A technology mapping based on graph of excitations and outputs for finite state machines
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.
Bell inequalities for graph states
International Nuclear Information System (INIS)
Toth, G.; Hyllus, P.; Briegel, H.J.; Guehne, O.
2005-01-01
Full text: In the last years graph states have attracted an increasing interest in the field of quantum information theory. Graph states form a family of multi-qubit states which comprises many popular states such as the GHZ states and the cluster states. They also play an important role in applications. For instance, measurement based quantum computation uses