, the clustering step improves the overall runtime and memory footprint of the segmentation process up to an order of magnitude without compromising the quality of the result.

Differentially Private Frequent Subgraph Mining

PubMed Central

Xu, Shengzhi; Xiong, Li; Cheng, Xiang; Xiao, Ke

2016-01-01

Mining frequent subgraphs from a collection of input graphs is an important topic in data mining research. However, if the input graphs contain sensitive information, releasing frequent subgraphs may pose considerable threats to individual's privacy. In this paper, we study the problem of frequent subgraph mining (FGM) under the rigorous differential privacy model. We introduce a novel differentially private FGM algorithm, which is referred to as DFG. In this algorithm, we first privately identify frequent subgraphs from input graphs, and then compute the noisy support of each identified frequent subgraph. In particular, to privately identify frequent subgraphs, we present a frequent subgraph identification approach which can improve the utility of frequent subgraph identifications through candidates pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support derivation approach, where a series of methods has been proposed to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that our DFG algorithm satisfies ε-differential privacy. Extensive experimental results on real datasets show that the DFG algorithm can privately find frequent subgraphs with high data utility. PMID:27616876

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

Short paths in expander graphs

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

Kleinberg, J.; Rubinfeld, R.

Graph expansion has proved to be a powerful general tool for analyzing the behavior of routing algorithms and the interconnection networks on which they run. We develop new routing algorithms and structural results for bounded-degree expander graphs. Our results are unified by the fact that they are all based upon, and extend, a body of work asserting that expanders are rich in short, disjoint paths. In particular, our work has consequences for the disjoint paths problem, multicommodify flow, and graph minor containment. We show: (i) A greedy algorithm for approximating the maximum disjoint paths problem achieves a polylogarithmic approximation ratiomore » in bounded-degree expanders. Although our algorithm is both deterministic and on-line, its performance guarantee is an improvement over previous bounds in expanders. (ii) For a multicommodily flow problem with arbitrary demands on a bounded-degree expander, there is a (1 + {epsilon})-optimal solution using only flow paths of polylogarithmic length. It follows that the multicommodity flow algorithm of Awerbuch and Leighton runs in nearly linear time per commodity in expanders. Our analysis is based on establishing the following: given edge weights on an expander G, one can increase some of the weights very slightly so the resulting shortest-path metric is smooth - the min-weight path between any pair of nodes uses a polylogarithmic number of edges. (iii) Every bounded-degree expander on n nodes contains every graph with O(n/log{sup O(1)} n) nodes and edges as a minor.« less

Evaluating approaches to find exon chains based on long reads.

PubMed

Kuosmanen, Anna; Norri, Tuukka; Mäkinen, Veli

2018-05-01

Transcript prediction can be modeled as a graph problem where exons are modeled as nodes and reads spanning two or more exons are modeled as exon chains. Pacific Biosciences third-generation sequencing technology produces significantly longer reads than earlier second-generation sequencing technologies, which gives valuable information about longer exon chains in a graph. However, with the high error rates of third-generation sequencing, aligning long reads correctly around the splice sites is a challenging task. Incorrect alignments lead to spurious nodes and arcs in the graph, which in turn lead to incorrect transcript predictions. We survey several approaches to find the exon chains corresponding to long reads in a splicing graph, and experimentally study the performance of these methods using simulated data to allow for sensitivity/precision analysis. Our experiments show that short reads from second-generation sequencing can be used to significantly improve exon chain correctness either by error-correcting the long reads before splicing graph creation, or by using them to create a splicing graph on which the long-read alignments are then projected. We also study the memory and time consumption of various modules, and show that accurate exon chains lead to significantly increased transcript prediction accuracy. The simulated data and in-house scripts used for this article are available at http://www.cs.helsinki.fi/group/gsa/exon-chains/exon-chains-bib.tar.bz2.

Crystallization and preliminary X-ray crystallographic analysis of the cysteine protease inhibitor clitocypin

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

Galeša, Katja; Brzin, Jože; Sabotič, Jerica

2006-01-01

Clitocypin is a cysteine protease inhibitor from the mushroom Clitocybe nebularis. The protein has been purified from natural sources and crystallized in a variety of non-isomorphous forms belonging to monoclinic and triclinic space groups. Clitocypin is a cysteine protease inhibitor from the mushroom Clitocybe nebularis. The protein has been purified from natural sources and crystallized in a variety of non-isomorphous forms belonging to monoclinic and triclinic space groups. A diffraction data set to 1.55 Å resolution was obtained from a crystal belonging to space group P2, with unit-cell parameters a = 38.326, b = 33.597, c = 55.568 Å, βmore » = 104°. An inability to achieve isomorphism forced the use of MAD and SAD phasing methods. Phasing is in progress.« less

T-Duality in an H-Flux: Exchange of Momentum and Winding

NASA Astrophysics Data System (ADS)

Han, Fei; Mathai, Varghese

2018-02-01

Using our earlier proposal for Ramond-Ramond fields in an H-flux on loop space (Han et al. in Commun Math Phys 337(1):127-150, 2015. arXiv:1405.1320), we extend the Hori isomorphism in Bouwknegt et al. (Commun Math Phys 249:383-415, 2004. arXiv:hep-th/0306062; Phys Rev Lett 92:181601, 2004. arXiv:hep-th/0312052) from invariant differential forms, to invariant exotic differential forms such that the momentum and winding numbers are exchanged, filling in a gap in the literature. We also extend the compatibility of the action of invariant exact Courant algebroids on the T-duality isomorphism in Cavalcanti and Gualtieri (in: CRM proceedings of lecture notes, vol 50, pp 341-365, American Mathematical Society, Providence, 2010 ), to the T-duality isomorphism on exotic invariant differential forms.

Causality in Statistical Power: Isomorphic Properties of Measurement, Research Design, Effect Size, and Sample Size.

PubMed

Heidel, R Eric

2016-01-01

Statistical power is the ability to detect a significant effect, given that the effect actually exists in a population. Like most statistical concepts, statistical power tends to induce cognitive dissonance in hepatology researchers. However, planning for statistical power by an a priori sample size calculation is of paramount importance when designing a research study. There are five specific empirical components that make up an a priori sample size calculation: the scale of measurement of the outcome, the research design, the magnitude of the effect size, the variance of the effect size, and the sample size. A framework grounded in the phenomenon of isomorphism, or interdependencies amongst different constructs with similar forms, will be presented to understand the isomorphic effects of decisions made on each of the five aforementioned components of statistical power.

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 .

Institutionalisation in a Newly Created Private University

ERIC Educational Resources Information Center

Hodson, Peter; Connolly, Michael; Younes, Said

2008-01-01

Purpose: The purpose of this paper is to examine the introduction of a quality assurance system in a new, private university in Syria, and considers the extent to which the theoretical model based on institutional theory and isomorphism is reflected in practice. Design/methodology/approach: A five year longitudinal study which reviews the design,…

Hard and Soft Governance: The Journey from Transnational Agencies to School Leadership

ERIC Educational Resources Information Center

Moos, Lejf

2009-01-01

The governance and leadership at transnational, national and school level seem to be converging into a number of isomorphic forms as we see a tendency towards substituting "hard" forms of governance, that are legally binding, with "soft" forms based on persuasion and advice. This article analyses and discusses governance forms…

Exploring Higher Education Governance in Poland and Romania: Re-Convergence after Divergence?

ERIC Educational Resources Information Center

Dobbins, Michael

2017-01-01

This analysis focuses on changes in higher education governance in Poland and Romania in the post-communist era. The author applies a theoretical framework based on institutional isomorphism and historical institutionalism and maps the policy trajectories of both systems on the basis of three governance ideal-types. The public higher education…

A comparison of video modeling, text-based instruction, and no instruction for creating multiple baseline graphs in Microsoft Excel.

PubMed

Tyner, Bryan C; Fienup, Daniel M

2015-09-01

Graphing is socially significant for behavior analysts; however, graphing can be difficult to learn. Video modeling (VM) may be a useful instructional method but lacks evidence for effective teaching of computer skills. A between-groups design compared the effects of VM, text-based instruction, and no instruction on graphing performance. Participants who used VM constructed graphs significantly faster and with fewer errors than those who used text-based instruction or no instruction. Implications for instruction are discussed. © Society for the Experimental Analysis of Behavior.

Zeroth Poisson Homology, Foliated Cohomology and Perfect Poisson Manifolds

NASA Astrophysics Data System (ADS)

Martínez-Torres, David; Miranda, Eva

2018-01-01

We prove that, for compact regular Poisson manifolds, the zeroth homology group is isomorphic to the top foliated cohomology group, and we give some applications. In particular, we show that, for regular unimodular Poisson manifolds, top Poisson and foliated cohomology groups are isomorphic. Inspired by the symplectic setting, we define what a perfect Poisson manifold is. We use these Poisson homology computations to provide families of perfect Poisson manifolds.

Isomorphism and solid solutions among Ag- and Au-selenides

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

Palyanova, Galina A.; Seryotkin, Yurii V.; Novosibirsk State University

Au-Ag selenides were synthesized by heating stoichiometric mixtures of elementary substances of initial compositions Ag{sub 2−x}Au{sub x}Se with a step of x=0.25 (0≤x≤2) to 1050 °C and annealing at 500 °C. Scanning electron microscopy, optical microscopy, electron microprobe analysis and X-ray powder diffraction methods have been applied to study synthesized samples. Results of studies of synthesized products revealed the existence of three solid solutions with limited isomorphism Ag↔Au: naumannite Ag{sub 2}Se – Ag{sub 1.94}Au{sub 0.06}Se, fischesserite Ag{sub 3}AuSe{sub 2} - Ag{sub 3.2}Au{sub 0.8}Se{sub 2} and gold selenide AuSe - Au{sub 0.94}Ag{sub 0.06}Se. Solid solutions and AgAuSe phases were added tomore » the phase diagram of Ag-Au-Se system. Crystal-chemical interpretation of Ag-Au isomorphism in selenides was made on the basis of structural features of fischesserite, naumannite, and AuSe. - Highlights: • Au-Ag selenides were synthesized. • Limited Ag-Au isomorphism in the selenides is affected by structural features. • Some new phases were introduced to the phase diagram Ag-Au-Se.« less

Graph reconstruction using covariance-based methods.

PubMed

Sulaimanov, Nurgazy; Koeppl, Heinz

2016-12-01

Methods based on correlation and partial correlation are today employed in the reconstruction of a statistical interaction graph from high-throughput omics data. These dedicated methods work well even for the case when the number of variables exceeds the number of samples. In this study, we investigate how the graphs extracted from covariance and concentration matrix estimates are related by using Neumann series and transitive closure and through discussing concrete small examples. Considering the ideal case where the true graph is available, we also compare correlation and partial correlation methods for large realistic graphs. In particular, we perform the comparisons with optimally selected parameters based on the true underlying graph and with data-driven approaches where the parameters are directly estimated from the data.

Function plot response: A scalable system for teaching kinematics graphs

NASA Astrophysics Data System (ADS)

Laverty, James; Kortemeyer, Gerd

2012-08-01

Understanding and interpreting graphs are essential skills in all sciences. While students are mostly proficient in plotting given functions and reading values off graphs, they frequently lack the ability to construct and interpret graphs in a meaningful way. Students can use graphs as representations of value pairs, but often fail to interpret them as the representation of functions, and mostly fail to use them as representations of physical reality. 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. Initial experiences using the new problem type in an introductory physics course are reported.

Constructing compact and effective graphs for recommender systems via node and edge aggregations

DOE PAGES

Lee, Sangkeun; Kahng, Minsuk; Lee, Sang-goo

2014-12-10

Exploiting graphs for recommender systems has great potential to flexibly incorporate heterogeneous information for producing better recommendation results. As our baseline approach, we first introduce a naive graph-based recommendation method, which operates with a heterogeneous log-metadata graph constructed from user log and content metadata databases. Although the na ve graph-based recommendation method is simple, it allows us to take advantages of heterogeneous information and shows promising flexibility and recommendation accuracy. However, it often leads to extensive processing time due to the sheer size of the graphs constructed from entire user log and content metadata databases. In this paper, we proposemore » node and edge aggregation approaches to constructing compact and e ective graphs called Factor-Item bipartite graphs by aggregating nodes and edges of a log-metadata graph. Furthermore, experimental results using real world datasets indicate that our approach can significantly reduce the size of graphs exploited for recommender systems without sacrificing the recommendation quality.« less

graphkernels: R and Python packages for graph comparison

PubMed Central

Ghisu, M Elisabetta; Llinares-López, Felipe; Borgwardt, Karsten

2018-01-01

Abstract Summary Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C ++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples. Availability and implementation The R and Python packages including source code are available at https://CRAN.R-project.org/package=graphkernels and https://pypi.python.org/pypi/graphkernels. Contact mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch Supplementary information Supplementary data are available online at Bioinformatics. PMID:29028902

graphkernels: R and Python packages for graph comparison.

PubMed

Sugiyama, Mahito; Ghisu, M Elisabetta; Llinares-López, Felipe; Borgwardt, Karsten

2018-02-01

Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C ++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples. The R and Python packages including source code are available at https://CRAN.R-project.org/package=graphkernels and https://pypi.python.org/pypi/graphkernels. mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch. Supplementary data are available online at Bioinformatics. © The Author(s) 2017. Published by Oxford University Press.

An MBO Scheme for Minimizing the Graph Ohta-Kawasaki Functional

NASA Astrophysics Data System (ADS)

van Gennip, Yves

2018-06-01

We study a graph-based version of the Ohta-Kawasaki functional, which was originally introduced in a continuum setting to model pattern formation in diblock copolymer melts and has been studied extensively as a paradigmatic example of a variational model for pattern formation. Graph-based problems inspired by partial differential equations (PDEs) and variational methods have been the subject of many recent papers in the mathematical literature, because of their applications in areas such as image processing and data classification. This paper extends the area of PDE inspired graph-based problems to pattern-forming models, while continuing in the tradition of recent papers in the field. We introduce a mass conserving Merriman-Bence-Osher (MBO) scheme for minimizing the graph Ohta-Kawasaki functional with a mass constraint. We present three main results: (1) the Lyapunov functionals associated with this MBO scheme Γ -converge to the Ohta-Kawasaki functional (which includes the standard graph-based MBO scheme and total variation as a special case); (2) there is a class of graphs on which the Ohta-Kawasaki MBO scheme corresponds to a standard MBO scheme on a transformed graph and for which generalized comparison principles hold; (3) this MBO scheme allows for the numerical computation of (approximate) minimizers of the graph Ohta-Kawasaki functional with a mass constraint.

Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data Based on User-Authored Annotations.

PubMed

Zhao, Jian; Glueck, Michael; Breslav, Simon; Chevalier, Fanny; Khan, Azam

2017-01-01

User-authored annotations of data can support analysts in the activity of hypothesis generation and sensemaking, where it is not only critical to document key observations, but also to communicate insights between analysts. We present annotation graphs, a dynamic graph visualization that enables meta-analysis of data based on user-authored annotations. The annotation graph topology encodes annotation semantics, which describe the content of and relations between data selections, comments, and tags. We present a mixed-initiative approach to graph layout that integrates an analyst's manual manipulations with an automatic method based on similarity inferred from the annotation semantics. Various visual graph layout styles reveal different perspectives on the annotation semantics. Annotation graphs are implemented within C8, a system that supports authoring annotations during exploratory analysis of a dataset. We apply principles of Exploratory Sequential Data Analysis (ESDA) in designing C8, and further link these to an existing task typology in the visualization literature. We develop and evaluate the system through an iterative user-centered design process with three experts, situated in the domain of analyzing HCI experiment data. The results suggest that annotation graphs are effective as a method of visually extending user-authored annotations to data meta-analysis for discovery and organization of ideas.

An action research study; cultural differences impact how manufacturing organizations receive continuous improvement

NASA Astrophysics Data System (ADS)

Kattman, Braden R.

National culture and organizational culture impact how continuous improvement methods are received, implemented and deployed by suppliers. Previous research emphasized the dominance of national culture over organizational culture. The countries studied included Poland, Mexico, China, Taiwan, South Korea, Estonia, India, Canada, the United States, the United Kingdom, and Japan. The research found that Canada was most receptive to continuous improvement, with China being the least receptive. The study found that organizational culture was more influential than national culture. Isomorphism and benchmarking is driving continuous-improvement language and methods to be more universally known within business. Business and management practices are taking precedence in driving change within organizations.

Sampling ARG of multiple populations under complex configurations of subdivision and admixture.

PubMed

Carrieri, Anna Paola; Utro, Filippo; Parida, Laxmi

2016-04-01

Simulating complex evolution scenarios of multiple populations is an important task for answering many basic questions relating to population genomics. Apart from the population samples, the underlying Ancestral Recombinations Graph (ARG) is an additional important means in hypothesis checking and reconstruction studies. Furthermore, complex simulations require a plethora of interdependent parameters making even the scenario-specification highly non-trivial. We present an algorithm SimRA that simulates generic multiple population evolution model with admixture. It is based on random graphs that improve dramatically in time and space requirements of the classical algorithm of single populations.Using the underlying random graphs model, we also derive closed forms of expected values of the ARG characteristics i.e., height of the graph, number of recombinations, number of mutations and population diversity in terms of its defining parameters. This is crucial in aiding the user to specify meaningful parameters for the complex scenario simulations, not through trial-and-error based on raw compute power but intelligent parameter estimation. To the best of our knowledge this is the first time closed form expressions have been computed for the ARG properties. We show that the expected values closely match the empirical values through simulations.Finally, we demonstrate that SimRA produces the ARG in compact forms without compromising any accuracy. We demonstrate the compactness and accuracy through extensive experiments. SimRA (Simulation based on Random graph Algorithms) source, executable, user manual and sample input-output sets are available for downloading at: https://github.com/ComputationalGenomics/SimRA CONTACT: : parida@us.ibm.com Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

An internet graph model based on trade-off optimization

NASA Astrophysics Data System (ADS)

Alvarez-Hamelin, J. I.; Schabanel, N.

2004-03-01

This paper presents a new model for the Internet graph (AS graph) based on the concept of heuristic trade-off optimization, introduced by Fabrikant, Koutsoupias and Papadimitriou in[CITE] to grow a random tree with a heavily tailed degree distribution. We propose here a generalization of this approach to generate a general graph, as a candidate for modeling the Internet. We present the results of our simulations and an analysis of the standard parameters measured in our model, compared with measurements from the physical Internet graph.

PRIMAL: Page Rank-Based Indoor Mapping and Localization Using Gene-Sequenced Unlabeled WLAN Received Signal Strength

PubMed Central

Zhou, Mu; Zhang, Qiao; Xu, Kunjie; Tian, Zengshan; Wang, Yanmeng; He, Wei

2015-01-01

Due to the wide deployment of wireless local area networks (WLAN), received signal strength (RSS)-based indoor WLAN localization has attracted considerable attention in both academia and industry. In this paper, we propose a novel page rank-based indoor mapping and localization (PRIMAL) by using the gene-sequenced unlabeled WLAN RSS for simultaneous localization and mapping (SLAM). Specifically, first of all, based on the observation of the motion patterns of the people in the target environment, we use the Allen logic to construct the mobility graph to characterize the connectivity among different areas of interest. Second, the concept of gene sequencing is utilized to assemble the sporadically-collected RSS sequences into a signal graph based on the transition relations among different RSS sequences. Third, we apply the graph drawing approach to exhibit both the mobility graph and signal graph in a more readable manner. Finally, the page rank (PR) algorithm is proposed to construct the mapping from the signal graph into the mobility graph. The experimental results show that the proposed approach achieves satisfactory localization accuracy and meanwhile avoids the intensive time and labor cost involved in the conventional location fingerprinting-based indoor WLAN localization. PMID:26404274

Highly efficient periodically poled KTP-isomorphs with large apertures and extreme domain aspect-ratios

NASA Astrophysics Data System (ADS)

Canalias, Carlota; Zukauskas, Andrius; Tjörnhamman, Staffan; Viotti, Anne-Lise; Pasiskevicius, Valdas; Laurell, Fredrik

2018-02-01

Since the early 1990's, a substantial effort has been devoted to the development of quasi-phased-matched (QPM) nonlinear devices, not only in ferroelectric oxides like LiNbO3, LiTaO3 and KTiOPO4 (KTP), but also in semiconductors as GaAs, and GaP. The technology to implement QPM structures in ferroelectric oxides has by now matured enough to satisfy the most basic frequency-conversion schemes without substantial modification of the poling procedures. Here, we present a qualitative leap in periodic poling techniques that allows us to demonstrate devices and frequency conversion schemes that were deemed unfeasible just a few years ago. Thanks to our short-pulse poling and coercive-field engineering techniques, we are able to demonstrate large aperture (5 mm) periodically poled Rb-doped KTP devices with a highly-uniform conversion efficiency over the whole aperture. These devices allow parametric conversion with energies larger than 60 mJ. Moreover, by employing our coercive-field engineering technique we fabricate highlyefficient sub-µm periodically poled devices, with periodicities as short as 500 nm, uniform over 1 mm-thick crystals, which allow us to realize mirrorless optical parametric oscillators with counter-propagating signal and idler waves. These novel devices present unique spectral and tuning properties, superior to those of conventional OPOs. Furthermore, our techniques are compatible with KTA, a KTP isomorph with extended transparency in the mid-IR range. We demonstrate that our highly-efficient PPKTA is superior both for mid-IR and for green light generation - as a result of improved transmission properties in the visible range. Our KTP-isomorph poling techniques leading to highly-efficient QPM devices will be presented. Their optical performance and attractive damage thresholds will be discussed.

The metaphor-gestalt synergy underlying the self-organisation of perception as a semiotic process.

PubMed

Rail, David

2013-04-01

Recently the basis of concept and language formation has been redefined by the proposal that they both stem from perception and embodiment. The experiential revolution has lead to a far more integrated and dynamic understanding of perception as a semiotic system. The emergence of meaning in the perceptual process stems from the interaction between two key mechanisms. These are first, the generation of schemata through recurrent sensorimotor activity (SM) that underlies category and language formation (L). The second is the interaction between metaphor (M) and gestalt mechanisms (G) that generate invariant mappings beyond the SM domain that both conserve and diversify our understanding and meaning potential. We propose an important advance in our understanding of perception as a semiotic system through exploring the affect of self-organising to criticality where hierarchical behaviour becomes widely integrated through 1/f process and isomorphisms. Our proposal leads to several important implications. First, that SM and L form a functional isomorphism depicted as SM <=> L. We contend that SM <=> L is emergent, corresponding to the phenomenal self. Second, meaning structures the isomorphism SM <=>L through the synergy between M and G (M-G). M-G synergy is based on a combination of structuring and imagination. We contend that the interaction between M-G and SM <=> L functions as a macro-micro comutation that governs perception as semiosis. We discuss how our model relates to current research in fractal time and verb formation.

A Comparison of Video Modeling, Text-Based Instruction, and No Instruction for Creating Multiple Baseline Graphs in Microsoft Excel

ERIC Educational Resources Information Center

Tyner, Bryan C.; Fienup, Daniel M.

2015-01-01

Graphing is socially significant for behavior analysts; however, graphing can be difficult to learn. Video modeling (VM) may be a useful instructional method but lacks evidence for effective teaching of computer skills. A between-groups design compared the effects of VM, text-based instruction, and no instruction on graphing performance.…

Evolutionary dynamics on graphs: Efficient method for weak selection

NASA Astrophysics Data System (ADS)

Fu, Feng; Wang, Long; Nowak, Martin A.; Hauert, Christoph

2009-04-01

Investigating the evolutionary dynamics of game theoretical interactions in populations where individuals are arranged on a graph can be challenging in terms of computation time. Here, we propose an efficient method to study any type of game on arbitrary graph structures for weak selection. In this limit, evolutionary game dynamics represents a first-order correction to neutral evolution. Spatial correlations can be empirically determined under neutral evolution and provide the basis for formulating the game dynamics as a discrete Markov process by incorporating a detailed description of the microscopic dynamics based on the neutral correlations. This framework is then applied to one of the most intriguing questions in evolutionary biology: the evolution of cooperation. We demonstrate that the degree heterogeneity of a graph impedes cooperation and that the success of tit for tat depends not only on the number of rounds but also on the degree of the graph. Moreover, considering the mutation-selection equilibrium shows that the symmetry of the stationary distribution of states under weak selection is skewed in favor of defectors for larger selection strengths. In particular, degree heterogeneity—a prominent feature of scale-free networks—generally results in a more pronounced increase in the critical benefit-to-cost ratio required for evolution to favor cooperation as compared to regular graphs. This conclusion is corroborated by an analysis of the effects of population structures on the fixation probabilities of strategies in general 2×2 games for different types of graphs. Computer simulations confirm the predictive power of our method and illustrate the improved accuracy as compared to previous studies.

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.

Contact Graph Routing Enhancements Developed in ION for DTN

NASA Technical Reports Server (NTRS)

Segui, John S.; Burleigh, Scott

2013-01-01

The Interplanetary Overlay Network (ION) software suite is an open-source, flight-ready implementation of networking protocols including the Delay/Disruption Tolerant Networking (DTN) Bundle Protocol (BP), the CCSDS (Consultative Committee for Space Data Systems) File Delivery Protocol (CFDP), and many others including the Contact Graph Routing (CGR) DTN routing system. While DTN offers the capability to tolerate disruption and long signal propagation delays in transmission, without an appropriate routing protocol, no data can be delivered. CGR was built for space exploration networks with scheduled communication opportunities (typically based on trajectories and orbits), represented as a contact graph. Since CGR uses knowledge of future connectivity, the contact graph can grow rather large, and so efficient processing is desired. These enhancements allow CGR to scale to predicted NASA space network complexities and beyond. This software improves upon CGR by adopting an earliest-arrival-time cost metric and using the Dijkstra path selection algorithm. Moving to Dijkstra path selection also enables construction of an earliest- arrival-time tree for multicast routing. The enhancements have been rolled into ION 3.0 available on sourceforge.net.

A lymphocyte spatial distribution graph-based method for automated classification of recurrence risk on lung cancer images

NASA Astrophysics Data System (ADS)

Garciá-Arteaga, Juan D.; Corredor, Germán.; Wang, Xiangxue; Velcheti, Vamsidhar; Madabhushi, Anant; Romero, Eduardo

2017-11-01

Tumor-infiltrating lymphocytes occurs when various classes of white blood cells migrate from the blood stream towards the tumor, infiltrating it. The presence of TIL is predictive of the response of the patient to therapy. In this paper, we show how the automatic detection of lymphocytes in digital H and E histopathological images and the quantitative evaluation of the global lymphocyte configuration, evaluated through global features extracted from non-parametric graphs, constructed from the lymphocytes' detected positions, can be correlated to the patient's outcome in early-stage non-small cell lung cancer (NSCLC). The method was assessed on a tissue microarray cohort composed of 63 NSCLC cases. From the evaluated graphs, minimum spanning trees and K-nn showed the highest predictive ability, yielding F1 Scores of 0.75 and 0.72 and accuracies of 0.67 and 0.69, respectively. The predictive power of the proposed methodology indicates that graphs may be used to develop objective measures of the infiltration grade of tumors, which can, in turn, be used by pathologists to improve the decision making and treatment planning processes.

Application of graph-based semi-supervised learning for development of cyber COP and network intrusion detection

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Colonna-Romano, John; Eslami, Mohammed

2017-05-01

The United States increasingly relies on cyber-physical systems to conduct military and commercial operations. Attacks on these systems have increased dramatically around the globe. The attackers constantly change their methods, making state-of-the-art commercial and military intrusion detection systems ineffective. In this paper, we present a model to identify functional behavior of network devices from netflow traces. Our model includes two innovations. First, we define novel features for a host IP using detection of application graph patterns in IP's host graph constructed from 5-min aggregated packet flows. Second, we present the first application, to the best of our knowledge, of Graph Semi-Supervised Learning (GSSL) to the space of IP behavior classification. Using a cyber-attack dataset collected from NetFlow packet traces, we show that GSSL trained with only 20% of the data achieves higher attack detection rates than Support Vector Machines (SVM) and Naïve Bayes (NB) classifiers trained with 80% of data points. We also show how to improve detection quality by filtering out web browsing data, and conclude with discussion of future research directions.

An improved graph cut segmentation method for cervical lymph nodes on sonograms and its relationship with node's shape assessment.

PubMed

Zhang, Junhua; Wang, Yuanyuan; Shi, Xinling

2009-12-01

A modified graph cut was proposed under the elliptical shape constraint to segment cervical lymph nodes on sonograms, and its effect on the measurement of short axis to long axis ratio (S/L) was investigated by using the relative ultimate measurement accuracy (RUMA). Under the same user inputs, the proposed algorithm successfully segmented all 60 sonograms tested, while the traditional graph cut failed. The mean RUMA resulted from the developed method was comparable to that resulted from the manual segmentation. Results indicated that utilizing the elliptical shape prior could appreciably improve the graph cut for nodes segmentation, and the proposed method satisfied the accuracy requirement of S/L measurement.

Cognitive Aids for Guiding Graph Comprehension

ERIC Educational Resources Information Center

Mautone, Patricia D.; Mayer, Richard E.

2007-01-01

This study sought to improve students' comprehension of scientific graphs by adapting scaffolding techniques used to aid text comprehension. In 3 experiments involving 121 female and 88 male college students, some students were shown cognitive aids prior to viewing 4 geography graphs whereas others were not; all students were then asked to write a…

Data-based Decision-making: Teachers' Comprehension of Curriculum-based Measurement Progress-monitoring Graphs

ERIC Educational Resources Information Center

van den Bosch, Roxette M.; Espin, Christine A.; Chung, Siuman; Saab, Nadira

2017-01-01

Teachers have difficulty using data from Curriculum-based Measurement (CBM) progress graphs of students with learning difficulties for instructional decision-making. As a first step in unraveling those difficulties, we studied teachers' comprehension of CBM graphs. Using think-aloud methodology, we examined 23 teachers' ability to…

Flexible data integration and curation using a graph-based approach.

PubMed

Croset, Samuel; Rupp, Joachim; Romacker, Martin

2016-03-15

The increasing diversity of data available to the biomedical scientist holds promise for better understanding of diseases and discovery of new treatments for patients. In order to provide a complete picture of a biomedical question, data from many different origins needs to be combined into a unified representation. During this data integration process, inevitable errors and ambiguities present in the initial sources compromise the quality of the resulting data warehouse, and greatly diminish the scientific value of the content. Expensive and time-consuming manual curation is then required to improve the quality of the information. However, it becomes increasingly difficult to dedicate and optimize the resources for data integration projects as available repositories are growing both in size and in number everyday. We present a new generic methodology to identify problematic records, causing what we describe as 'data hairball' structures. The approach is graph-based and relies on two metrics traditionally used in social sciences: the graph density and the betweenness centrality. We evaluate and discuss these measures and show their relevance for flexible, optimized and automated data curation and linkage. The methodology focuses on information coherence and correctness to improve the scientific meaningfulness of data integration endeavors, such as knowledge bases and large data warehouses. samuel.croset@roche.com Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Current Logical Framework: The Propositional Fragment

DTIC Science & Technology

2003-01-01

Under the Curry- Howard isomorphism, M can also be read as a proof term, and A as a proposition of intuitionistic linear logic in its formulation as DILL...the obliga- tion to ensure that the underlying logic (via the Curry- Howard isomorphism, if you like) is sensible. In particular, the principles of...Proceedings of the International Logic Programming Symposium (ILPS󈨣), pages 51-65, Portland, Oregon, December 1995. MIT Press. 6. G. Bellin and P. J

Homology and isomorphism: Bourdieu in conversation with New Institutionalism.

PubMed

Wang, Yingyao

2016-06-01

Bourdieusian Field Theory (BFT) provided decisive inspiration for the early conceptual formulation of New Institutionalism (NI). This paper attempts to reinvigorate the stalled intellectual dialogue between NI and BFT by comparing NI's concept of isomorphism with BFT's notion of homology. I argue that Bourdieu's understanding of domination-oriented social action, transposable habitus, and a non-linear causality, embodied in his neglected concept of homology, provides an alternative theorization of field-level convergence to New Institutionalism's central idea of institutional isomorphism. To showcase how BFT can be useful for organizational research, I postulate a habitus-informed and field-conditioned theory of transference to enrich NI's spin-off thesis of 'diffusion'. I propose that while NI can benefit from BFT's potential of bringing social structure back into organizational research, BFT can enrich its social analysis by borrowing from NI's elaboration of the symbolic system of organizations. © London School of Economics and Political Science 2016.

Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction

PubMed Central

Kim, Dokyoon; Joung, Je-Gun; Sohn, Kyung-Ah; Shin, Hyunjung; Park, Yu Rang; Ritchie, Marylyn D; Kim, Ju Han

2015-01-01

Objective Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. Methods Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. Results Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. Conclusions Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies. PMID:25002459

Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.

PubMed

Kim, Dokyoon; Joung, Je-Gun; Sohn, Kyung-Ah; Shin, Hyunjung; Park, Yu Rang; Ritchie, Marylyn D; Kim, Ju Han

2015-01-01

Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association.

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.

EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures.

PubMed

Wang, Lei; Long, Xi; Arends, Johan B A M; Aarts, Ronald M

2017-10-01

The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed. A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG. A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FD t /h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FD t /h of 1.4s). The proposed VGS-based features can help improve seizure detection for ID patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

DOEpatents

Brettin, Thomas S.; Cottingham, Robert W.; Griffith, Shelton D.; Quest, Daniel J.

2015-09-08

A system and method of integrating diverse sources of data and data streams is presented. The method can include selecting a scenario based on a topic, creating a multi-relational directed graph based on the scenario, identifying and converting resources in accordance with the scenario and updating the multi-directed graph based on the resources, identifying data feeds in accordance with the scenario and updating the multi-directed graph based on the data feeds, identifying analytical routines in accordance with the scenario and updating the multi-directed graph using the analytical routines and identifying data outputs in accordance with the scenario and defining queries to produce the data outputs from the multi-directed graph.

Taming the Data Monster: Collecting and Analyzing Classroom Data to Improve Student Progress

ERIC Educational Resources Information Center

Kabot, Susan; Reeve, Christine E.

2016-01-01

Faced with increasing demands for accountability, teachers are having to base their instructional decisions and choice of interventions on data on student performance. This book shows how to make this otherwise daunting task much more manageable by means of case studies and countless evidence-based forms and graphs. Although this book often refers…

Graph-Based Object Class Discovery

NASA Astrophysics Data System (ADS)

Xia, Shengping; Hancock, Edwin R.

We are interested in the problem of discovering the set of object classes present in a database of images using a weakly supervised graph-based framework. Rather than making use of the ”Bag-of-Features (BoF)” approach widely used in current work on object recognition, we represent each image by a graph using a group of selected local invariant features. Using local feature matching and iterative Procrustes alignment, we perform graph matching and compute a similarity measure. Borrowing the idea of query expansion , we develop a similarity propagation based graph clustering (SPGC) method. Using this method class specific clusters of the graphs can be obtained. Such a cluster can be generally represented by using a higher level graph model whose vertices are the clustered graphs, and the edge weights are determined by the pairwise similarity measure. Experiments are performed on a dataset, in which the number of images increases from 1 to 50K and the number of objects increases from 1 to over 500. Some objects have been discovered with total recall and a precision 1 in a single cluster.

Discrimination Power of Polynomial-Based Descriptors for Graphs by Using Functional Matrices.

PubMed

Dehmer, Matthias; Emmert-Streib, Frank; Shi, Yongtang; Stefu, Monica; Tripathi, Shailesh

2015-01-01

In this paper, we study the discrimination power of graph measures that are based on graph-theoretical matrices. The paper generalizes the work of [M. Dehmer, M. Moosbrugger. Y. Shi, Encoding structural information uniquely with polynomial-based descriptors by employing the Randić matrix, Applied Mathematics and Computation, 268(2015), 164-168]. We demonstrate that by using the new functional matrix approach, exhaustively generated graphs can be discriminated more uniquely than shown in the mentioned previous work.

Discrimination Power of Polynomial-Based Descriptors for Graphs by Using Functional Matrices

PubMed Central

Dehmer, Matthias; Emmert-Streib, Frank; Shi, Yongtang; Stefu, Monica; Tripathi, Shailesh

2015-01-01

In this paper, we study the discrimination power of graph measures that are based on graph-theoretical matrices. The paper generalizes the work of [M. Dehmer, M. Moosbrugger. Y. Shi, Encoding structural information uniquely with polynomial-based descriptors by employing the Randić matrix, Applied Mathematics and Computation, 268(2015), 164–168]. We demonstrate that by using the new functional matrix approach, exhaustively generated graphs can be discriminated more uniquely than shown in the mentioned previous work. PMID:26479495

MEDRank: using graph-based concept ranking to index biomedical texts

PubMed Central

Herskovic, Jorge R.; Cohen, Trevor; Subramanian, Devika; Iyengar, M. Sriram; Smith, Jack W.; Bernstam, Elmer V.

2011-01-01

BACKGROUND As the volume of biomedical text increases exponentially, automatic indexing becomes increasingly important. However, existing approaches do not distinguish central (or core) concepts from concepts that were mentioned in passing. We focus on the problem of indexing MEDLINE records, a process that is currently performed by highly-trained humans at the National Library of Medicine (NLM). NLM indexers are assisted by a system called the Medical Text Indexer (MTI) that suggests candidate indexing terms. OBJECTIVE To improve the ability of MTI to select the core terms in MEDLINE abstracts. These core concepts are deemed to be most important and are designated as “major headings” by MEDLINE indexers. We introduce and evaluate a graph-based indexing methodology called MEDRank that generates concept graphs from biomedical text and then ranks the concepts within these graphs to identify the most important ones. METHODS We insert a MEDRank step into the MTI and compare MTI’s output with and without MEDRank to the MEDLINE indexers’ selected terms for a sample of 11,803 PubMed Central articles. We also tested whether human raters prefer terms generated by the MEDLINE indexers, MTI without MEDRank, and MTI with MEDRank for a sample of 36 PubMed Central articles. RESULTS MEDRank improved recall of major headings designated by 30% over MTI without MEDRank (0.489 vs 0.376). Overall recall was only slightly (6.5%) higher (0.490 vs 0.460) as was F2 (3%, 0.408 vs 0.396). However, overall precision was 3.9% lower (0.268 vs 0.279). Human raters preferred terms generated by MTI with MEDRank over terms generated by MTI without MEDRank (by an average of 1.00 more term per article), and preferred terms generated by MTI with MEDRank and the MEDLINE indexers at the same rate. CONCLUSIONS The addition of MEDRank to MTI significantly improved the retrieval of core concepts in MEDLINE abstracts and more closely matched human expectations compared to MTI without MEDRank. In addition, MEDRank slightly improved overall recall and F2. PMID:21439897

MEDRank: using graph-based concept ranking to index biomedical texts.

PubMed

Herskovic, Jorge R; Cohen, Trevor; Subramanian, Devika; Iyengar, M Sriram; Smith, Jack W; Bernstam, Elmer V

2011-06-01

As the volume of biomedical text increases exponentially, automatic indexing becomes increasingly important. However, existing approaches do not distinguish central (or core) concepts from concepts that were mentioned in passing. We focus on the problem of indexing MEDLINE records, a process that is currently performed by highly trained humans at the National Library of Medicine (NLM). NLM indexers are assisted by a system called the Medical Text Indexer (MTI) that suggests candidate indexing terms. To improve the ability of MTI to select the core terms in MEDLINE abstracts. These core concepts are deemed to be most important and are designated as "major headings" by MEDLINE indexers. We introduce and evaluate a graph-based indexing methodology called MEDRank that generates concept graphs from biomedical text and then ranks the concepts within these graphs to identify the most important ones. We insert a MEDRank step into the MTI and compare MTI's output with and without MEDRank to the MEDLINE indexers' selected terms for a sample of 11,803 PubMed Central articles. We also tested whether human raters prefer terms generated by the MEDLINE indexers, MTI without MEDRank, and MTI with MEDRank for a sample of 36 PubMed Central articles. MEDRank improved recall of major headings designated by 30% over MTI without MEDRank (0.489 vs. 0.376). Overall recall was only slightly (6.5%) higher (0.490 vs. 0.460) as was F(2) (3%, 0.408 vs. 0.396). However, overall precision was 3.9% lower (0.268 vs. 0.279). Human raters preferred terms generated by MTI with MEDRank over terms generated by MTI without MEDRank (by an average of 1.00 more term per article), and preferred terms generated by MTI with MEDRank and the MEDLINE indexers at the same rate. The addition of MEDRank to MTI significantly improved the retrieval of core concepts in MEDLINE abstracts and more closely matched human expectations compared to MTI without MEDRank. In addition, MEDRank slightly improved overall recall and F(2). Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Using ontology network structure in text mining.

PubMed

Berndt, Donald J; McCart, James A; Luther, Stephen L

2010-11-13

Statistical text mining treats documents as bags of words, with a focus on term frequencies within documents and across document collections. Unlike natural language processing (NLP) techniques that rely on an engineered vocabulary or a full-featured ontology, statistical approaches do not make use of domain-specific knowledge. The freedom from biases can be an advantage, but at the cost of ignoring potentially valuable knowledge. The approach proposed here investigates a hybrid strategy based on computing graph measures of term importance over an entire ontology and injecting the measures into the statistical text mining process. As a starting point, we adapt existing search engine algorithms such as PageRank and HITS to determine term importance within an ontology graph. The graph-theoretic approach is evaluated using a smoking data set from the i2b2 National Center for Biomedical Computing, cast as a simple binary classification task for categorizing smoking-related documents, demonstrating consistent improvements in accuracy.

Mathematical modeling of the malignancy of cancer using graph evolution.

PubMed

Gunduz-Demir, Cigdem

2007-10-01

We report a novel computational method based on graph evolution process to model the malignancy of brain cancer called glioma. In this work, we analyze the phases that a graph passes through during its evolution and demonstrate strong relation between the malignancy of cancer and the phase of its graph. From the photomicrographs of tissues, which are diagnosed as normal, low-grade cancerous and high-grade cancerous, we construct cell-graphs based on the locations of cells; we probabilistically generate an edge between every pair of cells depending on the Euclidean distance between them. For a cell-graph, we extract connectivity information including the properties of its connected components in order to analyze the phase of the cell-graph. Working with brain tissue samples surgically removed from 12 patients, we demonstrate that cell-graphs generated for different tissue types evolve differently and that they exhibit different phase properties, which distinguish a tissue type from another.

Helping Students Make Sense of Graphs: An Experimental Trial of SmartGraphs Software

ERIC Educational Resources Information Center

Zucker, Andrew; Kay, Rachel; Staudt, Carolyn

2014-01-01

Graphs are commonly used in science, mathematics, and social sciences to convey important concepts; yet students at all ages demonstrate difficulties interpreting graphs. This paper reports on an experimental study of free, Web-based software called SmartGraphs that is specifically designed to help students overcome their misconceptions regarding…

A general framework for regularized, similarity-based image restoration.

PubMed

Kheradmand, Amin; Milanfar, Peyman

2014-12-01

Any image can be represented as a function defined on a weighted graph, in which the underlying structure of the image is encoded in kernel similarity and associated Laplacian matrices. In this paper, we develop an iterative graph-based framework for image restoration based on a new definition of the normalized graph Laplacian. We propose a cost function, which consists of a new data fidelity term and regularization term derived from the specific definition of the normalized graph Laplacian. The normalizing coefficients used in the definition of the Laplacian and associated regularization term are obtained using fast symmetry preserving matrix balancing. This results in some desired spectral properties for the normalized Laplacian such as being symmetric, positive semidefinite, and returning zero vector when applied to a constant image. Our algorithm comprises of outer and inner iterations, where in each outer iteration, the similarity weights are recomputed using the previous estimate and the updated objective function is minimized using inner conjugate gradient iterations. This procedure improves the performance of the algorithm for image deblurring, where we do not have access to a good initial estimate of the underlying image. In addition, the specific form of the cost function allows us to render the spectral analysis for the solutions of the corresponding linear equations. In addition, the proposed approach is general in the sense that we have shown its effectiveness for different restoration problems, including deblurring, denoising, and sharpening. Experimental results verify the effectiveness of the proposed algorithm on both synthetic and real examples.

Community detection enhancement using non-negative matrix factorization with graph regularization

NASA Astrophysics Data System (ADS)

Liu, Xiao; Wei, Yi-Ming; Wang, Jian; Wang, Wen-Jun; He, Dong-Xiao; Song, Zhan-Jie

2016-06-01

Community detection is a meaningful task in the analysis of complex networks, which has received great concern in various domains. A plethora of exhaustive studies has made great effort and proposed many methods on community detection. Particularly, a kind of attractive one is the two-step method which first makes a preprocessing for the network and then identifies its communities. However, not all types of methods can achieve satisfactory results by using such preprocessing strategy, such as the non-negative matrix factorization (NMF) methods. In this paper, rather than using the above two-step method as most works did, we propose a graph regularized-based model to improve, specialized, the NMF-based methods for the detection of communities, namely NMFGR. In NMFGR, we introduce the similarity metric which contains both the global and local information of networks, to reflect the relationships between two nodes, so as to improve the accuracy of community detection. Experimental results on both artificial and real-world networks demonstrate the superior performance of NMFGR to some competing methods.

Theoretical Bound of CRLB for Energy Efficient Technique of RSS-Based Factor Graph Geolocation

NASA Astrophysics Data System (ADS)

Kahar Aziz, Muhammad Reza; Heriansyah; Saputra, EfaMaydhona; Musa, Ardiansyah

2018-03-01

To support the increase of wireless geolocation development as the key of the technology in the future, this paper proposes theoretical bound derivation, i.e., Cramer Rao lower bound (CRLB) for energy efficient of received signal strength (RSS)-based factor graph wireless geolocation technique. The theoretical bound derivation is crucially important to evaluate whether the energy efficient technique of RSS-based factor graph wireless geolocation is effective as well as to open the opportunity to further innovation of the technique. The CRLB is derived in this paper by using the Fisher information matrix (FIM) of the main formula of the RSS-based factor graph geolocation technique, which is lied on the Jacobian matrix. The simulation result shows that the derived CRLB has the highest accuracy as a bound shown by its lowest root mean squared error (RMSE) curve compared to the RMSE curve of the RSS-based factor graph geolocation technique. Hence, the derived CRLB becomes the lower bound for the efficient technique of RSS-based factor graph wireless geolocation.

Model-based multiple patterning layout decomposition

NASA Astrophysics Data System (ADS)

Guo, Daifeng; Tian, Haitong; Du, Yuelin; Wong, Martin D. F.

2015-10-01

As one of the most promising next generation lithography technologies, multiple patterning lithography (MPL) plays an important role in the attempts to keep in pace with 10 nm technology node and beyond. With feature size keeps shrinking, it has become impossible to print dense layouts within one single exposure. As a result, MPL such as double patterning lithography (DPL) and triple patterning lithography (TPL) has been widely adopted. There is a large volume of literature on DPL/TPL layout decomposition, and the current approach is to formulate the problem as a classical graph-coloring problem: Layout features (polygons) are represented by vertices in a graph G and there is an edge between two vertices if and only if the distance between the two corresponding features are less than a minimum distance threshold value dmin. The problem is to color the vertices of G using k colors (k = 2 for DPL, k = 3 for TPL) such that no two vertices connected by an edge are given the same color. This is a rule-based approach, which impose a geometric distance as a minimum constraint to simply decompose polygons within the distance into different masks. It is not desired in practice because this criteria cannot completely capture the behavior of the optics. For example, it lacks of sufficient information such as the optical source characteristics and the effects between the polygons outside the minimum distance. To remedy the deficiency, a model-based layout decomposition approach to make the decomposition criteria base on simulation results was first introduced at SPIE 2013.1 However, the algorithm1 is based on simplified assumption on the optical simulation model and therefore its usage on real layouts is limited. Recently AMSL2 also proposed a model-based approach to layout decomposition by iteratively simulating the layout, which requires excessive computational resource and may lead to sub-optimal solutions. The approach2 also potentially generates too many stiches. In this paper, we propose a model-based MPL layout decomposition method using a pre-simulated library of frequent layout patterns. Instead of using the graph G in the standard graph-coloring formulation, we build an expanded graph H where each vertex represents a group of adjacent features together with a coloring solution. By utilizing the library and running sophisticated graph algorithms on H, our approach can obtain optimal decomposition results efficiently. Our model-based solution can achieve a practical mask design which significantly improves the lithography quality on the wafer compared to the rule based decomposition.

Edge Pushing is Equivalent to Vertex Elimination for Computing Hessians

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

Wang, Mu; Pothen, Alex; Hovland, Paul

We prove the equivalence of two different Hessian evaluation algorithms in AD. The first is the Edge Pushing algorithm of Gower and Mello, which may be viewed as a second order Reverse mode algorithm for computing the Hessian. In earlier work, we have derived the Edge Pushing algorithm by exploiting a Reverse mode invariant based on the concept of live variables in compiler theory. The second algorithm is based on eliminating vertices in a computational graph of the gradient, in which intermediate variables are successively eliminated from the graph, and the weights of the edges are updated suitably. We provemore » that if the vertices are eliminated in a reverse topological order while preserving symmetry in the computational graph of the gradient, then the Vertex Elimination algorithm and the Edge Pushing algorithm perform identical computations. In this sense, the two algorithms are equivalent. This insight that unifies two seemingly disparate approaches to Hessian computations could lead to improved algorithms and implementations for computing Hessians. Read More: http://epubs.siam.org/doi/10.1137/1.9781611974690.ch11« less

Improving graph-based OCT segmentation for severe pathology in retinitis pigmentosa patients

NASA Astrophysics Data System (ADS)

Lang, Andrew; Carass, Aaron; Bittner, Ava K.; Ying, Howard S.; Prince, Jerry L.

2017-03-01

Three dimensional segmentation of macular optical coherence tomography (OCT) data of subjects with retinitis pigmentosa (RP) is a challenging problem due to the disappearance of the photoreceptor layers, which causes algorithms developed for segmentation of healthy data to perform poorly on RP patients. In this work, we present enhancements to a previously developed graph-based OCT segmentation pipeline to enable processing of RP data. The algorithm segments eight retinal layers in RP data by relaxing constraints on the thickness and smoothness of each layer learned from healthy data. Following from prior work, a random forest classifier is first trained on the RP data to estimate boundary probabilities, which are used by a graph search algorithm to find the optimal set of nine surfaces that fit the data. Due to the intensity disparity between normal layers of healthy controls and layers in various stages of degeneration in RP patients, an additional intensity normalization step is introduced. Leave-one-out validation on data acquired from nine subjects showed an average overall boundary error of 4.22 μm as compared to 6.02 μm using the original algorithm.

Improving Graduate Students' Graphing Skills of Multiple Baseline Designs with Microsoft[R] Excel 2007

ERIC Educational Resources Information Center

Lo, Ya-yu; Starling, A. Leyf Peirce

2009-01-01

This study examined the effects of a graphing task analysis using the Microsoft[R] Office Excel 2007 program on the single-subject multiple baseline graphing skills of three university graduate students. Using a multiple probe across participants design, the study demonstrated a functional relationship between the number of correct graphing…

OPEX: Optimized Eccentricity Computation in Graphs

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

Henderson, Keith

2011-11-14

Real-world graphs have many properties of interest, but often these properties are expensive to compute. We focus on eccentricity, radius and diameter in this work. These properties are useful measures of the global connectivity patterns in a graph. Unfortunately, computing eccentricity for all nodes is O(n2) for a graph with n nodes. We present OPEX, a novel combination of optimizations which improves computation time of these properties by orders of magnitude in real-world experiments on graphs of many different sizes. We run OPEX on graphs with up to millions of links. OPEX gives either exact results or bounded approximations, unlikemore » its competitors which give probabilistic approximations or sacrifice node-level information (eccentricity) to compute graphlevel information (diameter).« less

Signal Processing for Time-Series Functions on a Graph

DTIC Science & Technology

2018-02-01

as filtering to functions supported on graphs. These methods can be applied to scalar functions with a domain that can be described by a fixed...classical signal processing such as filtering to account for the graph domain. This work essentially divides into 2 basic approaches: graph Laplcian...based filtering and weighted adjacency matrix-based filtering . In Shuman et al.,11 and elaborated in Bronstein et al.,13 filtering operators are

An approach for brain-controlled prostheses based on Scene Graph Steady-State Visual Evoked Potentials.

PubMed

Li, Rui; Zhang, Xiaodong; Li, Hanzhe; Zhang, Liming; Lu, Zhufeng; Chen, Jiangcheng

2018-08-01

Brain control technology can restore communication between the brain and a prosthesis, and choosing a Brain-Computer Interface (BCI) paradigm to evoke electroencephalogram (EEG) signals is an essential step for developing this technology. In this paper, the Scene Graph paradigm used for controlling prostheses was proposed; this paradigm is based on Steady-State Visual Evoked Potentials (SSVEPs) regarding the Scene Graph of a subject's intention. A mathematic model was built to predict SSVEPs evoked by the proposed paradigm and a sinusoidal stimulation method was used to present the Scene Graph stimulus to elicit SSVEPs from subjects. Then, a 2-degree of freedom (2-DOF) brain-controlled prosthesis system was constructed to validate the performance of the Scene Graph-SSVEP (SG-SSVEP)-based BCI. The classification of SG-SSVEPs was detected via the Canonical Correlation Analysis (CCA) approach. To assess the efficiency of proposed BCI system, the performances of traditional SSVEP-BCI system were compared. Experimental results from six subjects suggested that the proposed system effectively enhanced the SSVEP responses, decreased the degradation of SSVEP strength and reduced the visual fatigue in comparison with the traditional SSVEP-BCI system. The average signal to noise ratio (SNR) of SG-SSVEP was 6.31 ± 2.64 dB, versus 3.38 ± 0.78 dB of traditional-SSVEP. In addition, the proposed system achieved good performances in prosthesis control. The average accuracy was 94.58% ± 7.05%, and the corresponding high information transfer rate (IRT) was 19.55 ± 3.07 bit/min. The experimental results revealed that the SG-SSVEP based BCI system achieves the good performance and improved the stability relative to the conventional approach. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

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

Graph-based biomedical text summarization: An itemset mining and sentence clustering approach.

PubMed

Nasr Azadani, Mozhgan; Ghadiri, Nasser; Davoodijam, Ensieh

2018-06-12

Automatic text summarization offers an efficient solution to access the ever-growing amounts of both scientific and clinical literature in the biomedical domain by summarizing the source documents while maintaining their most informative contents. In this paper, we propose a novel graph-based summarization method that takes advantage of the domain-specific knowledge and a well-established data mining technique called frequent itemset mining. Our summarizer exploits the Unified Medical Language System (UMLS) to construct a concept-based model of the source document and mapping the document to the concepts. Then, it discovers frequent itemsets to take the correlations among multiple concepts into account. The method uses these correlations to propose a similarity function based on which a represented graph is constructed. The summarizer then employs a minimum spanning tree based clustering algorithm to discover various subthemes of the document. Eventually, it generates the final summary by selecting the most informative and relative sentences from all subthemes within the text. We perform an automatic evaluation over a large number of summaries using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results demonstrate that the proposed summarization system outperforms various baselines and benchmark approaches. The carried out research suggests that the incorporation of domain-specific knowledge and frequent itemset mining equips the summarization system in a better way to address the informativeness measurement of the sentences. Moreover, clustering the graph nodes (sentences) can enable the summarizer to target different main subthemes of a source document efficiently. The evaluation results show that the proposed approach can significantly improve the performance of the summarization systems in the biomedical domain. Copyright © 2018. Published by Elsevier Inc.

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.

Diabetes Interactive Atlas

PubMed Central

Burrows, Nilka R.; Geiss, Linda S.

2014-01-01

The Diabetes Interactive Atlas is a recently released Web-based collection of maps that allows users to view geographic patterns and examine trends in diabetes and its risk factors over time across the United States and within states. The atlas provides maps, tables, graphs, and motion charts that depict national, state, and county data. Large amounts of data can be viewed in various ways simultaneously. In this article, we describe the design and technical issues for developing the atlas and provide an overview of the atlas’ maps and graphs. The Diabetes Interactive Atlas improves visualization of geographic patterns, highlights observation of trends, and demonstrates the concomitant geographic and temporal growth of diabetes and obesity. PMID:24503340

Convergence of Mayer and Virial expansions and the Penrose tree-graph identity

NASA Astrophysics Data System (ADS)

Procacci, Aldo; Yuhjtman, Sergio A.

2017-01-01

We establish new lower bounds for the convergence radius of the Mayer series and the Virial series of a continuous particle system interacting via a stable and tempered pair potential. Our bounds considerably improve those given by Penrose (J Math Phys 4:1312, 1963) and Ruelle (Ann Phys 5:109-120, 1963) for the Mayer series and by Lebowitz and Penrose (J Math Phys 7:841-847, 1964) for the Virial series. To get our results, we exploit the tree-graph identity given by Penrose (Statistical mechanics: foundations and applications. Benjamin, New York, 1967) using a new partition scheme based on minimum spanning trees.

Genetic algorithm and graph theory based matrix factorization method for online friend recommendation.

PubMed

Li, Qu; Yao, Min; Yang, Jianhua; Xu, Ning

2014-01-01

Online friend recommendation is a fast developing topic in web mining. In this paper, we used SVD matrix factorization to model user and item feature vector and used stochastic gradient descent to amend parameter and improve accuracy. To tackle cold start problem and data sparsity, we used KNN model to influence user feature vector. At the same time, we used graph theory to partition communities with fairly low time and space complexity. What is more, matrix factorization can combine online and offline recommendation. Experiments showed that the hybrid recommendation algorithm is able to recommend online friends with good accuracy.

Efficient dynamic graph construction for inductive semi-supervised learning.

PubMed

Dornaika, F; Dahbi, R; Bosaghzadeh, A; Ruichek, Y

2017-10-01

Most of graph construction techniques assume a transductive setting in which the whole data collection is available at construction time. Addressing graph construction for inductive setting, in which data are coming sequentially, has received much less attention. For inductive settings, constructing the graph from scratch can be very time consuming. This paper introduces a generic framework that is able to make any graph construction method incremental. This framework yields an efficient and dynamic graph construction method that adds new samples (labeled or unlabeled) to a previously constructed graph. As a case study, we use the recently proposed Two Phase Weighted Regularized Least Square (TPWRLS) graph construction method. The paper has two main contributions. First, we use the TPWRLS coding scheme to represent new sample(s) with respect to an existing database. The representative coefficients are then used to update the graph affinity matrix. The proposed method not only appends the new samples to the graph but also updates the whole graph structure by discovering which nodes are affected by the introduction of new samples and by updating their edge weights. The second contribution of the article is the application of the proposed framework to the problem of graph-based label propagation using multiple observations for vision-based recognition tasks. Experiments on several image databases show that, without any significant loss in the accuracy of the final classification, the proposed dynamic graph construction is more efficient than the batch graph construction. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessing the impact of background spectral graph construction techniques on the topological anomaly detection algorithm

NASA Astrophysics Data System (ADS)

Ziemann, Amanda K.; Messinger, David W.; Albano, James A.; Basener, William F.

2012-06-01

Anomaly detection algorithms have historically been applied to hyperspectral imagery in order to identify pixels whose material content is incongruous with the background material in the scene. Typically, the application involves extracting man-made objects from natural and agricultural surroundings. A large challenge in designing these algorithms is determining which pixels initially constitute the background material within an image. The topological anomaly detection (TAD) algorithm constructs a graph theory-based, fully non-parametric topological model of the background in the image scene, and uses codensity to measure deviation from this background. In TAD, the initial graph theory structure of the image data is created by connecting an edge between any two pixel vertices x and y if the Euclidean distance between them is less than some resolution r. While this type of proximity graph is among the most well-known approaches to building a geometric graph based on a given set of data, there is a wide variety of dierent geometrically-based techniques. In this paper, we present a comparative test of the performance of TAD across four dierent constructs of the initial graph: mutual k-nearest neighbor graph, sigma-local graph for two different values of σ > 1, and the proximity graph originally implemented in TAD.

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

Zhang, Fangyan; Zhang, Song; Chung Wong, Pak

Effectively visualizing large graphs and capturing the statistical properties are two challenging tasks. To aid in these two tasks, many sampling approaches for graph simplification have been proposed, falling into three categories: node sampling, edge sampling, and traversal-based sampling. It is still unknown which approach is the best. We evaluate commonly used graph sampling methods through a combined visual and statistical comparison of graphs sampled at various rates. We conduct our evaluation on three graph models: random graphs, small-world graphs, and scale-free graphs. Initial results indicate that the effectiveness of a sampling method is dependent on the graph model, themore » size of the graph, and the desired statistical property. This benchmark study can be used as a guideline in choosing the appropriate method for a particular graph sampling task, and the results presented can be incorporated into graph visualization and analysis tools.« less

Here's How To Make Better Graphs.

ERIC Educational Resources Information Center

Smith, Curtis A.

1997-01-01

Explains how to improve visual displays employed in school finance by examining a theoretical framework and applying it to the displays. Discusses and illustrates important display principles based on William Cleveland's ideas about decoding/encoding, length judgments, distance, detection, and superimposed curves; and Edward Tufte's work on data…

Cosegregation of Robertsonian metacentric chromosomes in the first meiotic division of multiple heterozygous male mice as revealed by FISH analysis of spermatocyte II metaphases.

PubMed

Scascitelli, M; Pacchierotti, F; Rizzoni, M; Gustavino, B; Spirito, F

2003-01-01

Contrasting results (random segregation or cosegregation of isomorphic chromosomes) have been reported up to now on the segregation pattern of Robertsonian metacentric chromosomes of Mus musculus domesticus in multiple heterozygotes, using different approaches (karyotypical analysis of the progeny or of second meiotic metaphases). In the present contribution data are presented based on FISH (Fluorescence In Situ Hybridisation) analysis with telomeric probes, which allowed us to distinguish metacentric chromosomes from pairs of acrocentric chromosomes with their centromeric regions close to each other. Probes were hybridized to DAPI stained metaphases of spermatocytes II of mice heterozygous for two, three or four Robertsonian metacentrics in an all-acrocentric background, the karyotype of which has been reconstructed starting from laboratory strains. Isomorphic chromosomes tend to cosegregate (metacentrics with metacentrics, acrocentrics with acrocentrics); the values found for cosegregation have a clear even if moderate effect on the reproductive isolation caused by underdominant chromosomal rearrangements. Copyright 2003 S. Karger AG, Basel

Model-based morphological segmentation and labeling of coronary angiograms.

PubMed

Haris, K; Efstratiadis, S N; Maglaveras, N; Pappas, C; Gourassas, J; Louridas, G

1999-10-01

A method for extraction and labeling of the coronary arterial tree (CAT) using minimal user supervision in single-view angiograms is proposed. The CAT structural description (skeleton and borders) is produced, along with quantitative information for the artery dimensions and assignment of coded labels, based on a given coronary artery model represented by a graph. The stages of the method are: 1) CAT tracking and detection; 2) artery skeleton and border estimation; 3) feature graph creation; and iv) artery labeling by graph matching. The approximate CAT centerline and borders are extracted by recursive tracking based on circular template analysis. The accurate skeleton and borders of each CAT segment are computed, based on morphological homotopy modification and watershed transform. The approximate centerline and borders are used for constructing the artery segment enclosing area (ASEA), where the defined skeleton and border curves are considered as markers. Using the marked ASEA, an artery gradient image is constructed where all the ASEA pixels (except the skeleton ones) are assigned the gradient magnitude of the original image. The artery gradient image markers are imposed as its unique regional minima by the homotopy modification method, the watershed transform is used for extracting the artery segment borders, and the feature graph is updated. Finally, given the created feature graph and the known model graph, a graph matching algorithm assigns the appropriate labels to the extracted CAT using weighted maximal cliques on the association graph corresponding to the two given graphs. Experimental results using clinical digitized coronary angiograms are presented.

Nonschematic drawing recognition: a new approach based on attributed graph grammar with flexible embedding

NASA Astrophysics Data System (ADS)

Lee, Kyu J.; Kunii, T. L.; Noma, T.

1993-01-01

In this paper, we propose a syntactic pattern recognition method for non-schematic drawings, based on a new attributed graph grammar with flexible embedding. In our graph grammar, the embedding rule permits the nodes of a guest graph to be arbitrarily connected with the nodes of a host graph. The ambiguity caused by this flexible embedding is controlled with the evaluation of synthesized attributes and the check of context sensitivity. To integrate parsing with the synthesized attribute evaluation and the context sensitivity check, we also develop a bottom up parsing algorithm.

An experimental study of graph connectivity for unsupervised word sense disambiguation.

PubMed

Navigli, Roberto; Lapata, Mirella

2010-04-01

Word sense disambiguation (WSD), the task of identifying the intended meanings (senses) of words in context, has been a long-standing research objective for natural language processing. In this paper, we are concerned with graph-based algorithms for large-scale WSD. Under this framework, finding the right sense for a given word amounts to identifying the most "important" node among the set of graph nodes representing its senses. We introduce a graph-based WSD algorithm which has few parameters and does not require sense-annotated data for training. Using this algorithm, we investigate several measures of graph connectivity with the aim of identifying those best suited for WSD. We also examine how the chosen lexicon and its connectivity influences WSD performance. We report results on standard data sets and show that our graph-based approach performs comparably to the state of the art.

Transfer-Efficient Face Routing Using the Planar Graphs of Neighbors in High Density WSNs

PubMed Central

Kim, Sang-Ha

2017-01-01

Face routing has been adopted in wireless sensor networks (WSNs) where topological changes occur frequently or maintaining full network information is difficult. For message forwarding in networks, a planar graph is used to prevent looping, and because long edges are removed by planarization and the resulting planar graph is composed of short edges, and messages are forwarded along multiple nodes connected by them even though they can be forwarded directly. To solve this, face routing using information on all nodes within 2-hop range was adopted to forward messages directly to the farthest node within radio range. However, as the density of the nodes increases, network performance plunges because message transfer nodes receive and process increased node information. To deal with this problem, we propose a new face routing using the planar graphs of neighboring nodes to improve transfer efficiency. It forwards a message directly to the farthest neighbor and reduces loads and processing time by distributing network graph construction and planarization to the neighbors. It also decreases the amount of location information to be transmitted by sending information on the planar graph nodes rather than on all neighboring nodes. Simulation results show that it significantly improves transfer efficiency. PMID:29053623

A Ga2O3 underlayer as an isomorphic template for ultrathin hematite films toward efficient photoelectrochemical water splitting.

PubMed

Hisatomi, Takashi; Brillet, Jérémie; Cornuz, Maurin; Le Formal, Florian; Tétreault, Nicolas; Sivula, Kevin; Grätzel, Michael

2012-01-01

Hematite photoanodes for photoelectrochemical (PEC) water splitting are often fabricated as extremely-thin films to minimize charge recombination because of the short diffusion lengths of photoexcited carriers. However, poor crystallinity caused by structural interaction with a substrate negates the potential of ultrathin hematite photoanodes. This study demonstrates that ultrathin Ga2O3 underlayers, which were deposited on conducting substrates prior to hematite layers by atomic layer deposition, served as an isomorphic (corundum-type) structural template for ultrathin hematite and improved the photocurrent onset of PEC water splitting by 0.2 V. The benefit from Ga2O3 underlayers was most pronounced when the thickness of the underlayer was approximately 2 nm. Thinner underlayers did not work effectively as a template presumably because of insufficient crystallinity of the underlayer, while thicker ones diminished the PEC performance of hematite because the underlayer prevented electron injection from hematite to a conductive substrate due to the large conduction band offset. The enhancement of PEC performance by a Ga2O3 underlayer was more significant for thinner hematite layers owing to greater margins for improving the crystallinity of ultrathin hematite. It was confirmed that a Ga2O3 underlayer was applicable to a rough conducting substrate loaded with Sb-doped SnO2 nanoparticles, improving the photocurrent by a factor of 1.4. Accordingly, a Ga2O3 underlayer could push forward the development of host-guest-type nanocomposites consisting of highly-rough substrates and extremely-thin hematite absorbers.

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.

Graph Drawing Aesthetics-Created by Users, Not Algorithms.

PubMed

Purchase, H C; Pilcher, C; Plimmer, B

2012-01-01

Prior empirical work on layout aesthetics for graph drawing algorithms has concentrated on the interpretation of existing graph drawings. We report on experiments which focus on the creation and layout of graph drawings: participants were asked to draw graphs based on adjacency lists, and to lay them out "nicely." Two interaction methods were used for creating the drawings: a sketch interface which allows for easy, natural hand movements, and a formal point-and-click interface similar to a typical graph editing system. We find, in common with many other studies, that removing edge crossings is the most significant aesthetic, but also discover that aligning nodes and edges to an underlying grid is important. We observe that the aesthetics favored by participants during creation of a graph drawing are often not evident in the final product and that the participants did not make a clear distinction between the processes of creation and layout. Our results suggest that graph drawing systems should integrate automatic layout with the user's manual editing process, and provide facilities to support grid-based graph creation.

Banach Synaptic Algebras

NASA Astrophysics Data System (ADS)

Foulis, David J.; Pulmannov, Sylvia

2018-04-01

Using a representation theorem of Erik Alfsen, Frederic Schultz, and Erling Størmer for special JB-algebras, we prove that a synaptic algebra is norm complete (i.e., Banach) if and only if it is isomorphic to the self-adjoint part of a Rickart C∗-algebra. Also, we give conditions on a Banach synaptic algebra that are equivalent to the condition that it is isomorphic to the self-adjoint part of an AW∗-algebra. Moreover, we study some relationships between synaptic algebras and so-called generalized Hermitian algebras.

The Rings with Identity Whose Additive Subgroups Are One-Sided Ideals

ERIC Educational Resources Information Center

Dobbs, David E.

2017-01-01

Let R be a ring with identity. Then {0} and R are the only additive subgroups of R if and only if R is isomorphic (as a ring with identity) to (exactly) one of {0}, Z/pZ for a prime number p. Also, each additive subgroup of R is a one-sided ideal of R if and only if R is isomorphic to (exactly) one of {0}, Z, Z/nZ for an integer n = 2. This note…

A reductionist perspective on quantum statistical mechanics: Coarse-graining of path integrals

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

Sinitskiy, Anton V.; Voth, Gregory A., E-mail: gavoth@uchicago.edu

2015-09-07

Computational modeling of the condensed phase based on classical statistical mechanics has been rapidly developing over the last few decades and has yielded important information on various systems containing up to millions of atoms. However, if a system of interest contains important quantum effects, well-developed classical techniques cannot be used. One way of treating finite temperature quantum systems at equilibrium has been based on Feynman’s imaginary time path integral approach and the ensuing quantum-classical isomorphism. This isomorphism is exact only in the limit of infinitely many classical quasiparticles representing each physical quantum particle. In this work, we present a reductionistmore » perspective on this problem based on the emerging methodology of coarse-graining. This perspective allows for the representations of one quantum particle with only two classical-like quasiparticles and their conjugate momenta. One of these coupled quasiparticles is the centroid particle of the quantum path integral quasiparticle distribution. Only this quasiparticle feels the potential energy function. The other quasiparticle directly provides the observable averages of quantum mechanical operators. The theory offers a simplified perspective on quantum statistical mechanics, revealing its most reductionist connection to classical statistical physics. By doing so, it can facilitate a simpler representation of certain quantum effects in complex molecular environments.« less

A reductionist perspective on quantum statistical mechanics: Coarse-graining of path integrals.

PubMed

Sinitskiy, Anton V; Voth, Gregory A

2015-09-07

Computational modeling of the condensed phase based on classical statistical mechanics has been rapidly developing over the last few decades and has yielded important information on various systems containing up to millions of atoms. However, if a system of interest contains important quantum effects, well-developed classical techniques cannot be used. One way of treating finite temperature quantum systems at equilibrium has been based on Feynman's imaginary time path integral approach and the ensuing quantum-classical isomorphism. This isomorphism is exact only in the limit of infinitely many classical quasiparticles representing each physical quantum particle. In this work, we present a reductionist perspective on this problem based on the emerging methodology of coarse-graining. This perspective allows for the representations of one quantum particle with only two classical-like quasiparticles and their conjugate momenta. One of these coupled quasiparticles is the centroid particle of the quantum path integral quasiparticle distribution. Only this quasiparticle feels the potential energy function. The other quasiparticle directly provides the observable averages of quantum mechanical operators. The theory offers a simplified perspective on quantum statistical mechanics, revealing its most reductionist connection to classical statistical physics. By doing so, it can facilitate a simpler representation of certain quantum effects in complex molecular environments.

Sketch Matching on Topology Product Graph.

PubMed

Liang, Shuang; Luo, Jun; Liu, Wenyin; Wei, Yichen

2015-08-01

Sketch matching is the fundamental problem in sketch based interfaces. After years of study, it remains challenging when there exists large irregularity and variations in the hand drawn sketch shapes. While most existing works exploit topology relations and graph representations for this problem, they are usually limited by the coarse topology exploration and heuristic (thus suboptimal) similarity metrics between graphs. We present a new sketch matching method with two novel contributions. We introduce a comprehensive definition of topology relations, which results in a rich and informative graph representation of sketches. For graph matching, we propose topology product graph that retains the full correspondence for matching two graphs. Based on it, we derive an intuitive sketch similarity metric whose exact solution is easy to compute. In addition, the graph representation and new metric naturally support partial matching, an important practical problem that received less attention in the literature. Extensive experimental results on a real challenging dataset and the superior performance of our method show that it outperforms the state-of-the-art.

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

Regulation of the demographic structure in isomorphic biphasic life cycles at the spatial fine scale.

PubMed

Vieira, Vasco Manuel Nobre de Carvalho da Silva; Mateus, Marcos Duarte

2014-01-01

Isomorphic biphasic algal life cycles often occur in the environment at ploidy abundance ratios (Haploid:Diploid) different from 1. Its spatial variability occurs within populations related to intertidal height and hydrodynamic stress, possibly reflecting the niche partitioning driven by their diverging adaptation to the environment argued necessary for their prevalence (evolutionary stability). Demographic models based in matrix algebra were developed to investigate which vital rates may efficiently generate an H:D variability at a fine spatial resolution. It was also taken into account time variation and type of life strategy. Ploidy dissimilarities in fecundity rates set an H:D spatial structure miss-fitting the ploidy fitness ratio. The same happened with ploidy dissimilarities in ramet growth whenever reproductive output dominated the population demography. Only through ploidy dissimilarities in looping rates (stasis, breakage and clonal growth) did the life cycle respond to a spatially heterogeneous environment efficiently creating a niche partition. Marginal locations were more sensitive than central locations. Related results have been obtained experimentally and numerically for widely different life cycles from the plant and animal kingdoms. Spore dispersal smoothed the effects of ploidy dissimilarities in fertility and enhanced the effects of ploidy dissimilarities looping rates. Ploidy dissimilarities in spore dispersal could also create the necessary niche partition, both over the space and time dimensions, even in spatial homogeneous environments and without the need for conditional differentiation of the ramets. Fine scale spatial variability may be the key for the prevalence of isomorphic biphasic life cycles, which has been neglected so far.

Regulation of the Demographic Structure in Isomorphic Biphasic Life Cycles at the Spatial Fine Scale

PubMed Central

Vieira, Vasco Manuel Nobre de Carvalho da Silva; Mateus, Marcos Duarte

2014-01-01

Isomorphic biphasic algal life cycles often occur in the environment at ploidy abundance ratios (Haploid:Diploid) different from 1. Its spatial variability occurs within populations related to intertidal height and hydrodynamic stress, possibly reflecting the niche partitioning driven by their diverging adaptation to the environment argued necessary for their prevalence (evolutionary stability). Demographic models based in matrix algebra were developed to investigate which vital rates may efficiently generate an H:D variability at a fine spatial resolution. It was also taken into account time variation and type of life strategy. Ploidy dissimilarities in fecundity rates set an H:D spatial structure miss-fitting the ploidy fitness ratio. The same happened with ploidy dissimilarities in ramet growth whenever reproductive output dominated the population demography. Only through ploidy dissimilarities in looping rates (stasis, breakage and clonal growth) did the life cycle respond to a spatially heterogeneous environment efficiently creating a niche partition. Marginal locations were more sensitive than central locations. Related results have been obtained experimentally and numerically for widely different life cycles from the plant and animal kingdoms. Spore dispersal smoothed the effects of ploidy dissimilarities in fertility and enhanced the effects of ploidy dissimilarities looping rates. Ploidy dissimilarities in spore dispersal could also create the necessary niche partition, both over the space and time dimensions, even in spatial homogeneous environments and without the need for conditional differentiation of the ramets. Fine scale spatial variability may be the key for the prevalence of isomorphic biphasic life cycles, which has been neglected so far. PMID:24658603

The construction of combinatorial manifolds with prescribed sets of links of vertices

NASA Astrophysics Data System (ADS)

Gaifullin, A. A.

2008-10-01

To every oriented closed combinatorial manifold we assign the set (with repetitions) of isomorphism classes of links of its vertices. The resulting transformation \\mathcal{L} is the main object of study in this paper. We pose an inversion problem for \\mathcal{L} and show that this problem is closely related to Steenrod's problem on the realization of cycles and to the Rokhlin-Schwartz-Thom construction of combinatorial Pontryagin classes. We obtain a necessary condition for a set of isomorphism classes of combinatorial spheres to belong to the image of \\mathcal{L}. (Sets satisfying this condition are said to be balanced.) We give an explicit construction showing that every balanced set of isomorphism classes of combinatorial spheres falls into the image of \\mathcal{L} after passing to a multiple set and adding several pairs of the form (Z,-Z), where -Z is the sphere Z with the orientation reversed. Given any singular simplicial cycle \\xi of a space X, this construction enables us to find explicitly a combinatorial manifold M and a map \\varphi\\colon M\\to X such that \\varphi_* \\lbrack M \\rbrack =r[\\xi] for some positive integer r. The construction is based on resolving singularities of \\xi. We give applications of the main construction to cobordisms of manifolds with singularities and cobordisms of simple cells. In particular, we prove that every rational additive invariant of cobordisms of manifolds with singularities admits a local formula. Another application is the construction of explicit (though inefficient) local combinatorial formulae for polynomials in the rational Pontryagin classes of combinatorial manifolds.

A rational approach to heavy-atom derivative screening

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

Joyce, M. Gordon; Radaev, Sergei; Sun, Peter D., E-mail: psun@nih.gov

2010-04-01

In order to overcome the difficulties associated with the ‘classical’ heavy-atom derivatization procedure, an attempt has been made to develop a rational crystal-free heavy-atom-derivative screening method and a quick-soak derivatization procedure which allows heavy-atom compound identification. Despite the development in recent times of a range of techniques for phasing macromolecules, the conventional heavy-atom derivatization method still plays a significant role in protein structure determination. However, this method has become less popular in modern high-throughput oriented crystallography, mostly owing to its trial-and-error nature, which often results in lengthy empirical searches requiring large numbers of well diffracting crystals. In addition, the phasingmore » power of heavy-atom derivatives is often compromised by lack of isomorphism or even loss of diffraction. In order to overcome the difficulties associated with the ‘classical’ heavy-atom derivatization procedure, an attempt has been made to develop a rational crystal-free heavy-atom derivative-screening method and a quick-soak derivatization procedure which allows heavy-atom compound identification. The method includes three basic steps: (i) the selection of likely reactive compounds for a given protein and specific crystallization conditions based on pre-defined heavy-atom compound reactivity profiles, (ii) screening of the chosen heavy-atom compounds for their ability to form protein adducts using mass spectrometry and (iii) derivatization of crystals with selected heavy-metal compounds using the quick-soak method to maximize diffraction quality and minimize non-isomorphism. Overall, this system streamlines the process of heavy-atom compound identification and minimizes the problem of non-isomorphism in phasing.« less

Cocrystals and alloys of nitazoxanide: enhanced pharmacokinetics.

PubMed

Suresh, Kuthuru; Mannava, M K Chaitanya; Nangia, Ashwini

2016-03-18

Two isomorphous cocrystals of nitazoxanide (NTZ) with p-aminosalicylic acid (PASA) and p-aminobenzoic acid (PABA) as well as their alloys were prepared by slurry and grinding techniques. The cocrystals exhibit faster dissolution rates and higher pharmacokinetic properties compared to the reference drug, and surprisingly the cocrystal alloy NTZ-PABA : NTZ-PASA (0.75 : 0.25) exhibited 4 fold higher bioavailability of NTZ in Sprague Dawley rats. This study opens the opportunity for cocrystal alloys as improved medicines.

Classification of real Lie superalgebras based on a simple Lie algebra, giving rise to interesting examples involving {mathfrak {su}}(2,2)

NASA Astrophysics Data System (ADS)

Guzzo, H.; Hernández, I.; Sánchez-Valenzuela, O. A.

2014-09-01

Finite dimensional semisimple real Lie superalgebras are described via finite dimensional semisimple complex Lie superalgebras. As an application of these results, finite dimensional real Lie superalgebras mathfrak {m}=mathfrak {m}_0 oplus mathfrak {m}_1 for which mathfrak {m}_0 is a simple Lie algebra are classified up to isomorphism.

Graphing Misconceptions and Possible Remedies Using Microcomputer-Based Labs.

ERIC Educational Resources Information Center

Barclay, William L.

Graphing is a common and powerful symbol system for representing concrete data. Yet research has shown that students often have graphical misconceptions about how graphs are related to the concrete event. Currently, the Technical Education Research Center (TERC) is developing microcomputer-based laboratories (MBL) science units that use probes to…

Electronic Document Management Systems: Where Are They Today?

ERIC Educational Resources Information Center

Koulopoulos, Thomas M.; Frappaolo, Carl

1993-01-01

Discusses developments in document management systems based on a survey of over 400 corporations and government agencies. Text retrieval and imaging markets, architecture and integration, purchasing plans, and vendor market leaders are covered. Five graphs present data on user preferences for improvements. A sidebar article reviews the development…

Solving Graph Laplacian Systems Through Recursive Bisections and Two-Grid Preconditioning

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

Ponce, Colin; Vassilevski, Panayot S.

2016-02-18

We present a parallelizable direct method for computing the solution to graph Laplacian-based linear systems derived from graphs that can be hierarchically bipartitioned with small edge cuts. For a graph of size n with constant-size edge cuts, our method decomposes a graph Laplacian in time O(n log n), and then uses that decomposition to perform a linear solve in time O(n log n). We then use the developed technique to design a preconditioner for graph Laplacians that do not have this property. Finally, we augment this preconditioner with a two-grid method that accounts for much of the preconditioner's weaknesses. Wemore » present an analysis of this method, as well as a general theorem for the condition number of a general class of two-grid support graph-based preconditioners. Numerical experiments illustrate the performance of the studied methods.« less

Software reliability through fault-avoidance and fault-tolerance

NASA Technical Reports Server (NTRS)

Vouk, Mladen A.; Mcallister, David F.

1993-01-01

Strategies and tools for the testing, risk assessment and risk control of dependable software-based systems were developed. Part of this project consists of studies to enable the transfer of technology to industry, for example the risk management techniques for safety-concious systems. Theoretical investigations of Boolean and Relational Operator (BRO) testing strategy were conducted for condition-based testing. The Basic Graph Generation and Analysis tool (BGG) was extended to fully incorporate several variants of the BRO metric. Single- and multi-phase risk, coverage and time-based models are being developed to provide additional theoretical and empirical basis for estimation of the reliability and availability of large, highly dependable software. A model for software process and risk management was developed. The use of cause-effect graphing for software specification and validation was investigated. Lastly, advanced software fault-tolerance models were studied to provide alternatives and improvements in situations where simple software fault-tolerance strategies break down.

Self-organizing maps for learning the edit costs in graph matching.

PubMed

Neuhaus, Michel; Bunke, Horst

2005-06-01

Although graph matching and graph edit distance computation have become areas of intensive research recently, the automatic inference of the cost of edit operations has remained an open problem. In the present paper, we address the issue of learning graph edit distance cost functions for numerically labeled graphs from a corpus of sample graphs. We propose a system of self-organizing maps (SOMs) that represent the distance measuring spaces of node and edge labels. Our learning process is based on the concept of self-organization. It adapts the edit costs in such a way that the similarity of graphs from the same class is increased, whereas the similarity of graphs from different classes decreases. The learning procedure is demonstrated on two different applications involving line drawing graphs and graphs representing diatoms, respectively.

Predicting drug-disease interactions by semi-supervised graph cut algorithm and three-layer data integration.

PubMed

Wu, Guangsheng; Liu, Juan; Wang, Caihua

2017-12-28

Prediction of drug-disease interactions is promising for either drug repositioning or disease treatment fields. The discovery of novel drug-disease interactions, on one hand can help to find novel indictions for the approved drugs; on the other hand can provide new therapeutic approaches for the diseases. Recently, computational methods for finding drug-disease interactions have attracted lots of attention because of their far more higher efficiency and lower cost than the traditional wet experiment methods. However, they still face several challenges, such as the organization of the heterogeneous data, the performance of the model, and so on. In this work, we present to hierarchically integrate the heterogeneous data into three layers. The drug-drug and disease-disease similarities are first calculated separately in each layer, and then the similarities from three layers are linearly fused into comprehensive drug similarities and disease similarities, which can then be used to measure the similarities between two drug-disease pairs. We construct a novel weighted drug-disease pair network, where a node is a drug-disease pair with known or unknown treatment relation, an edge represents the node-node relation which is weighted with the similarity score between two pairs. Now that similar drug-disease pairs are supposed to show similar treatment patterns, we can find the optimal graph cut of the network. The drug-disease pair with unknown relation can then be considered to have similar treatment relation with that within the same cut. Therefore, we develop a semi-supervised graph cut algorithm, SSGC, to find the optimal graph cut, based on which we can identify the potential drug-disease treatment interactions. By comparing with three representative network-based methods, SSGC achieves the highest performances, in terms of both AUC score and the identification rates of true drug-disease pairs. The experiments with different integration strategies also demonstrate that considering several sources of data can improve the performances of the predictors. Further case studies on four diseases, the top-ranked drug-disease associations have been confirmed by KEGG, CTD database and the literature, illustrating the usefulness of SSGC. The proposed comprehensive similarity scores from multi-views and multiple layers and the graph-cut based algorithm can greatly improve the prediction performances of drug-disease associations.

A comparative study of theoretical graph models for characterizing structural networks of human brain.

PubMed

Li, Xiaojin; Hu, Xintao; Jin, Changfeng; Han, Junwei; Liu, Tianming; Guo, Lei; Hao, Wei; Li, Lingjiang

2013-01-01

Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.

Automatic extraction of protein point mutations using a graph bigram association.

PubMed

Lee, Lawrence C; Horn, Florence; Cohen, Fred E

2007-02-02

Protein point mutations are an essential component of the evolutionary and experimental analysis of protein structure and function. While many manually curated databases attempt to index point mutations, most experimentally generated point mutations and the biological impacts of the changes are described in the peer-reviewed published literature. We describe an application, Mutation GraB (Graph Bigram), that identifies, extracts, and verifies point mutations from biomedical literature. The principal problem of point mutation extraction is to link the point mutation with its associated protein and organism of origin. Our algorithm uses a graph-based bigram traversal to identify these relevant associations and exploits the Swiss-Prot protein database to verify this information. The graph bigram method is different from other models for point mutation extraction in that it incorporates frequency and positional data of all terms in an article to drive the point mutation-protein association. Our method was tested on 589 articles describing point mutations from the G protein-coupled receptor (GPCR), tyrosine kinase, and ion channel protein families. We evaluated our graph bigram metric against a word-proximity metric for term association on datasets of full-text literature in these three different protein families. Our testing shows that the graph bigram metric achieves a higher F-measure for the GPCRs (0.79 versus 0.76), protein tyrosine kinases (0.72 versus 0.69), and ion channel transporters (0.76 versus 0.74). Importantly, in situations where more than one protein can be assigned to a point mutation and disambiguation is required, the graph bigram metric achieves a precision of 0.84 compared with the word distance metric precision of 0.73. We believe the graph bigram search metric to be a significant improvement over previous search metrics for point mutation extraction and to be applicable to text-mining application requiring the association of words.

Methods of visualizing graphs

DOEpatents

Wong, Pak C.; Mackey, Patrick S.; Perrine, Kenneth A.; Foote, Harlan P.; Thomas, James J.

2008-12-23

Methods for visualizing a graph by automatically drawing elements of the graph as labels are disclosed. In one embodiment, the method comprises receiving node information and edge information from an input device and/or communication interface, constructing a graph layout based at least in part on that information, wherein the edges are automatically drawn as labels, and displaying the graph on a display device according to the graph layout. In some embodiments, the nodes are automatically drawn as labels instead of, or in addition to, the label-edges.

Isomorphism and solid solutions among Ag- and Au-selenides

NASA Astrophysics Data System (ADS)

Palyanova, Galina A.; Seryotkin, Yurii V.; Kokh, Konstantin A.; Bakakin, Vladimir V.

2016-09-01

Au-Ag selenides were synthesized by heating stoichiometric mixtures of elementary substances of initial compositions Ag2-xAuxSe with a step of х=0.25 (0≤х≤2) to 1050 °С and annealing at 500 °C. Scanning electron microscopy, optical microscopy, electron microprobe analysis and X-ray powder diffraction methods have been applied to study synthesized samples. Results of studies of synthesized products revealed the existence of three solid solutions with limited isomorphism Ag↔Au: naumannite Ag2Se - Ag1.94Au0.06Se, fischesserite Ag3AuSe2 - Ag3.2Au0.8Se2 and gold selenide AuSe - Au0.94Ag0.06Se. Solid solutions and AgAuSe phases were added to the phase diagram of Ag-Au-Se system. Crystal-chemical interpretation of Ag-Au isomorphism in selenides was made on the basis of structural features of fischesserite, naumannite, and AuSe.

A new intrusion prevention model using planning knowledge graph

NASA Astrophysics Data System (ADS)

Cai, Zengyu; Feng, Yuan; Liu, Shuru; Gan, Yong

2013-03-01

Intelligent plan is a very important research in artificial intelligence, which has applied in network security. This paper proposes a new intrusion prevention model base on planning knowledge graph and discuses the system architecture and characteristics of this model. The Intrusion Prevention based on plan knowledge graph is completed by plan recognition based on planning knowledge graph, and the Intrusion response strategies and actions are completed by the hierarchical task network (HTN) planner in this paper. Intrusion prevention system has the advantages of intelligent planning, which has the advantage of the knowledge-sharing, the response focused, learning autonomy and protective ability.

G-Hash: Towards Fast Kernel-based Similarity Search in Large Graph Databases.

PubMed

Wang, Xiaohong; Smalter, Aaron; Huan, Jun; Lushington, Gerald H

2009-01-01

Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important research topic. Graph similarity search has applications in a wide range of domains including cheminformatics, bioinformatics, sensor network management, social network management, and XML documents, among others.Most of the current graph indexing methods focus on subgraph query processing, i.e. determining the set of database graphs that contains the query graph and hence do not directly support similarity search. In data mining and machine learning, various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models for supervised learning, graph kernel functions have (i) high computational complexity and (ii) non-trivial difficulty to be indexed in a graph database.Our objective is to bridge graph kernel function and similarity search in graph databases by proposing (i) a novel kernel-based similarity measurement and (ii) an efficient indexing structure for graph data management. Our method of similarity measurement builds upon local features extracted from each node and their neighboring nodes in graphs. A hash table is utilized to support efficient storage and fast search of the extracted local features. Using the hash table, a graph kernel function is defined to capture the intrinsic similarity of graphs and for fast similarity query processing. We have implemented our method, which we have named G-hash, and have demonstrated its utility on large chemical graph databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Most importantly, the new similarity measurement and the index structure is scalable to large database with smaller indexing size, faster indexing construction time, and faster query processing time as compared to state-of-the-art indexing methods such as C-tree, gIndex, and GraphGrep.

Significant enhancement of gas uptake capacity and selectivity via the judicious increase of open metal sites and Lewis basic sites within two polyhedron-based metal-organic frameworks.

PubMed

Liu, Bing; Yao, Shuo; Shi, Chao; Li, Guanghua; Huo, Qisheng; Liu, Yunling

2016-02-21

Two new isomorphous polyhedron-based MOFs ( and ), with dual functionalities of OMSs and LBSs, have been synthesized by using the SBB strategy. By judiciously avoiding the DABCO axial ligand, possesses more OMSs than , and exhibits a significant enhancement of CO2 uptake capacity 210 versus 162 cm(3) g(-1) for at 273 K under 1 bar.

Speech graphs provide a quantitative measure of thought disorder in psychosis.

PubMed

Mota, Natalia B; Vasconcelos, Nivaldo A P; Lemos, Nathalia; Pieretti, Ana C; Kinouchi, Osame; Cecchi, Guillermo A; Copelli, Mauro; Ribeiro, Sidarta

2012-01-01

Psychosis has various causes, including mania and schizophrenia. Since the differential diagnosis of psychosis is exclusively based on subjective assessments of oral interviews with patients, an objective quantification of the speech disturbances that characterize mania and schizophrenia is in order. In principle, such quantification could be achieved by the analysis of speech graphs. A graph represents a network with nodes connected by edges; in speech graphs, nodes correspond to words and edges correspond to semantic and grammatical relationships. To quantify speech differences related to psychosis, interviews with schizophrenics, manics and normal subjects were recorded and represented as graphs. Manics scored significantly higher than schizophrenics in ten graph measures. Psychopathological symptoms such as logorrhea, poor speech, and flight of thoughts were grasped by the analysis even when verbosity differences were discounted. Binary classifiers based on speech graph measures sorted schizophrenics from manics with up to 93.8% of sensitivity and 93.7% of specificity. In contrast, sorting based on the scores of two standard psychiatric scales (BPRS and PANSS) reached only 62.5% of sensitivity and specificity. The results demonstrate that alterations of the thought process manifested in the speech of psychotic patients can be objectively measured using graph-theoretical tools, developed to capture specific features of the normal and dysfunctional flow of thought, such as divergence and recurrence. The quantitative analysis of speech graphs is not redundant with standard psychometric scales but rather complementary, as it yields a very accurate sorting of schizophrenics and manics. Overall, the results point to automated psychiatric diagnosis based not on what is said, but on how it is said.

Case-Based Plan Recognition Using Action Sequence Graphs

DTIC Science & Technology

2014-10-01

resized as necessary. Similarly, trace- based reasoning (Zarka et al., 2013) and episode -based reasoning (Sánchez-Marré, 2005) store fixed-length...is a goal state of Π, where satisfies has the same semantics as originally laid out in Ghallab, Nau & Traverso (2004). Action 0 is ...Although there are syntactic similarities between planning encoding graphs and action sequence graphs, important semantic differences exist because the

Glutathion-S-Transferase P1 polymorphisms association with broncopulmonary dysplasia in preterm infants

PubMed Central

Karagianni, P; Rallis, D; Fidani, L; Porpodi, M; Kalinderi, K; Tsakalidis, C; Nikolaidis, N

2013-01-01

Background: Oxidative stress, characterized by the excretion of pre-oxidative and anti-oxidative proteases, has a key role in the pathogenesis of bronchopulmonary dysplasia (BPD). One of the many host anti-oxidant enzymes is glutathione-S-transferase P1 (GSTP1), with three polymorphic alleles having been identified: homozygous ile, heterozygous ile/val and homozygous val isomorph. The aim of this study was to examine the genetic predisposition to BPD in the GSTP1 polymorphisms. Methods: A prospective case-control study was carried out in the 2nd Neonatal Intensive Care Unit of Aristotle University in Thessaloniki, Greece during 2008. The genetic polymorphisms of GSTP1 in 28 preterms <32 weeks gestational age (GA) with BPD compared to 74 controls (33 preterms without BPD and 41 healthy terms) were examined. Results: The homozygous ile isomorph was predominant in all groups (preterms with BPD: 82%, preterms without BPD: 70%, healthy terms: 78%), followed by the heterozygous ile/val (14%, 18% and 20% respectively) and the homozygous val isomorph (4%, 12% and 2% respectively). The homozygous ile isomorph was also identified in the majority of preterms with mild (80%), moderate (100%) and severe (73%) BPD. The GSTP1 genetic distribution did not differ between the groups and GSTP1 polymorphisms were not associated with the severity of BPD. Conclusions: This study could not confirm an association between GSTP1 polymorphisms and the development of BPD or the severity of the disease. PMID:25031518

Glutathion-S-Transferase P1 polymorphisms association with broncopulmonary dysplasia in preterm infants.

PubMed

Karagianni, P; Rallis, D; Fidani, L; Porpodi, M; Kalinderi, K; Tsakalidis, C; Nikolaidis, N

2013-10-01

Oxidative stress, characterized by the excretion of pre-oxidative and anti-oxidative proteases, has a key role in the pathogenesis of bronchopulmonary dysplasia (BPD). One of the many host anti-oxidant enzymes is glutathione-S-transferase P1 (GSTP1), with three polymorphic alleles having been identified: homozygous ile, heterozygous ile/val and homozygous val isomorph. The aim of this study was to examine the genetic predisposition to BPD in the GSTP1 polymorphisms. A prospective case-control study was carried out in the 2nd Neonatal Intensive Care Unit of Aristotle University in Thessaloniki, Greece during 2008. The genetic polymorphisms of GSTP1 in 28 preterms <32 weeks gestational age (GA) with BPD compared to 74 controls (33 preterms without BPD and 41 healthy terms) were examined. The homozygous ile isomorph was predominant in all groups (preterms with BPD: 82%, preterms without BPD: 70%, healthy terms: 78%), followed by the heterozygous ile/val (14%, 18% and 20% respectively) and the homozygous val isomorph (4%, 12% and 2% respectively). The homozygous ile isomorph was also identified in the majority of preterms with mild (80%), moderate (100%) and severe (73%) BPD. The GSTP1 genetic distribution did not differ between the groups and GSTP1 polymorphisms were not associated with the severity of BPD. This study could not confirm an association between GSTP1 polymorphisms and the development of BPD or the severity of the disease.

Learning Mathematics with Interactive Whiteboards and Computer-Based Graphing Utility

ERIC Educational Resources Information Center

Erbas, Ayhan Kursat; Ince, Muge; Kaya, Sukru

2015-01-01

The purpose of this study was to explore the effect of a technology-supported learning environment utilizing an interactive whiteboard (IWB) and NuCalc graphing software compared to a traditional direct instruction-based environment on student achievement in graphs of quadratic functions and attitudes towards mathematics and technology. Sixty-five…

A genetic graph-based approach for partitional clustering.

PubMed

Menéndez, Héctor D; Barrero, David F; Camacho, David

2014-05-01

Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments.

BootGraph: probabilistic fiber tractography using bootstrap algorithms and graph theory.

PubMed

Vorburger, Robert S; Reischauer, Carolin; Boesiger, Peter

2013-02-01

Bootstrap methods have recently been introduced to diffusion-weighted magnetic resonance imaging to estimate the measurement uncertainty of ensuing diffusion parameters directly from the acquired data without the necessity to assume a noise model. These methods have been previously combined with deterministic streamline tractography algorithms to allow for the assessment of connection probabilities in the human brain. Thereby, the local noise induced disturbance in the diffusion data is accumulated additively due to the incremental progression of streamline tractography algorithms. Graph based approaches have been proposed to overcome this drawback of streamline techniques. For this reason, the bootstrap method is in the present work incorporated into a graph setup to derive a new probabilistic fiber tractography method, called BootGraph. The acquired data set is thereby converted into a weighted, undirected graph by defining a vertex in each voxel and edges between adjacent vertices. By means of the cone of uncertainty, which is derived using the wild bootstrap, a weight is thereafter assigned to each edge. Two path finding algorithms are subsequently applied to derive connection probabilities. While the first algorithm is based on the shortest path approach, the second algorithm takes all existing paths between two vertices into consideration. Tracking results are compared to an established algorithm based on the bootstrap method in combination with streamline fiber tractography and to another graph based algorithm. The BootGraph shows a very good performance in crossing situations with respect to false negatives and permits incorporating additional constraints, such as a curvature threshold. By inheriting the advantages of the bootstrap method and graph theory, the BootGraph method provides a computationally efficient and flexible probabilistic tractography setup to compute connection probability maps and virtual fiber pathways without the drawbacks of streamline tractography algorithms or the assumption of a noise distribution. Moreover, the BootGraph can be applied to common DTI data sets without further modifications and shows a high repeatability. Thus, it is very well suited for longitudinal studies and meta-studies based on DTI. Copyright © 2012 Elsevier Inc. All rights reserved.

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

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.

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…

Improving activity recognition using temporal coherence.

PubMed

Ataya, Abbas; Jallon, Pierre; Bianchi, Pascal; Doron, Maeva

2013-01-01

Assessment of daily physical activity using data from wearable sensors has recently become a prominent research area in the biomedical engineering field and a substantial application for pattern recognition. In this paper, we present an accelerometer-based activity recognition scheme on the basis of a hierarchical structured classifier. A first step consists of distinguishing static activities from dynamic ones in order to extract relevant features for each activity type. Next, a separate classifier is applied to detect more specific activities of the same type. On top of our activity recognition system, we introduce a novel approach to take into account the temporal coherence of activities. Inter-activity transition information is modeled by a directed graph Markov chain. Confidence measures in activity classes are then evaluated from conventional classifier's outputs and coupled with the graph to reinforce activity estimation. Accurate results and significant improvement of activity detection are obtained when applying our system for the recognition of 9 activities for 48 subjects.

MISAGA: An Algorithm for Mining Interesting Subgraphs in Attributed Graphs.

PubMed

He, Tiantian; Chan, Keith C C

2018-05-01

An attributed graph contains vertices that are associated with a set of attribute values. Mining clusters or communities, which are interesting subgraphs in the attributed graph is one of the most important tasks of graph analytics. Many problems can be defined as the mining of interesting subgraphs in attributed graphs. Algorithms that discover subgraphs based on predefined topologies cannot be used to tackle these problems. To discover interesting subgraphs in the attributed graph, we propose an algorithm called mining interesting subgraphs in attributed graph algorithm (MISAGA). MISAGA performs its tasks by first using a probabilistic measure to determine whether the strength of association between a pair of attribute values is strong enough to be interesting. Given the interesting pairs of attribute values, then the degree of association is computed for each pair of vertices using an information theoretic measure. Based on the edge structure and degree of association between each pair of vertices, MISAGA identifies interesting subgraphs by formulating it as a constrained optimization problem and solves it by identifying the optimal affiliation of subgraphs for the vertices in the attributed graph. MISAGA has been tested with several large-sized real graphs and is found to be potentially very useful for various applications.

A distributed query execution engine of big attributed graphs.

PubMed

Batarfi, Omar; Elshawi, Radwa; Fayoumi, Ayman; Barnawi, Ahmed; Sakr, Sherif

2016-01-01

A graph is a popular data model that has become pervasively used for modeling structural relationships between objects. In practice, in many real-world graphs, the graph vertices and edges need to be associated with descriptive attributes. Such type of graphs are referred to as attributed graphs. G-SPARQL has been proposed as an expressive language, with a centralized execution engine, for querying attributed graphs. G-SPARQL supports various types of graph querying operations including reachability, pattern matching and shortest path where any G-SPARQL query may include value-based predicates on the descriptive information (attributes) of the graph edges/vertices in addition to the structural predicates. In general, a main limitation of centralized systems is that their vertical scalability is always restricted by the physical limits of computer systems. This article describes the design, implementation in addition to the performance evaluation of DG-SPARQL, a distributed, hybrid and adaptive parallel execution engine of G-SPARQL queries. In this engine, the topology of the graph is distributed over the main memory of the underlying nodes while the graph data are maintained in a relational store which is replicated on the disk of each of the underlying nodes. DG-SPARQL evaluates parts of the query plan via SQL queries which are pushed to the underlying relational stores while other parts of the query plan, as necessary, are evaluated via indexless memory-based graph traversal algorithms. Our experimental evaluation shows the efficiency and the scalability of DG-SPARQL on querying massive attributed graph datasets in addition to its ability to outperform the performance of Apache Giraph, a popular distributed graph processing system, by orders of magnitudes.

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.

New Methods of Spectral-Density Based Graph Construction and Their Application to Hyperspectral Image Analysis

NASA Astrophysics Data System (ADS)

Stevens, Jeffrey

The past decade has seen the emergence of many hyperspectral image (HSI) analysis algorithms based on graph theory and derived manifold-coordinates. Yet, despite the growing number of algorithms, there has been limited study of the graphs constructed from spectral data themselves. Which graphs are appropriate for various HSI analyses--and why? This research aims to begin addressing these questions as the performance of graph-based techniques is inextricably tied to the graphical model constructed from the spectral data. We begin with a literature review providing a survey of spectral graph construction techniques currently used by the hyperspectral community, starting with simple constructs demonstrating basic concepts and then incrementally adding components to derive more complex approaches. Throughout this development, we discuss algorithm advantages and disadvantages for different types of hyperspectral analysis. A focus is provided on techniques influenced by spectral density through which the concept of community structure arises. Through the use of simulated and real HSI data, we demonstrate density-based edge allocation produces more uniform nearest neighbor lists than non-density based techniques through increasing the number of intracluster edges, facilitating higher k-nearest neighbor (k-NN) classification performance. Imposing the common mutuality constraint to symmetrify adjacency matrices is demonstrated to be beneficial in most circumstances, especially in rural (less cluttered) scenes. Many complex adaptive edge-reweighting techniques are shown to slightly degrade nearest-neighbor list characteristics. Analysis suggests this condition is possibly attributable to the validity of characterizing spectral density by a single variable representing data scale for each pixel. Additionally, it is shown that imposing mutuality hurts the performance of adaptive edge-allocation techniques or any technique that aims to assign a low number of edges (<10) to any pixel. A simple k bias addresses this problem. Many of the adaptive edge-reweighting techniques are based on the concept of codensity, so we explore codensity properties as they relate to density-based edge reweighting. We find that codensity may not be the best estimator of local scale due to variations in cluster density, so we introduce and compare two inherently density-weighted graph construction techniques from the data mining literature: shared nearest neighbors (SNN) and mutual proximity (MP). MP and SNN are not reliant upon a codensity measure, hence are not susceptible to its shortcomings. Neither has been used for hyperspectral analyses, so this presents the first study of these techniques on HSI data. We demonstrate MP and SNN can offer better performance, but in general none of the reweighting techniques improve the quality of these spectral graphs in our neighborhood structure tests. As such, these complex adaptive edge-reweighting techniques may need to be modified to increase their effectiveness. During this investigation, we probe deeper into properties of high-dimensional data and introduce the concept of concentration of measure (CoM)--the degradation in the efficacy of many common distance measures with increasing dimensionality--as it relates to spectral graph construction. CoM exists in pairwise distances between HSI pixels, but not to the degree experienced in random data of the same extrinsic dimension; a characteristic we demonstrate is due to the rich correlation and cluster structure present in HSI data. CoM can lead to hubness--a condition wherein some nodes have short distances (high similarities) to an exceptionally large number of nodes. We study hub presence in 49 HSI datasets of varying resolutions, altitudes, and spectral bands to demonstrate hubness effects are negligible in a k-NN classification example (generalized counting scenarios), but we note its impact on methods that use edge weights to derive manifold coordinates or splitting clusters based on spectral graph theory requires more investigation. Many of these new graph-related quantities can be exploited to demonstrate new techniques for HSI classification and anomaly detection. We present an initial exploration into this relatively new and exciting field based on an enhanced Schroedinger Eigenmap classification example and compare results to the current state-of-the-art approach. We produce equivalent results, but demonstrate different types of misclassifications, opening the door to combine the best of both approaches to achieve truly superior performance. A separate less mature hubness-assisted anomaly detector (HAAD) is also presented.

Biometric Subject Verification Based on Electrocardiographic Signals

NASA Technical Reports Server (NTRS)

Dusan, Sorin V. (Inventor); Jorgensen, Charles C. (Inventor)

2014-01-01

A method of authenticating or declining to authenticate an asserted identity of a candidate-person. In an enrollment phase, a reference PQRST heart action graph is provided or constructed from information obtained from a plurality of graphs that resemble each other for a known reference person, using a first graph comparison metric. In a verification phase, a candidate-person asserts his/her identity and presents a plurality of his/her heart cycle graphs. If a sufficient number of the candidate-person's measured graphs resemble each other, a representative composite graph is constructed from the candidate-person's graphs and is compared with a composite reference graph, for the person whose identity is asserted, using a second graph comparison metric. When the second metric value lies in a selected range, the candidate-person's assertion of identity is accepted.

EvoGraph: On-The-Fly Efficient Mining of Evolving Graphs on GPU

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

Sengupta, Dipanjan; Song, Shuaiwen

With the prevalence of the World Wide Web and social networks, there has been a growing interest in high performance analytics for constantly-evolving dynamic graphs. Modern GPUs provide massive AQ1 amount of parallelism for efficient graph processing, but the challenges remain due to their lack of support for the near real-time streaming nature of dynamic graphs. Specifically, due to the current high volume and velocity of graph data combined with the complexity of user queries, traditional processing methods by first storing the updates and then repeatedly running static graph analytics on a sequence of versions or snapshots are deemed undesirablemore » and computational infeasible on GPU. We present EvoGraph, a highly efficient and scalable GPU- based dynamic graph analytics framework.« less

Investigating the effects of streamline-based fiber tractography on matrix scaling in brain connective network.

PubMed

Jan, Hengtai; Chao, Yi-Ping; Cho, Kuan-Hung; Kuo, Li-Wei

2013-01-01

Investigating the brain connective network using the modern graph theory has been widely applied in cognitive and clinical neuroscience research. In this study, we aimed to investigate the effects of streamline-based fiber tractography on the change of network properties and established a systematic framework to understand how an adequate network matrix scaling can be determined. The network properties, including degree, efficiency and betweenness centrality, show similar tendency in both left and right hemispheres. By employing the curve-fitting process with exponential law and measuring the residuals, the association between changes of network properties and threshold of track numbers is found and an adequate range of investigating the lateralization of brain network is suggested. The proposed approach can be further applied in clinical applications to improve the diagnostic sensitivity using network analysis with graph theory.

Recursive Feature Extraction in Graphs

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

2014-08-14

ReFeX extracts recursive topological features from graph data. The input is a graph as a csv file and the output is a csv file containing feature values for each node in the graph. The features are based on topological counts in the neighborhoods of each nodes, as well as recursive summaries of neighbors' features.

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing.

PubMed

Li, Chunhua; Zhao, Pengpeng; Sheng, Victor S; Xian, Xuefeng; Wu, Jian; Cui, Zhiming

2017-01-01

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.

Learning graph matching.

PubMed

Caetano, Tibério S; McAuley, Julian J; Cheng, Li; Le, Quoc V; Smola, Alex J

2009-06-01

As a fundamental problem in pattern recognition, graph matching has applications in a variety of fields, from computer vision to computational biology. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. Many formulations of this problem can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility and a quadratic term encodes edge compatibility. The main research focus in this theme is about designing efficient algorithms for approximately solving the quadratic assignment problem, since it is NP-hard. In this paper we turn our attention to a different question: how to estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We present a method for learning graph matching: the training examples are pairs of graphs and the 'labels' are matches between them. Our experimental results reveal that learning can substantially improve the performance of standard graph matching algorithms. In particular, we find that simple linear assignment with such a learning scheme outperforms Graduated Assignment with bistochastic normalisation, a state-of-the-art quadratic assignment relaxation algorithm.

Solving graph data issues using a layered architecture approach with applications to web spam detection.

PubMed

Scarselli, Franco; Tsoi, Ah Chung; Hagenbuchner, Markus; Noi, Lucia Di

2013-12-01

This paper proposes the combination of two state-of-the-art algorithms for processing graph input data, viz., the probabilistic mapping graph self organizing map, an unsupervised learning approach, and the graph neural network, a supervised learning approach. We organize these two algorithms in a cascade architecture containing a probabilistic mapping graph self organizing map, and a graph neural network. We show that this combined approach helps us to limit the long-term dependency problem that exists when training the graph neural network resulting in an overall improvement in performance. This is demonstrated in an application to a benchmark problem requiring the detection of spam in a relatively large set of web sites. It is found that the proposed method produces results which reach the state of the art when compared with some of the best results obtained by others using quite different approaches. A particular strength of our method is its applicability towards any input domain which can be represented as a graph. Copyright © 2013 Elsevier Ltd. All rights reserved.

Association Rule Based Feature Extraction for Character Recognition

NASA Astrophysics Data System (ADS)

Dua, Sumeet; Singh, Harpreet

Association rules that represent isomorphisms among data have gained importance in exploratory data analysis because they can find inherent, implicit, and interesting relationships among data. They are also commonly used in data mining to extract the conditions among attribute values that occur together frequently in a dataset [1]. These rules have wide range of applications, namely in the financial and retail sectors of marketing, sales, and medicine.

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.

Computers and the Rational-Root Theorem--Another View.

ERIC Educational Resources Information Center

Waits, Bert K.; Demana, Franklin

1989-01-01

An approach to finding the rational roots of polynomial equations based on computer graphing is given. It integrates graphing with the purely algebraic approach. Either computers or graphing calculators can be used. (MNS)

Information Retrieval and Graph Analysis Approaches for Book Recommendation.

PubMed

Benkoussas, Chahinez; Bellot, Patrice

2015-01-01

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments.

Information Retrieval and Graph Analysis Approaches for Book Recommendation

PubMed Central

Benkoussas, Chahinez; Bellot, Patrice

2015-01-01

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments. PMID:26504899

Fixation probability on clique-based graphs

NASA Astrophysics Data System (ADS)

Choi, Jeong-Ok; Yu, Unjong

2018-02-01

The fixation probability of a mutant in the evolutionary dynamics of Moran process is calculated by the Monte-Carlo method on a few families of clique-based graphs. It is shown that the complete suppression of fixation can be realized with the generalized clique-wheel graph in the limit of small wheel-clique ratio and infinite size. The family of clique-star is an amplifier, and clique-arms graph changes from amplifier to suppressor as the fitness of the mutant increases. We demonstrate that the overall structure of a graph can be more important to determine the fixation probability than the degree or the heat heterogeneity. The dependence of the fixation probability on the position of the first mutant is discussed.

Fitchi: haplotype genealogy graphs based on the Fitch algorithm.

PubMed

Matschiner, Michael

2016-04-15

: In population genetics and phylogeography, haplotype genealogy graphs are important tools for the visualization of population structure based on sequence data. In this type of graph, node sizes are often drawn in proportion to haplotype frequencies and edge lengths represent the minimum number of mutations separating adjacent nodes. I here present Fitchi, a new program that produces publication-ready haplotype genealogy graphs based on the Fitch algorithm. http://www.evoinformatics.eu/fitchi.htm : michaelmatschiner@mac.com Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Mathematics of Web science: structure, dynamics and incentives.

PubMed

Chayes, Jennifer

2013-03-28

Dr Chayes' talk described how, to a discrete mathematician, 'all the world's a graph, and all the people and domains merely vertices'. A graph is represented as a set of vertices V and a set of edges E, so that, for instance, in the World Wide Web, V is the set of pages and E the directed hyperlinks; in a social network, V is the people and E the set of relationships; and in the autonomous system Internet, V is the set of autonomous systems (such as AOL, Yahoo! and MSN) and E the set of connections. This means that mathematics can be used to study the Web (and other large graphs in the online world) in the following way: first, we can model online networks as large finite graphs; second, we can sample pieces of these graphs; third, we can understand and then control processes on these graphs; and fourth, we can develop algorithms for these graphs and apply them to improve the online experience.

GraphMeta: Managing HPC Rich Metadata in Graphs

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

Dai, Dong; Chen, Yong; Carns, Philip

High-performance computing (HPC) systems face increasingly critical metadata management challenges, especially in the approaching exascale era. These challenges arise not only from exploding metadata volumes, but also from increasingly diverse metadata, which contains data provenance and arbitrary user-defined attributes in addition to traditional POSIX metadata. This ‘rich’ metadata is becoming critical to supporting advanced data management functionality such as data auditing and validation. In our prior work, we identified a graph-based model as a promising solution to uniformly manage HPC rich metadata due to its flexibility and generality. However, at the same time, graph-based HPC rich metadata anagement also introducesmore » significant challenges to the underlying infrastructure. In this study, we first identify the challenges on the underlying infrastructure to support scalable, high-performance rich metadata management. Based on that, we introduce GraphMeta, a graphbased engine designed for this use case. It achieves performance scalability by introducing a new graph partitioning algorithm and a write-optimal storage engine. We evaluate GraphMeta under both synthetic and real HPC metadata workloads, compare it with other approaches, and demonstrate its advantages in terms of efficiency and usability for rich metadata management in HPC systems.« less

3-D segmentation of articular cartilages by graph cuts using knee MR images from osteoarthritis initiative

NASA Astrophysics Data System (ADS)

Shim, Hackjoon; Lee, Soochan; Kim, Bohyeong; Tao, Cheng; Chang, Samuel; Yun, Il Dong; Lee, Sang Uk; Kwoh, Kent; Bae, Kyongtae

2008-03-01

Knee osteoarthritis is the most common debilitating health condition affecting elderly population. MR imaging of the knee is highly sensitive for diagnosis and evaluation of the extent of knee osteoarthritis. Quantitative analysis of the progression of osteoarthritis is commonly based on segmentation and measurement of articular cartilage from knee MR images. Segmentation of the knee articular cartilage, however, is extremely laborious and technically demanding, because the cartilage is of complex geometry and thin and small in size. To improve precision and efficiency of the segmentation of the cartilage, we have applied a semi-automated segmentation method that is based on an s/t graph cut algorithm. The cost function was defined integrating regional and boundary cues. While regional cues can encode any intensity distributions of two regions, "object" (cartilage) and "background" (the rest), boundary cues are based on the intensity differences between neighboring pixels. For three-dimensional (3-D) segmentation, hard constraints are also specified in 3-D way facilitating user interaction. When our proposed semi-automated method was tested on clinical patients' MR images (160 slices, 0.7 mm slice thickness), a considerable amount of segmentation time was saved with improved efficiency, compared to a manual segmentation approach.

Use of graph theory measures to identify errors in record linkage.

PubMed

Randall, Sean M; Boyd, James H; Ferrante, Anna M; Bauer, Jacqueline K; Semmens, James B

2014-07-01

Ensuring high linkage quality is important in many record linkage applications. Current methods for ensuring quality are manual and resource intensive. This paper seeks to determine the effectiveness of graph theory techniques in identifying record linkage errors. A range of graph theory techniques was applied to two linked datasets, with known truth sets. The ability of graph theory techniques to identify groups containing errors was compared to a widely used threshold setting technique. This methodology shows promise; however, further investigations into graph theory techniques are required. The development of more efficient and effective methods of improving linkage quality will result in higher quality datasets that can be delivered to researchers in shorter timeframes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Institutionalizing environmental protection through self-regulation: the case of environmental standards adoption in Lithuania.

NASA Astrophysics Data System (ADS)

Bileisis, Mantas; Misiune, Ieva

2017-04-01

The impacts of human activity in the environment have a global dimension, but there are no effective global governance instruments to enforce environmental standards. At the same time, many national governments lack incentives to pursue strict environmental policies. In this context, self-regulation is seen as an alternative venue to address environmental challenges. This work aims to identify factors that influence companies to engage in environmental self-regulation? For this aim in March 2015 a survey of 482 companies was conducted. The target group were companies operating in Lithuania that hold ISO14001 certificates - one of the most prolific instruments for self-regulation. The questionnaire was designed to test assumptions developed in new institutionalist literature which claim that common practices can emerge through isomorphism.- The results showed that the main motive for environmental self-regulation is the desire to improve company image, rather than protecting the environment per se. Another important finding was that the main source of pressure to adopt self-regulation was based less on the perceived demands but the customers. Rather the driver for the adoption was a feeling of a need no to fall behind industry leaders. Thus, normative isomorphism is the main mechanism through which environmental self-regulation proliferates. We claim for a rapid proliferation of environmental self-regulation perceived industry leaders need to be identified and they need to be persuaded that environmental standards are key for the development of the industry. However, this also raises questions of sustainability. Few industries have long standing leaders, and through successful investment and technological development new actors can arise and this may risk stalling or even reversing self-regulation.

Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages

PubMed Central

Nunez-Iglesias, Juan; Kennedy, Ryan; Plaza, Stephen M.; Chakraborty, Anirban; Katz, William T.

2014-01-01

The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them. PMID:24772079

Many-core graph analytics using accelerated sparse linear algebra routines

NASA Astrophysics Data System (ADS)

Kozacik, Stephen; Paolini, Aaron L.; Fox, Paul; Kelmelis, Eric

2016-05-01

Graph analytics is a key component in identifying emerging trends and threats in many real-world applications. Largescale graph analytics frameworks provide a convenient and highly-scalable platform for developing algorithms to analyze large datasets. Although conceptually scalable, these techniques exhibit poor performance on modern computational hardware. Another model of graph computation has emerged that promises improved performance and scalability by using abstract linear algebra operations as the basis for graph analysis as laid out by the GraphBLAS standard. By using sparse linear algebra as the basis, existing highly efficient algorithms can be adapted to perform computations on the graph. This approach, however, is often less intuitive to graph analytics experts, who are accustomed to vertex-centric APIs such as Giraph, GraphX, and Tinkerpop. We are developing an implementation of the high-level operations supported by these APIs in terms of linear algebra operations. This implementation is be backed by many-core implementations of the fundamental GraphBLAS operations required, and offers the advantages of both the intuitive programming model of a vertex-centric API and the performance of a sparse linear algebra implementation. This technology can reduce the number of nodes required, as well as the run-time for a graph analysis problem, enabling customers to perform more complex analysis with less hardware at lower cost. All of this can be accomplished without the requirement for the customer to make any changes to their analytics code, thanks to the compatibility with existing graph APIs.

A graph-based approach to inequality assessment

NASA Astrophysics Data System (ADS)

Palestini, Arsen; Pignataro, Giuseppe

2016-08-01

In a population consisting of heterogeneous types, whose income factors are indicated by nonnegative vectors, policies aggregating different factors can be represented by coalitions in a cooperative game, whose characteristic function is a multi-factor inequality index. When it is not possible to form all coalitions, the feasible ones can be indicated by a graph. We redefine Shapley and Banzhaf values on graph games to deduce some properties involving the degrees of the graph vertices and marginal contributions to overall inequality. An example is finally provided based on a modified multi-factor Atkinson index.

Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs

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

Zhang, Baichuan; Choudhury, Sutanay; Al-Hasan, Mohammad

2016-02-01

Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique for learning models for each predicate. Experimental results on large-scale knowledge bases such as YAGO2 show that our approach achieves substantially higher performance than several state-of-art approaches. Furthermore, we also study the performance of the link prediction algorithm in termsmore » of topological properties of the Knowledge Graph and present a linear regression model to reason about its expected level of accuracy.« less

Graphs as a Problem-Solving Tool in 1-D Kinematics

ERIC Educational Resources Information Center

Desbien, Dwain M.

2008-01-01

In this age of the microcomputer-based lab (MBL), students are quite accustomed to looking at graphs of position, velocity, and acceleration versus time. A number of textbooks argue convincingly that the slope of the velocity graph gives the acceleration, the area under the velocity graph yields the displacement, and the area under the…

Approximate Computing Techniques for Iterative Graph Algorithms

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

Panyala, Ajay R.; Subasi, Omer; Halappanavar, Mahantesh

Approximate computing enables processing of large-scale graphs by trading off quality for performance. Approximate computing techniques have become critical not only due to the emergence of parallel architectures but also the availability of large scale datasets enabling data-driven discovery. Using two prototypical graph algorithms, PageRank and community detection, we present several approximate computing heuristics to scale the performance with minimal loss of accuracy. We present several heuristics including loop perforation, data caching, incomplete graph coloring and synchronization, and evaluate their efficiency. We demonstrate performance improvements of up to 83% for PageRank and up to 450x for community detection, with lowmore » impact of accuracy for both the algorithms. We expect the proposed approximate techniques will enable scalable graph analytics on data of importance to several applications in science and their subsequent adoption to scale similar graph algorithms.« less

Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia

PubMed Central

Yu, Qingbao; Erhardt, Erik B.; Sui, Jing; Du, Yuhui; He, Hao; Hjelm, Devon; Cetin, Mustafa S.; Rachakonda, Srinivas; Miller, Robyn L.; Pearlson, Godfrey; Calhoun, Vince D.

2014-01-01

Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia which may underscore the abnormal brain performance in this mental illness. PMID:25514514

Intuitive color-based visualization of multimedia content as large graphs

NASA Astrophysics Data System (ADS)

Delest, Maylis; Don, Anthony; Benois-Pineau, Jenny

2004-06-01

Data visualization techniques are penetrating in various technological areas. In the field of multimedia such as information search and retrieval in multimedia archives, or digital media production and post-production, data visualization methodologies based on large graphs give an exciting alternative to conventional storyboard visualization. In this paper we develop a new approach to visualization of multimedia (video) documents based both on large graph clustering and preliminary video segmenting and indexing.

Crystallization, dehydration and experimental phasing of WbdD, a bifunctional kinase and methyltransferase from Escherichia coli O9a

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

Hagelueken, Gregor; Huang, Hexian; Harlos, Karl

2012-10-01

The optimization of WbdD crystals using a novel dehydration protocol and experimental phasing at 3.5 Å resolution by cross-crystal averaging followed by molecular replacement of electron density into a non-isomorphous 3.0 Å resolution native data set are reported. WbdD is a bifunctional kinase/methyltransferase that is responsible for regulation of lipopolysaccharide O antigen polysaccharide chain length in Escherichia coli serotype O9a. Solving the crystal structure of this protein proved to be a challenge because the available crystals belonging to space group I23 only diffracted to low resolution (>95% of the crystals diffracted to resolution lower than 4 Å and most onlymore » to 8 Å) and were non-isomorphous, with changes in unit-cell dimensions of greater than 10%. Data from a serendipitously found single native crystal that diffracted to 3.0 Å resolution were non-isomorphous with a lower (3.5 Å) resolution selenomethionine data set. Here, a strategy for improving poor (3.5 Å resolution) initial phases by density modification and cross-crystal averaging with an additional 4.2 Å resolution data set to build a crude model of WbdD is desribed. Using this crude model as a mask to cut out the 3.5 Å resolution electron density yielded a successful molecular-replacement solution of the 3.0 Å resolution data set. The resulting map was used to build a complete model of WbdD. The hydration status of individual crystals appears to underpin the variable diffraction quality of WbdD crystals. After the initial structure had been solved, methods to control the hydration status of WbdD were developed and it was thus possible to routinely obtain high-resolution diffraction (to better than 2.5 Å resolution). This novel and facile crystal-dehydration protocol may be useful for similar challenging situations.« less

Extended Graph-Based Models for Enhanced Similarity Search in Cavbase.

PubMed

Krotzky, Timo; Fober, Thomas; Hüllermeier, Eyke; Klebe, Gerhard

2014-01-01

To calculate similarities between molecular structures, measures based on the maximum common subgraph are frequently applied. For the comparison of protein binding sites, these measures are not fully appropriate since graphs representing binding sites on a detailed atomic level tend to get very large. In combination with an NP-hard problem, a large graph leads to a computationally demanding task. Therefore, for the comparison of binding sites, a less detailed coarse graph model is used building upon so-called pseudocenters. Consistently, a loss of structural data is caused since many atoms are discarded and no information about the shape of the binding site is considered. This is usually resolved by performing subsequent calculations based on additional information. These steps are usually quite expensive, making the whole approach very slow. The main drawback of a graph-based model solely based on pseudocenters, however, is the loss of information about the shape of the protein surface. In this study, we propose a novel and efficient modeling formalism that does not increase the size of the graph model compared to the original approach, but leads to graphs containing considerably more information assigned to the nodes. More specifically, additional descriptors considering surface characteristics are extracted from the local surface and attributed to the pseudocenters stored in Cavbase. These properties are evaluated as additional node labels, which lead to a gain of information and allow for much faster but still very accurate comparisons between different structures.

Granular Flow Graph, Adaptive Rule Generation and Tracking.

PubMed

Pal, Sankar Kumar; Chakraborty, Debarati Bhunia

2017-12-01

A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking. In the process, several new concepts and operations are introduced, and methodologies formulated with superior performance. The flow graph enables in defining an intelligent technique for rule base adaptation where its characteristics in mapping the relevance of attributes and rules in decision-making system are exploited. Two new features, namely, expected flow graph and mutual dependency between flow graphs are defined to make the flow graph applicable in the tasks of both training and validation. All these techniques are performed in neighborhood granular level. A way of forming spatio-temporal 3-D granules of arbitrary shape and size is introduced. The rough flow graph-based adaptive granular rule-based system, thus produced for unsupervised video tracking, is capable of handling the uncertainties and incompleteness in frames, able to overcome the incompleteness in information that arises without initial manual interactions and in providing superior performance and gaining in computation time. The cases of partial overlapping and detecting the unpredictable changes are handled efficiently. It is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation. The quantitative indices used for evaluating the performance of tracking do not require any information on ground truth as in the other methods. Superiority of the algorithm to nonadaptive and other recent ones is demonstrated extensively.

Building occupancy simulation and data assimilation using a graph-based agent-oriented model

NASA Astrophysics Data System (ADS)

Rai, Sanish; Hu, Xiaolin

2018-07-01

Building occupancy simulation and estimation simulates the dynamics of occupants and estimates their real-time spatial distribution in a building. It requires a simulation model and an algorithm for data assimilation that assimilates real-time sensor data into the simulation model. Existing building occupancy simulation models include agent-based models and graph-based models. The agent-based models suffer high computation cost for simulating large numbers of occupants, and graph-based models overlook the heterogeneity and detailed behaviors of individuals. Recognizing the limitations of existing models, this paper presents a new graph-based agent-oriented model which can efficiently simulate large numbers of occupants in various kinds of building structures. To support real-time occupancy dynamics estimation, a data assimilation framework based on Sequential Monte Carlo Methods is also developed and applied to the graph-based agent-oriented model to assimilate real-time sensor data. Experimental results show the effectiveness of the developed model and the data assimilation framework. The major contributions of this work are to provide an efficient model for building occupancy simulation that can accommodate large numbers of occupants and an effective data assimilation framework that can provide real-time estimations of building occupancy from sensor data.

Structural Changes Correlated with Magnetic Spin State Isomorphism in the S2 State of the Mn4CaO5 Cluster in the Oxygen-Evolving Complex of Photosystem II

PubMed Central

Chatterjee, Ruchira; Han, Guangye; Kern, Jan; Gul, Sheraz; Fuller, Franklin D.; Garachtchenko, Anna; Young, Iris; Weng, Tsu-Chien; Nordlund, Dennis; Alonso-Mori, Roberto; Bergmann, Uwe; Sokaras, Dimosthenis; Hatakeyama, Makoto; Yachandra, Vittal K.; Yano, Junko

2016-01-01

The Mn4CaO5 cluster in Photosystem II catalyzes the four-electron redox reaction of water oxidation in natural photosynthesis. This catalytic reaction cycles through four intermediate states (Si, i = 0 to 4), involving changes in the redox state of the four Mn atoms in the cluster. Recent studies suggest the presence and importance of isomorphous structures within the same redox/intermediate S-state. It is highly likely that geometric and electronic structural flexibility play a role in the catalytic mechanism. Among the catalytic intermediates that have been identified experimentally thus far, there is clear evidence of such isomorphism in the S2 state, with a high-spin (5/2) (HS) and a low spin (1/2) (LS) form, identified and characterized by their distinct electron paramagnetic resonance (EPR spectroscopy) signals. We studied these two S2 isomers with Mn extended X-ray absorption fine structure (EXAFS) and absorption and emission spectroscopy (XANES/XES) to characterize the structural and electronic structural properties. The geometric and electronic structure of the HS and LS S2 states are different as determined using Mn EXAFS and XANES/XES, respectively. The Mn K-edge XANES and XES for the HS form are different from the LS and indicate a slightly lower positive charge on the Mn atoms compared to the LS form. Based on the EXAFS results which are clearly different, we propose possible structural differences between the two spin states. Such structural and magnetic redox-isomers if present at room temperature, will likely play a role in the mechanism for water-exchange/oxidation in photosynthesis. PMID:28044099

Structural changes correlated with magnetic spin state isomorphism in the S 2 state of the Mn 4CaO 5 cluster in the oxygen-evolving complex of photosystem II

DOE PAGES

Chatterjee, Ruchira; Han, Guangye; Kern, Jan; ...

2016-05-09

The Mn 4CaO 5 cluster in photosystem II catalyzes the four-electron redox reaction of water oxidation in natural photosynthesis. This catalytic reaction cycles through four intermediate states (S i, i = 0 to 4), involving changes in the redox state of the four Mn atoms in the cluster. Recent studies suggest the presence and importance of isomorphous structures within the same redox/intermediate S-state. It is highly likely that geometric and electronic structural flexibility play a role in the catalytic mechanism. Among the catalytic intermediates that have been identified experimentally thus far, there is clear evidence of such isomorphism in themore » S2 state, with a high-spin (5/2) (HS) and a low spin (1/2) (LS) form, identified and characterized by their distinct electron paramagnetic resonance (EPR spectroscopy) signals. We studied these two S2 isomers with Mn extended X-ray absorption fine structure (EXAFS) and absorption and emission spectroscopy (XANES/XES) to characterize the structural and electronic structural properties. The geometric and electronic structure of the HS and LS S2 states are different as determined using Mn EXAFS and XANES/XES, respectively. The Mn K-edge XANES and XES for the HS form are different from the LS and indicate a slightly lower positive charge on the Mn atoms compared to the LS form. Based on the EXAFS results which are clearly different, we propose possible structural differences between the two spin states. As a result, such structural and magnetic redox-isomers if present at room temperature, will likely play a role in the mechanism for water-exchange/oxidation in photosynthesis.« less

Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking.

PubMed

Hu, Weiming; Gao, Jin; Xing, Junliang; Zhang, Chao; Maybank, Stephen

2017-01-01

An appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a two-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learning- based semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object's appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm.

Measuring Primary Students' Graph Interpretation Skills Via a Performance Assessment: A case study in instrument development

NASA Astrophysics Data System (ADS)

Peterman, Karen; Cranston, Kayla A.; Pryor, Marie; Kermish-Allen, Ruth

2015-11-01

This case study was conducted within the context of a place-based education project that was implemented with primary school students in the USA. The authors and participating teachers created a performance assessment of standards-aligned tasks to examine 6-10-year-old students' graph interpretation skills as part of an exploratory research project. Fifty-five students participated in a performance assessment interview at the beginning and end of a place-based investigation. Two forms of the assessment were created and counterbalanced within class at pre and post. In situ scoring was conducted such that responses were scored as correct versus incorrect during the assessment's administration. Criterion validity analysis demonstrated an age-level progression in student scores. Tests of discriminant validity showed that the instrument detected variability in interpretation skills across each of three graph types (line, bar, dot plot). Convergent validity was established by correlating in situ scores with those from the Graph Interpretation Scoring Rubric. Students' proficiency with interpreting different types of graphs matched expectations based on age and the standards-based progression of graphs across primary school grades. The assessment tasks were also effective at detecting pre-post gains in students' interpretation of line graphs and dot plots after the place-based project. The results of the case study are discussed in relation to the common challenges associated with performance assessment. Implications are presented in relation to the need for authentic and performance-based instructional and assessment tasks to respond to the Common Core State Standards and the Next Generation Science Standards.

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.

Distributed Computation of the knn Graph for Large High-Dimensional Point Sets

PubMed Central

Plaku, Erion; Kavraki, Lydia E.

2009-01-01

High-dimensional problems arising from robot motion planning, biology, data mining, and geographic information systems often require the computation of k nearest neighbor (knn) graphs. The knn graph of a data set is obtained by connecting each point to its k closest points. As the research in the above-mentioned fields progressively addresses problems of unprecedented complexity, the demand for computing knn graphs based on arbitrary distance metrics and large high-dimensional data sets increases, exceeding resources available to a single machine. In this work we efficiently distribute the computation of knn graphs for clusters of processors with message passing. Extensions to our distributed framework include the computation of graphs based on other proximity queries, such as approximate knn or range queries. Our experiments show nearly linear speedup with over one hundred processors and indicate that similar speedup can be obtained with several hundred processors. PMID:19847318

Experimental demonstration of graph-state quantum secret sharing.

PubMed

Bell, B A; Markham, D; Herrera-Martí, D A; Marin, A; Wadsworth, W J; Rarity, J G; Tame, M S

2014-11-21

Quantum communication and computing offer many new opportunities for information processing in a connected world. Networks using quantum resources with tailor-made entanglement structures have been proposed for a variety of tasks, including distributing, sharing and processing information. Recently, a class of states known as graph states has emerged, providing versatile quantum resources for such networking tasks. Here we report an experimental demonstration of graph state-based quantum secret sharing--an important primitive for a quantum network with applications ranging from secure money transfer to multiparty quantum computation. We use an all-optical setup, encoding quantum information into photons representing a five-qubit graph state. We find that one can reliably encode, distribute and share quantum information amongst four parties, with various access structures based on the complex connectivity of the graph. Our results show that graph states are a promising approach for realising sophisticated multi-layered communication protocols in quantum networks.

The investigation of social networks based on multi-component random graphs

NASA Astrophysics Data System (ADS)

Zadorozhnyi, V. N.; Yudin, E. B.

2018-01-01

The methods of non-homogeneous random graphs calibration are developed for social networks simulation. The graphs are calibrated by the degree distributions of the vertices and the edges. The mathematical foundation of the methods is formed by the theory of random graphs with the nonlinear preferential attachment rule and the theory of Erdôs-Rényi random graphs. In fact, well-calibrated network graph models and computer experiments with these models would help developers (owners) of the networks to predict their development correctly and to choose effective strategies for controlling network projects.

Multi-phase simultaneous segmentation of tumor in lung 4D-CT data with context information.

PubMed

Shen, Zhengwen; Wang, Huafeng; Xi, Weiwen; Deng, Xiaogang; Chen, Jin; Zhang, Yu

2017-01-01

Lung 4D computed tomography (4D-CT) plays an important role in high-precision radiotherapy because it characterizes respiratory motion, which is crucial for accurate target definition. However, the manual segmentation of a lung tumor is a heavy workload for doctors because of the large number of lung 4D-CT data slices. Meanwhile, tumor segmentation is still a notoriously challenging problem in computer-aided diagnosis. In this paper, we propose a new method based on an improved graph cut algorithm with context information constraint to find a convenient and robust approach of lung 4D-CT tumor segmentation. We combine all phases of the lung 4D-CT into a global graph, and construct a global energy function accordingly. The sub-graph is first constructed for each phase. A context cost term is enforced to achieve segmentation results in every phase by adding a context constraint between neighboring phases. A global energy function is finally constructed by combining all cost terms. The optimization is achieved by solving a max-flow/min-cut problem, which leads to simultaneous and robust segmentation of the tumor in all the lung 4D-CT phases. The effectiveness of our approach is validated through experiments on 10 different lung 4D-CT cases. The comparison with the graph cut without context constraint, the level set method and the graph cut with star shape prior demonstrates that the proposed method obtains more accurate and robust segmentation results.

Robust Algorithms for on Minor-Free Graphs Based on the Sherali-Adams Hierarchy

NASA Astrophysics Data System (ADS)

Magen, Avner; Moharrami, Mohammad

This work provides a Linear Programming-based Polynomial Time Approximation Scheme (PTAS) for two classical NP-hard problems on graphs when the input graph is guaranteed to be planar, or more generally Minor Free. The algorithm applies a sufficiently large number (some function of when approximation is required) of rounds of the so-called Sherali-Adams Lift-and-Project system. needed to obtain a -approximation, where f is some function that depends only on the graph that should be avoided as a minor. The problem we discuss are the well-studied problems, the and problems. An curious fact we expose is that in the world of minor-free graph, the is harder in some sense than the.

A unified framework for building high performance DVEs

NASA Astrophysics Data System (ADS)

Lei, Kaibin; Ma, Zhixia; Xiong, Hua

2011-10-01

A unified framework for integrating PC cluster based parallel rendering with distributed virtual environments (DVEs) is presented in this paper. While various scene graphs have been proposed in DVEs, it is difficult to enable collaboration of different scene graphs. This paper proposes a technique for non-distributed scene graphs with the capability of object and event distribution. With the increase of graphics data, DVEs require more powerful rendering ability. But general scene graphs are inefficient in parallel rendering. The paper also proposes a technique to connect a DVE and a PC cluster based parallel rendering environment. A distributed multi-player video game is developed to show the interaction of different scene graphs and the parallel rendering performance on a large tiled display wall.

Writing and overwriting short-term memory

PubMed Central

Killeen, Peter R.

2008-01-01

An integrative account of short-term memory is based on data from pigeons trained to report the majority color in a sequence of lights. Performance showed strong recency effects, was invariant over changes in the interstimulus interval, and improved with increases in the intertrial interval. A compound model of binomial variance around geometrically decreasing memory described the data; a logit transformation rendered it isomorphic with other memory models. The model was generalized for variance in the parameters, where it was shown that averaging exponential and power functions from individuals or items with different decay rates generates new functions that are hyperbolic in time and in log time, respectively. The compound model provides a unified treatment of both the accrual and the dissipation of memory and is consistent with data from various experiments, including the choose-short bias in delayed recall, multielement stimuli, and Rubin and Wenzel’s (1996) meta-analyses of forgetting. PMID:11340865

A sediment graph model based on SCS-CN method

NASA Astrophysics Data System (ADS)

Singh, P. K.; Bhunya, P. K.; Mishra, S. K.; Chaube, U. C.

2008-01-01

SummaryThis paper proposes new conceptual sediment graph models based on coupling of popular and extensively used methods, viz., Nash model based instantaneous unit sediment graph (IUSG), soil conservation service curve number (SCS-CN) method, and Power law. These models vary in their complexity and this paper tests their performance using data of the Nagwan watershed (area = 92.46 km 2) (India). The sensitivity of total sediment yield and peak sediment flow rate computations to model parameterisation is analysed. The exponent of the Power law, β, is more sensitive than other model parameters. The models are found to have substantial potential for computing sediment graphs (temporal sediment flow rate distribution) as well as total sediment yield.

Semantic Drift in Espresso-style Bootstrapping: Graph-theoretic Analysis and Evaluation in Word Sense Disambiguation

NASA Astrophysics Data System (ADS)

Komachi, Mamoru; Kudo, Taku; Shimbo, Masashi; Matsumoto, Yuji

Bootstrapping has a tendency, called semantic drift, to select instances unrelated to the seed instances as the iteration proceeds. We demonstrate the semantic drift of Espresso-style bootstrapping has the same root as the topic drift of Kleinberg's HITS, using a simplified graph-based reformulation of bootstrapping. We confirm that two graph-based algorithms, the von Neumann kernels and the regularized Laplacian, can reduce the effect of semantic drift in the task of word sense disambiguation (WSD) on Senseval-3 English Lexical Sample Task. Proposed algorithms achieve superior performance to Espresso and previous graph-based WSD methods, even though the proposed algorithms have less parameters and are easy to calibrate.

Brain Graph Topology Changes Associated with Anti-Epileptic Drug Use

PubMed Central

Levin, Harvey S.; Chiang, Sharon

2015-01-01

Abstract Neuroimaging studies of functional connectivity using graph theory have furthered our understanding of the network structure in temporal lobe epilepsy (TLE). Brain network effects of anti-epileptic drugs could influence such studies, but have not been systematically studied. Resting-state functional MRI was analyzed in 25 patients with TLE using graph theory analysis. Patients were divided into two groups based on anti-epileptic medication use: those taking carbamazepine/oxcarbazepine (CBZ/OXC) (n=9) and those not taking CBZ/OXC (n=16) as a part of their medication regimen. The following graph topology metrics were analyzed: global efficiency, betweenness centrality (BC), clustering coefficient, and small-world index. Multiple linear regression was used to examine the association of CBZ/OXC with graph topology. The two groups did not differ from each other based on epilepsy characteristics. Use of CBZ/OXC was associated with a lower BC. Longer epilepsy duration was also associated with a lower BC. These findings can inform graph theory-based studies in patients with TLE. The changes observed are discussed in relation to the anti-epileptic mechanism of action and adverse effects of CBZ/OXC. PMID:25492633

Graphs and Tracks Revisited

NASA Astrophysics Data System (ADS)

Christian, Wolfgang; Belloni, Mario

2013-04-01

We have recently developed a Graphs and Tracks model based on an earlier program by David Trowbridge, as shown in Fig. 1. Our model can show position, velocity, acceleration, and energy graphs and can be used for motion-to-graphs exercises. Users set the heights of the track segments, and the model displays the motion of the ball on the track together with position, velocity, and acceleration graphs. This ready-to-run model is available in the ComPADRE OSP Collection at www.compadre.org/osp/items/detail.cfm?ID=12023.

Program for Generating Graphs and Charts

NASA Technical Reports Server (NTRS)

Ackerson, C. T.

1986-01-01

Office Automation Pilot (OAP) Graphics Database system offers IBM personal computer user assistance in producing wide variety of graphs and charts and convenient data-base system, called chart base, for creating and maintaining data associated with graphs and charts. Thirteen different graphics packages available. Access graphics capabilities obtained in similar manner. User chooses creation, revision, or chartbase-maintenance options from initial menu; Enters or modifies data displayed on graphic chart. OAP graphics data-base system written in Microsoft PASCAL.

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

ESIP's Earth Science Knowledge Graph (ESKG) Testbed Project: An Automatic Approach to Building Interdisciplinary Earth Science Knowledge Graphs to Improve Data Discovery

NASA Astrophysics Data System (ADS)

McGibbney, L. J.; Jiang, Y.; Burgess, A. B.

2017-12-01

Big Earth observation data have been produced, archived and made available online, but discovering the right data in a manner that precisely and efficiently satisfies user needs presents a significant challenge to the Earth Science (ES) community. An emerging trend in information retrieval community is to utilize knowledge graphs to assist users in quickly finding desired information from across knowledge sources. This is particularly prevalent within the fields of social media and complex multimodal information processing to name but a few, however building a domain-specific knowledge graph is labour-intensive and hard to keep up-to-date. In this work, we update our progress on the Earth Science Knowledge Graph (ESKG) project; an ESIP-funded testbed project which provides an automatic approach to building a dynamic knowledge graph for ES to improve interdisciplinary data discovery by leveraging implicit, latent existing knowledge present within across several U.S Federal Agencies e.g. NASA, NOAA and USGS. ESKG strengthens ties between observations and user communities by: 1) developing a knowledge graph derived from various sources e.g. Web pages, Web Services, etc. via natural language processing and knowledge extraction techniques; 2) allowing users to traverse, explore, query, reason and navigate ES data via knowledge graph interaction. ESKG has the potential to revolutionize the way in which ES communities interact with ES data in the open world through the entity, spatial and temporal linkages and characteristics that make it up. This project enables the advancement of ESIP collaboration areas including both Discovery and Semantic Technologies by putting graph information right at our fingertips in an interactive, modern manner and reducing the efforts to constructing ontology. To demonstrate the ESKG concept, we will demonstrate use of our framework across NASA JPL's PO.DAAC, NOAA's Earth Observation Requirements Evaluation System (EORES) and various USGS systems.

AUTOMATED ANALYSIS OF QUANTITATIVE IMAGE DATA USING ISOMORPHIC FUNCTIONAL MIXED MODELS, WITH APPLICATION TO PROTEOMICS DATA.

PubMed

Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Herrick, Richard C; Sanna, Pietro; Gutstein, Howard

2011-01-01

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper, we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate. Although the method we present is general and can be applied to quantitative image data from any application, in this paper we focus on image-based proteomic data. We apply our method to an animal study investigating the effects of opiate addiction on the brain proteome. Our image-based functional mixed model approach finds results that are missed with conventional spot-based analysis approaches. In particular, we find that the significant regions of the image identified by the proposed method frequently correspond to subregions of visible spots that may represent post-translational modifications or co-migrating proteins that cannot be visually resolved from adjacent, more abundant proteins on the gel image. Thus, it is possible that this image-based approach may actually improve the realized resolution of the gel, revealing differentially expressed proteins that would not have even been detected as spots by modern spot-based analyses.

Network reconstruction via graph blending

NASA Astrophysics Data System (ADS)

Estrada, Rolando

2016-05-01

Graphs estimated from empirical data are often noisy and incomplete due to the difficulty of faithfully observing all the components (nodes and edges) of the true graph. This problem is particularly acute for large networks where the number of components may far exceed available surveillance capabilities. Errors in the observed graph can render subsequent analyses invalid, so it is vital to develop robust methods that can minimize these observational errors. Errors in the observed graph may include missing and spurious components, as well fused (multiple nodes are merged into one) and split (a single node is misinterpreted as many) nodes. Traditional graph reconstruction methods are only able to identify missing or spurious components (primarily edges, and to a lesser degree nodes), so we developed a novel graph blending framework that allows us to cast the full estimation problem as a simple edge addition/deletion problem. Armed with this framework, we systematically investigate the viability of various topological graph features, such as the degree distribution or the clustering coefficients, and existing graph reconstruction methods for tackling the full estimation problem. Our experimental results suggest that incorporating any topological feature as a source of information actually hinders reconstruction accuracy. We provide a theoretical analysis of this phenomenon and suggest several avenues for improving this estimation problem.

Integer Flows and Circuit Covers of Graphs and Signed Graphs

NASA Astrophysics Data System (ADS)

Cheng, Jian

The work in Chapter 2 is motivated by Tutte and Jaeger's pioneering work on converting modulo flows into integer-valued flows for ordinary graphs. For a signed graphs (G, sigma), we first prove that for each k ∈ {2, 3}, if (G, sigma) is (k - 1)-edge-connected and contains an even number of negative edges when k = 2, then every modulo k-flow of (G, sigma) can be converted into an integer-valued ( k + 1)-ow with a larger or the same support. We also prove that if (G, sigma) is odd-(2p+1)-edge-connected, then (G, sigma) admits a modulo circular (2 + 1/ p)-flows if and only if it admits an integer-valued circular (2 + 1/p)-flows, which improves all previous result by Xu and Zhang (DM2005), Schubert and Steffen (EJC2015), and Zhu (JCTB2015). Shortest circuit cover conjecture is one of the major open problems in graph theory. It states that every bridgeless graph G contains a set of circuits F such that each edge is contained in at least one member of F and the length of F is at most 7/5∥E(G)∥. This concept was recently generalized to signed graphs by Macajova et al. (JGT2015). In Chapter 3, we improve their upper bound from 11∥E( G)∥ to 14/3 ∥E(G)∥, and if G is 2-edgeconnected and has even negativeness, then it can be further reduced to 11/3 ∥E(G)∥. Tutte's 3-flow conjecture has been studied by many graph theorists in the last several decades. As a new approach to this conjecture, DeVos and Thomassen considered the vectors as ow values and found that there is a close relation between vector S1-flows and integer 3-NZFs. Motivated by their observation, in Chapter 4, we prove that if a graph G admits a vector S1-flow with rank at most two, then G admits an integer 3-NZF. The concept of even factors is highly related to the famous Four Color Theorem. We conclude this dissertation in Chapter 5 with an improvement of a recent result by Chen and Fan (JCTB2016) on the upperbound of even factors. We show that if a graph G contains an even factor, then it contains an even factor H with. ∥E(H)∥ ≥ 4/7 (∥ E(G)∥+1)+ 1/7 ∥V2 (G)∥, where V2( G) is the set of vertices of degree two.

Exotic equilibria of Harary graphs and a new minimum degree lower bound for synchronization

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

Canale, Eduardo A., E-mail: ecanale@pol.una.py; Monzón, Pablo, E-mail: monzon@fing.edu.uy

2015-02-15

This work is concerned with stability of equilibria in the homogeneous (equal frequencies) Kuramoto model of weakly coupled oscillators. In 2012 [R. Taylor, J. Phys. A: Math. Theor. 45, 1–15 (2012)], a sufficient condition for almost global synchronization was found in terms of the minimum degree–order ratio of the graph. In this work, a new lower bound for this ratio is given. The improvement is achieved by a concrete infinite sequence of regular graphs. Besides, non standard unstable equilibria of the graphs studied in Wiley et al. [Chaos 16, 015103 (2006)] are shown to exist as conjectured in that work.

Phase-Space Detection of Cyber Events

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

Hernandez Jimenez, Jarilyn M; Ferber, Aaron E; Prowell, Stacy J

Energy Delivery Systems (EDS) are a network of processes that produce, transfer and distribute energy. EDS are increasingly dependent on networked computing assets, as are many Industrial Control Systems. Consequently, cyber-attacks pose a real and pertinent threat, as evidenced by Stuxnet, Shamoon and Dragonfly. Hence, there is a critical need for novel methods to detect, prevent, and mitigate effects of such attacks. To detect cyber-attacks in EDS, we developed a framework for gathering and analyzing timing data that involves establishing a baseline execution profile and then capturing the effect of perturbations in the state from injecting various malware. The datamore » analysis was based on nonlinear dynamics and graph theory to improve detection of anomalous events in cyber applications. The goal was the extraction of changing dynamics or anomalous activity in the underlying computer system. Takens' theorem in nonlinear dynamics allows reconstruction of topologically invariant, time-delay-embedding states from the computer data in a sufficiently high-dimensional space. The resultant dynamical states were nodes, and the state-to-state transitions were links in a mathematical graph. Alternatively, sequential tabulation of executing instructions provides the nodes with corresponding instruction-to-instruction links. Graph theorems guarantee graph-invariant measures to quantify the dynamical changes in the running applications. Results showed a successful detection of cyber events.« less

Multi-scale graph-cut algorithm for efficient water-fat separation.

PubMed

Berglund, Johan; Skorpil, Mikael

2017-09-01

To improve the accuracy and robustness to noise in water-fat separation by unifying the multiscale and graph cut based approaches to B 0 -correction. A previously proposed water-fat separation algorithm that corrects for B 0 field inhomogeneity in 3D by a single quadratic pseudo-Boolean optimization (QPBO) graph cut was incorporated into a multi-scale framework, where field map solutions are propagated from coarse to fine scales for voxels that are not resolved by the graph cut. The accuracy of the single-scale and multi-scale QPBO algorithms was evaluated against benchmark reference datasets. The robustness to noise was evaluated by adding noise to the input data prior to water-fat separation. Both algorithms achieved the highest accuracy when compared with seven previously published methods, while computation times were acceptable for implementation in clinical routine. The multi-scale algorithm was more robust to noise than the single-scale algorithm, while causing only a small increase (+10%) of the reconstruction time. The proposed 3D multi-scale QPBO algorithm offers accurate water-fat separation, robustness to noise, and fast reconstruction. The software implementation is freely available to the research community. Magn Reson Med 78:941-949, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Faster Parameterized Algorithms for Minor Containment

NASA Astrophysics Data System (ADS)

Adler, Isolde; Dorn, Frederic; Fomin, Fedor V.; Sau, Ignasi; Thilikos, Dimitrios M.

The theory of Graph Minors by Robertson and Seymour is one of the deepest and significant theories in modern Combinatorics. This theory has also a strong impact on the recent development of Algorithms, and several areas, like Parameterized Complexity, have roots in Graph Minors. Until very recently it was a common belief that Graph Minors Theory is mainly of theoretical importance. However, it appears that many deep results from Robertson and Seymour's theory can be also used in the design of practical algorithms. Minor containment testing is one of algorithmically most important and technical parts of the theory, and minor containment in graphs of bounded branchwidth is a basic ingredient of this algorithm. In order to implement minor containment testing on graphs of bounded branchwidth, Hicks [NETWORKS 04] described an algorithm, that in time O(3^{k^2}\\cdot (h+k-1)!\\cdot m) decides if a graph G with m edges and branchwidth k, contains a fixed graph H on h vertices as a minor. That algorithm follows the ideas introduced by Robertson and Seymour in [J'CTSB 95]. In this work we improve the dependence on k of Hicks' result by showing that checking if H is a minor of G can be done in time O(2^{(2k +1 )\\cdot log k} \\cdot h^{2k} \\cdot 2^{2h^2} \\cdot m). Our approach is based on a combinatorial object called rooted packing, which captures the properties of the potential models of subgraphs of H that we seek in our dynamic programming algorithm. This formulation with rooted packings allows us to speed up the algorithm when G is embedded in a fixed surface, obtaining the first single-exponential algorithm for minor containment testing. Namely, it runs in time 2^{O(k)} \\cdot h^{2k} \\cdot 2^{O(h)} \\cdot n, with n = |V(G)|. Finally, we show that slight modifications of our algorithm permit to solve some related problems within the same time bounds, like induced minor or contraction minor containment.

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

PubMed

Bakal, Gokhan; Talari, Preetham; Kakani, Elijah V; Kavuluru, Ramakanth

2018-06-01

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. We used 7000 treats and 2918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80-20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. We employed semantic graph patterns connecting pairs of candidate biomedical entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction. Copyright © 2018 Elsevier Inc. All rights reserved.

GraphSAW: a web-based system for graphical analysis of drug interactions and side effects using pharmaceutical and molecular data.

PubMed

Shoshi, Alban; Hoppe, Tobias; Kormeier, Benjamin; Ogultarhan, Venus; Hofestädt, Ralf

2015-02-28

Adverse drug reactions are one of the most common causes of death in industrialized Western countries. Nowadays, empirical data from clinical studies for the approval and monitoring of drugs and molecular databases is available. The integration of database information is a promising method for providing well-based knowledge to avoid adverse drug reactions. This paper presents our web-based decision support system GraphSAW which analyzes and evaluates drug interactions and side effects based on data from two commercial and two freely available molecular databases. The system is able to analyze single and combined drug-drug interactions, drug-molecule interactions as well as single and cumulative side effects. In addition, it allows exploring associative networks of drugs, molecules, metabolic pathways, and diseases in an intuitive way. The molecular medication analysis includes the capabilities of the upper features. A statistical evaluation of the integrated data and top 20 drugs concerning drug interactions and side effects is performed. The results of the data analysis give an overview of all theoretically possible drug interactions and side effects. The evaluation shows a mismatch between pharmaceutical and molecular databases. The concordance of drug interactions was about 12% and 9% of drug side effects. An application case with prescribed data of 11 patients is presented in order to demonstrate the functionality of the system under real conditions. For each patient at least two interactions occured in every medication and about 8% of total diseases were possibly induced by drug therapy. GraphSAW (http://tunicata.techfak.uni-bielefeld.de/graphsaw/) is meant to be a web-based system for health professionals and researchers. GraphSAW provides comprehensive drug-related knowledge and an improved medication analysis which may support efforts to reduce the risk of medication errors and numerous drastic side effects.

The Effects of Low to Moderate Intensity Aerobic Exercise on Fatigue in Breast Cancer Patients Following Clinical Treatment

DTIC Science & Technology

2006-09-01

between the groups (p = .0003) and within the groups (p =. 01). The decrease in depression in the exercise group (89%) neared significance (p = .052...exercise program was effective in improving aerobic capacity, lower-body flexibility, fatigue, depression , anxiety, confusion, anger, and energy in the...Appendix F: 10-week LASA Fatigue Graph Appendix G: 10-week LASA Depression Graph Appendix H: 10-week LASA Anxiety Graph Appendix I: 10-week

Shared-hole graph search with adaptive constraints for 3D optic nerve head optical coherence tomography image segmentation

PubMed Central

Yu, Kai; Shi, Fei; Gao, Enting; Zhu, Weifang; Chen, Haoyu; Chen, Xinjian

2018-01-01

Optic nerve head (ONH) is a crucial region for glaucoma detection and tracking based on spectral domain optical coherence tomography (SD-OCT) images. In this region, the existence of a “hole” structure makes retinal layer segmentation and analysis very challenging. To improve retinal layer segmentation, we propose a 3D method for ONH centered SD-OCT image segmentation, which is based on a modified graph search algorithm with a shared-hole and locally adaptive constraints. With the proposed method, both the optic disc boundary and nine retinal surfaces can be accurately segmented in SD-OCT images. An overall mean unsigned border positioning error of 7.27 ± 5.40 µm was achieved for layer segmentation, and a mean Dice coefficient of 0.925 ± 0.03 was achieved for optic disc region detection. PMID:29541497

Constructing Temporally Extended Actions through Incremental Community Detection

PubMed Central

Li, Ge

2018-01-01

Hierarchical reinforcement learning works on temporally extended actions or skills to facilitate learning. How to automatically form such abstraction is challenging, and many efforts tackle this issue in the options framework. While various approaches exist to construct options from different perspectives, few of them concentrate on options' adaptability during learning. This paper presents an algorithm to create options and enhance their quality online. Both aspects operate on detected communities of the learning environment's state transition graph. We first construct options from initial samples as the basis of online learning. Then a rule-based community revision algorithm is proposed to update graph partitions, based on which existing options can be continuously tuned. Experimental results in two problems indicate that options from initial samples may perform poorly in more complex environments, and our presented strategy can effectively improve options and get better results compared with flat reinforcement learning. PMID:29849543

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

A graph edit dictionary for correcting errors in roof topology graphs reconstructed from point clouds

NASA Astrophysics Data System (ADS)

Xiong, B.; Oude Elberink, S.; Vosselman, G.

2014-07-01

In the task of 3D building model reconstruction from point clouds we face the problem of recovering a roof topology graph in the presence of noise, small roof faces and low point densities. Errors in roof topology graphs will seriously affect the final modelling results. The aim of this research is to automatically correct these errors. We define the graph correction as a graph-to-graph problem, similar to the spelling correction problem (also called the string-to-string problem). The graph correction is more complex than string correction, as the graphs are 2D while strings are only 1D. We design a strategy based on a dictionary of graph edit operations to automatically identify and correct the errors in the input graph. For each type of error the graph edit dictionary stores a representative erroneous subgraph as well as the corrected version. As an erroneous roof topology graph may contain several errors, a heuristic search is applied to find the optimum sequence of graph edits to correct the errors one by one. The graph edit dictionary can be expanded to include entries needed to cope with errors that were previously not encountered. Experiments show that the dictionary with only fifteen entries already properly corrects one quarter of erroneous graphs in about 4500 buildings, and even half of the erroneous graphs in one test area, achieving as high as a 95% acceptance rate of the reconstructed models.

A technology mapping based on graph of excitations and outputs for finite state machines

NASA Astrophysics Data System (ADS)

Kania, Dariusz; Kulisz, Józef

2017-11-01

A new, efficient technology mapping method of FSMs, dedicated for PAL-based PLDs is proposed. The essence of the method consists in searching for the minimal set of PAL-based logic blocks that cover a set of multiple-output implicants describing the transition and output functions of an FSM. The method is based on a new concept of graph: the Graph of Excitations and Outputs. The proposed algorithm was tested using the FSM benchmarks. The obtained results were compared with the classical technology mapping of FSM.

Markov random fields and graphs for uncertainty management and symbolic data fusion in an urban scene interpretation

NASA Astrophysics Data System (ADS)

Moissinac, Henri; Maitre, Henri; Bloch, Isabelle

1995-11-01

An image interpretation method is presented for the automatic processing of aerial pictures of a urban landscape. In order to improve the picture analysis, some a priori knowledge extracted from a geographic map is introduced. A coherent graph-based model of the city is built, starting with the road network. A global uncertainty management scheme has been designed in order to evaluate the final confidence we can have in the final results. This model and the uncertainty management tend to reflect the hierarchy of the available data and the interpretation levels. The symbolic relationships linking the different kinds of elements are taken into account while propagating and combining the confidence measures along the interpretation process.

Residuals-Based Subgraph Detection with Cue Vertices

DTIC Science & Technology

2015-11-30

Workshop, 2012, pp. 129–132. [5] M. E. J. Newman , “Finding community structure in networks using the eigenvectors of matrices,” Phys. Rev. E, vol. 74, no...from Data, vol. 1, no. 1, 2007. [7] M. W. Mahoney , L. Orecchia, and N. K. Vishnoi, “A spectral algorithm for improving graph partitions,” CoRR, vol. abs

Large-scale DCMs for resting-state fMRI.

PubMed

Razi, Adeel; Seghier, Mohamed L; Zhou, Yuan; McColgan, Peter; Zeidman, Peter; Park, Hae-Jeong; Sporns, Olaf; Rees, Geraint; Friston, Karl J

2017-01-01

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity . This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM-with functional connectivity priors-is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

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.

Planification de trajectoires pour une flotte d'UAVs

NASA Astrophysics Data System (ADS)

Ait El Cadi, Abdessamad

In this thesis we address the problem of coordinating and controlling a fleet of Unmanned Aerial Vehicles (UAVs) during a surveillance mission in a dynamic context. The problem is vast and is related to several scientific domains. We have studied three important parts of this problem: • modeling the ground with all its constraints; • computing a shortest non-holonomic continuous path in a risky environment with a presence of obstacles; • planning a surveillance mission for a fleet of UAVs in a real context. While investigating the scientific literature related to these topics, we have detected deficiencies in the modeling of the ground and in the computation of the shortest continuous path, two critical aspects for the planning of a mission. So after the literature review, we have proposed answers to these two aspects and have applied our developments to the planning of a mission of a fleet of UAVs in a risky environment with the presence of obstacles. Obstacles could be natural like mountain or any non flyable zone. We have first modeled the ground as a directed graph. However, instead of using a classic mesh, we opted for an intelligent modeling that reduces the computing time on the graph without losing accuracy. The proposed model is based on the concept of visibility graph, and it also takes into account the obstacles, the danger areas and the constraint of non-holonomy of the UAVs- the kinematic constraint of the planes that imposes a maximum steering angle. The graph is then cleaned to keep only the minimum information needed for the calculation of trajectories. The generation of this graph possibly requires a lot of computation time, but it is done only once before the planning and will not affect the performance of trajectory calculations. We have also developed another simpler graph that does not take into account the constraint of non-holonomy. The advantage of this second graph is that it reduces the computation time. However, it requires the use of a correction procedure to make the resulting trajectory non-holonomic. This correction is possible within the context of our missions, but not for all types of autonomous vehicles. Once the directed graph is generated, we propose the use of a procedure for calculating the shortest continuous non-holonomic path in a risky environment with the presence of obstacles. The directed graph already incorporates all the constraints, which makes it possible to model the problem as a shortest path problem with resource a resource constraint (the resource here is the amount of permitted risk). The results are very satisfactory since the resulting routes are non-holonomic paths that meet all constraints. Moreover, the computing time is very short. For cases based on the simpler graph, we have created a procedure for correcting the trajectory to make it non-holonomic. All calculations of non-holonomy are based on Dubins curves (1957). We have finally applied our results to the planning of a mission of a fleet of UAVs in a risky environment with the presence of obstacles. For this purpose, we have developed a directed multi-graph where, for each pair of targets (points of departure and return of the mission included), we calculate a series of shorter trajectories with different limits of risk -- from the risk-free path to the riskiest path. We then use a Tabu Search with two tabu lists. Using these procedures, we have been able to produce routes for a fleet of UAVs that minimize the cost of the mission while respecting the limit of risk and avoiding obstacles. Tests are conducted on examples created on the basis of descriptions given by the Canadian Defense and, also on some instances of the CVRP (Capacitated Vehicle Routing Problem), those described by Christofides et Elion and those described by Christofides, Mingozzi et Toth. The results are of very satisfactory since all trajectories are non-holonomic and the improvement of the objective, when compared to a simple constructive method, achieves in some cases between 10 % and 43 %. We have even obtained an improvement of 69 %, but on a poor solution generated by a greedy algorithm. (Abstract shortened by UMI.)

Can multilinguality improve Biomedical Word Sense Disambiguation?

PubMed

Duque, Andres; Martinez-Romo, Juan; Araujo, Lourdes

2016-12-01

Ambiguity in the biomedical domain represents a major issue when performing Natural Language Processing tasks over the huge amount of available information in the field. For this reason, Word Sense Disambiguation is critical for achieving accurate systems able to tackle complex tasks such as information extraction, summarization or document classification. In this work we explore whether multilinguality can help to solve the problem of ambiguity, and the conditions required for a system to improve the results obtained by monolingual approaches. Also, we analyze the best ways to generate those useful multilingual resources, and study different languages and sources of knowledge. The proposed system, based on co-occurrence graphs containing biomedical concepts and textual information, is evaluated on a test dataset frequently used in biomedicine. We can conclude that multilingual resources are able to provide a clear improvement of more than 7% compared to monolingual approaches, for graphs built from a small number of documents. Also, empirical results show that automatically translated resources are a useful source of information for this particular task. Copyright © 2016 Elsevier Inc. All rights reserved.

Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms

NASA Astrophysics Data System (ADS)

Gao, Connie W.; Allen, Joshua W.; Green, William H.; West, Richard H.

2016-06-01

Reaction Mechanism Generator (RMG) constructs kinetic models composed of elementary chemical reaction steps using a general understanding of how molecules react. Species thermochemistry is estimated through Benson group additivity and reaction rate coefficients are estimated using a database of known rate rules and reaction templates. At its core, RMG relies on two fundamental data structures: graphs and trees. Graphs are used to represent chemical structures, and trees are used to represent thermodynamic and kinetic data. Models are generated using a rate-based algorithm which excludes species from the model based on reaction fluxes. RMG can generate reaction mechanisms for species involving carbon, hydrogen, oxygen, sulfur, and nitrogen. It also has capabilities for estimating transport and solvation properties, and it automatically computes pressure-dependent rate coefficients and identifies chemically-activated reaction paths. RMG is an object-oriented program written in Python, which provides a stable, robust programming architecture for developing an extensible and modular code base with a large suite of unit tests. Computationally intensive functions are cythonized for speed improvements.

WE-E-BRE-05: Ensemble of Graphical Models for Predicting Radiation Pneumontis Risk

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

Lee, S; Ybarra, N; Jeyaseelan, K

Purpose: We propose a prior knowledge-based approach to construct an interaction graph of biological and dosimetric radiation pneumontis (RP) covariates for the purpose of developing a RP risk classifier. Methods: We recruited 59 NSCLC patients who received curative radiotherapy with minimum 6 month follow-up. 16 RP events was observed (CTCAE grade ≥2). Blood serum was collected from every patient before (pre-RT) and during RT (mid-RT). From each sample the concentration of the following five candidate biomarkers were taken as covariates: alpha-2-macroglobulin (α2M), angiotensin converting enzyme (ACE), transforming growth factor β (TGF-β), interleukin-6 (IL-6), and osteopontin (OPN). Dose-volumetric parameters were alsomore » included as covariates. The number of biological and dosimetric covariates was reduced by a variable selection scheme implemented by L1-regularized logistic regression (LASSO). Posterior probability distribution of interaction graphs between the selected variables was estimated from the data under the literature-based prior knowledge to weight more heavily the graphs that contain the expected associations. A graph ensemble was formed by averaging the most probable graphs weighted by their posterior, creating a Bayesian Network (BN)-based RP risk classifier. Results: The LASSO selected the following 7 RP covariates: (1) pre-RT concentration level of α2M, (2) α2M level mid- RT/pre-RT, (3) pre-RT IL6 level, (4) IL6 level mid-RT/pre-RT, (5) ACE mid-RT/pre-RT, (6) PTV volume, and (7) mean lung dose (MLD). The ensemble BN model achieved the maximum sensitivity/specificity of 81%/84% and outperformed univariate dosimetric predictors as shown by larger AUC values (0.78∼0.81) compared with MLD (0.61), V20 (0.65) and V30 (0.70). The ensembles obtained by incorporating the prior knowledge improved classification performance for the ensemble size 5∼50. Conclusion: We demonstrated a probabilistic ensemble method to detect robust associations between RP covariates and its potential to improve RP prediction accuracy. Our Bayesian approach to incorporate prior knowledge can enhance efficiency in searching of such associations from data. The authors acknowledge partial support by: 1) CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290) and 2) The Terry Fox Foundation Strategic Training Initiative for Excellence in Radiation Research for the 21st Century (EIRR21)« less

Anisotropic Laplace-Beltrami Eigenmaps: Bridging Reeb Graphs and Skeletons*

PubMed Central

Shi, Yonggang; Lai, Rongjie; Krishna, Sheila; Sicotte, Nancy; Dinov, Ivo; Toga, Arthur W.

2010-01-01

In this paper we propose a novel approach of computing skeletons of robust topology for simply connected surfaces with boundary by constructing Reeb graphs from the eigenfunctions of an anisotropic Laplace-Beltrami operator. Our work brings together the idea of Reeb graphs and skeletons by incorporating a flux-based weight function into the Laplace-Beltrami operator. Based on the intrinsic geometry of the surface, the resulting Reeb graph is pose independent and captures the global profile of surface geometry. Our algorithm is very efficient and it only takes several seconds to compute on neuroanatomical structures such as the cingulate gyrus and corpus callosum. In our experiments, we show that the Reeb graphs serve well as an approximate skeleton with consistent topology while following the main body of conventional skeletons quite accurately. PMID:21339850

Hospital information system institutionalization processes in indonesian public, government-owned and privately owned hospitals.

PubMed

Handayani, P W; Hidayanto, A N; Ayuningtyas, Dumilah; Budi, Indra

2016-11-01

The Hospital Information System (HIS) could help hospitals as a public entity to provide optimal health services. One of the main challenges of HIS implementation is an institutional change. Using institutional theory as the analytical lens, this study aims to explain the institutionalization of HIS as an instance of e-health initiatives in Indonesia. Furthermore, this paper aims for hospital management and researchers to improve the understanding of the social forces that influence hospital personnel's HIS acceptance within an organizational context. We use case studies from four public, government-owned hospitals and four privately owned (public and specialty) hospitals to explain the HIS institutionalization process by exploring the three concepts of institutional theory: institutional isomorphism, institutional logic, and institutional entrepreneurship. This study reveals that differences exist between public, government-owned and private hospitals with regard to the institutionalization process: public, government-owned hospitals' management is more motivated to implement HIS to comply with the regulations, while private hospitals' management views HIS as an urgent requirement that must be achieved. The study findings also reveal that various institutional isomorphism mechanisms and forms of institutional logic emerge during the process. Finally, three factors-self-efficacy, social influence, and management support-have a significant influence on the individual acceptance of HIS. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

An improved approach of register allocation via graph coloring

NASA Astrophysics Data System (ADS)

Gao, Lei; Shi, Ce

2005-03-01

Register allocation is an important part of optimizing compiler. The algorithm of register allocation via graph coloring is implemented by Chaitin and his colleagues firstly and improved by Briggs and others. By abstracting register allocation to graph coloring, the allocation process is simplified. As the physical register number is limited, coloring of the interference graph can"t succeed for every node. The uncolored nodes must be spilled. There is an assumption that almost all the allocation method obeys: when a register is allocated to a variable v, it can"t be used by others before v quit even if v is not used for a long time. This may causes a waste of register resource. The authors relax this restriction under certain conditions and make some improvement. In this method, one register can be mapped to two or more interfered "living" live ranges at the same time if they satisfy some requirements. An operation named merge is defined which can arrange two interfered nodes occupy the same register with some cost. Thus, the resource of register can be used more effectively and the cost of memory access can be reduced greatly.

Multi-Level Anomaly Detection on Time-Varying Graph Data

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

Bridges, Robert A; Collins, John P; Ferragut, Erik M

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, thismore » multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.« less

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 construction method of shipborne and airborne radar intelligence and related equipment knowledge graph

NASA Astrophysics Data System (ADS)

Hao, Ruizhe; Huang, Jian

2017-08-01

Knowledge graph construction in military intelligence domain is sprouting but technically immature. This paper presents a method to construct the heterogeneous knowledge graph in the field of shipborne and airborne radar and equipment. Based on the expert knowledge and the up-to-date Internet open source information, we construct the knowledge graph of radar characteristic information and the equipment respectively, and establish relationships between two graphs, providing the pipeline and method for the intelligence organization and management in the context of the crowding battlefields big data.

Route Prediction on Tracking Data to Location-Based Services

NASA Astrophysics Data System (ADS)

Petróczi, Attila István; Gáspár-Papanek, Csaba

Wireless networks have become so widespread, it is beneficial to determine the ability of cellular networks for localization. This property enables the development of location-based services, providing useful information. These services can be improved by route prediction under the condition of using simple algorithms, because of the limited capabilities of mobile stations. This study gives alternative solutions for this problem of route prediction based on a specific graph model. Our models provide the opportunity to reach our destinations with less effort.

Unimodular lattice triangulations as small-world and scale-free random graphs

NASA Astrophysics Data System (ADS)

Krüger, B.; Schmidt, E. M.; Mecke, K.

2015-02-01

Real-world networks, e.g., the social relations or world-wide-web graphs, exhibit both small-world and scale-free behaviour. We interpret lattice triangulations as planar graphs by identifying triangulation vertices with graph nodes and one-dimensional simplices with edges. Since these triangulations are ergodic with respect to a certain Pachner flip, applying different Monte Carlo simulations enables us to calculate average properties of random triangulations, as well as canonical ensemble averages, using an energy functional that is approximately the variance of the degree distribution. All considered triangulations have clustering coefficients comparable with real-world graphs; for the canonical ensemble there are inverse temperatures with small shortest path length independent of system size. Tuning the inverse temperature to a quasi-critical value leads to an indication of scale-free behaviour for degrees k≥slant 5. Using triangulations as a random graph model can improve the understanding of real-world networks, especially if the actual distance of the embedded nodes becomes important.

DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.

PubMed

Olayan, Rawan S; Ashoor, Haitham; Bajic, Vladimir B

2018-04-01

Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model using different graph-based features extracted from the DTI heterogeneous graph. Using 5-repeats of 10-fold cross-validation, three testing setups, and the weighted average of area under the precision-recall curve (AUPR) scores, we show that DDR significantly reduces the AUPR score error relative to the next best start-of-the-art method for predicting DTIs by 34% when the drugs are new, by 23% when targets are new and by 34% when the drugs and the targets are known but not all DTIs between them are not known. Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs. The data and code are provided at https://bitbucket.org/RSO24/ddr/. vladimir.bajic@kaust.edu.sa. Supplementary data are available at Bioinformatics online.

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

Multiple graph regularized protein domain ranking.

PubMed

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2012-11-19

Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.

Multiple graph regularized protein domain ranking

PubMed Central

2012-01-01

Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. Results To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. Conclusion The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. PMID:23157331

A Graph Summarization Algorithm Based on RFID Logistics

NASA Astrophysics Data System (ADS)

Sun, Yan; Hu, Kongfa; Lu, Zhipeng; Zhao, Li; Chen, Ling

Radio Frequency Identification (RFID) applications are set to play an essential role in object tracking and supply chain management systems. The volume of data generated by a typical RFID application will be enormous as each item will generate a complete history of all the individual locations that it occupied at every point in time. The movement trails of such RFID data form gigantic commodity flowgraph representing the locations and durations of the path stages traversed by each item. In this paper, we use graph to construct a warehouse of RFID commodity flows, and introduce a database-style operation to summarize graphs, which produces a summary graph by grouping nodes based on user-selected node attributes, further allows users to control the hierarchy of summaries. It can cut down the size of graphs, and provide convenience for users to study just on the shrunk graph which they interested. Through extensive experiments, we demonstrate the effectiveness and efficiency of the proposed method.

Graph drawing using tabu search coupled with path relinking.

PubMed

Dib, Fadi K; Rodgers, Peter

2018-01-01

Graph drawing, or the automatic layout of graphs, is a challenging problem. There are several search based methods for graph drawing which are based on optimizing an objective function which is formed from a weighted sum of multiple criteria. In this paper, we propose a new neighbourhood search method which uses a tabu search coupled with path relinking to optimize such objective functions for general graph layouts with undirected straight lines. To our knowledge, before our work, neither of these methods have been previously used in general multi-criteria graph drawing. Tabu search uses a memory list to speed up searching by avoiding previously tested solutions, while the path relinking method generates new solutions by exploring paths that connect high quality solutions. We use path relinking periodically within the tabu search procedure to speed up the identification of good solutions. We have evaluated our new method against the commonly used neighbourhood search optimization techniques: hill climbing and simulated annealing. Our evaluation examines the quality of the graph layout (objective function's value) and the speed of layout in terms of the number of evaluated solutions required to draw a graph. We also examine the relative scalability of each method. Our experimental results were applied to both random graphs and a real-world dataset. We show that our method outperforms both hill climbing and simulated annealing by producing a better layout in a lower number of evaluated solutions. In addition, we demonstrate that our method has greater scalability as it can layout larger graphs than the state-of-the-art neighbourhood search methods. Finally, we show that similar results can be produced in a real world setting by testing our method against a standard public graph dataset.

Graph drawing using tabu search coupled with path relinking

PubMed Central

Rodgers, Peter

2018-01-01

Graph drawing, or the automatic layout of graphs, is a challenging problem. There are several search based methods for graph drawing which are based on optimizing an objective function which is formed from a weighted sum of multiple criteria. In this paper, we propose a new neighbourhood search method which uses a tabu search coupled with path relinking to optimize such objective functions for general graph layouts with undirected straight lines. To our knowledge, before our work, neither of these methods have been previously used in general multi-criteria graph drawing. Tabu search uses a memory list to speed up searching by avoiding previously tested solutions, while the path relinking method generates new solutions by exploring paths that connect high quality solutions. We use path relinking periodically within the tabu search procedure to speed up the identification of good solutions. We have evaluated our new method against the commonly used neighbourhood search optimization techniques: hill climbing and simulated annealing. Our evaluation examines the quality of the graph layout (objective function’s value) and the speed of layout in terms of the number of evaluated solutions required to draw a graph. We also examine the relative scalability of each method. Our experimental results were applied to both random graphs and a real-world dataset. We show that our method outperforms both hill climbing and simulated annealing by producing a better layout in a lower number of evaluated solutions. In addition, we demonstrate that our method has greater scalability as it can layout larger graphs than the state-of-the-art neighbourhood search methods. Finally, we show that similar results can be produced in a real world setting by testing our method against a standard public graph dataset. PMID:29746576

Analysis of quality raw data of second generation sequencers with Quality Assessment Software.

PubMed

Ramos, Rommel Tj; Carneiro, Adriana R; Baumbach, Jan; Azevedo, Vasco; Schneider, Maria Pc; Silva, Artur

2011-04-18

Second generation technologies have advantages over Sanger; however, they have resulted in new challenges for the genome construction process, especially because of the small size of the reads, despite the high degree of coverage. Independent of the program chosen for the construction process, DNA sequences are superimposed, based on identity, to extend the reads, generating contigs; mismatches indicate a lack of homology and are not included. This process improves our confidence in the sequences that are generated. We developed Quality Assessment Software, with which one can review graphs showing the distribution of quality values from the sequencing reads. This software allow us to adopt more stringent quality standards for sequence data, based on quality-graph analysis and estimated coverage after applying the quality filter, providing acceptable sequence coverage for genome construction from short reads. Quality filtering is a fundamental step in the process of constructing genomes, as it reduces the frequency of incorrect alignments that are caused by measuring errors, which can occur during the construction process due to the size of the reads, provoking misassemblies. Application of quality filters to sequence data, using the software Quality Assessment, along with graphing analyses, provided greater precision in the definition of cutoff parameters, which increased the accuracy of genome construction.

Weights and topology: a study of the effects of graph construction on 3D image segmentation.

PubMed

Grady, Leo; Jolly, Marie-Pierre

2008-01-01

Graph-based algorithms have become increasingly popular for medical image segmentation. The fundamental process for each of these algorithms is to use the image content to generate a set of weights for the graph and then set conditions for an optimal partition of the graph with respect to these weights. To date, the heuristics used for generating the weighted graphs from image intensities have largely been ignored, while the primary focus of attention has been on the details of providing the partitioning conditions. In this paper we empirically study the effects of graph connectivity and weighting function on the quality of the segmentation results. To control for algorithm-specific effects, we employ both the Graph Cuts and Random Walker algorithms in our experiments.

Fast generation of sparse random kernel graphs

DOE PAGES

Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

2015-09-10

The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

An automatic graph-based approach for artery/vein classification in retinal images.

PubMed

Dashtbozorg, Behdad; Mendonça, Ana Maria; Campilho, Aurélio

2014-03-01

The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIRE-AVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.

A Set of Handwriting Features for Use in Automated Writer Identification.

PubMed

Miller, John J; Patterson, Robert Bradley; Gantz, Donald T; Saunders, Christopher P; Walch, Mark A; Buscaglia, JoAnn

2017-05-01

A writer's biometric identity can be characterized through the distribution of physical feature measurements ("writer's profile"); a graph-based system that facilitates the quantification of these features is described. To accomplish this quantification, handwriting is segmented into basic graphical forms ("graphemes"), which are "skeletonized" to yield the graphical topology of the handwritten segment. The graph-based matching algorithm compares the graphemes first by their graphical topology and then by their geometric features. Graphs derived from known writers can be compared against graphs extracted from unknown writings. The process is computationally intensive and relies heavily upon statistical pattern recognition algorithms. This article focuses on the quantification of these physical features and the construction of the associated pattern recognition methods for using the features to discriminate among writers. The graph-based system described in this article has been implemented in a highly accurate and approximately language-independent biometric recognition system of writers of cursive documents. © 2017 American Academy of Forensic Sciences.

Graph-Based Semantic Web Service Composition for Healthcare Data Integration.

PubMed

Arch-Int, Ngamnij; Arch-Int, Somjit; Sonsilphong, Suphachoke; Wanchai, Paweena

2017-01-01

Within the numerous and heterogeneous web services offered through different sources, automatic web services composition is the most convenient method for building complex business processes that permit invocation of multiple existing atomic services. The current solutions in functional web services composition lack autonomous queries of semantic matches within the parameters of web services, which are necessary in the composition of large-scale related services. In this paper, we propose a graph-based Semantic Web Services composition system consisting of two subsystems: management time and run time. The management-time subsystem is responsible for dependency graph preparation in which a dependency graph of related services is generated automatically according to the proposed semantic matchmaking rules. The run-time subsystem is responsible for discovering the potential web services and nonredundant web services composition of a user's query using a graph-based searching algorithm. The proposed approach was applied to healthcare data integration in different health organizations and was evaluated according to two aspects: execution time measurement and correctness measurement.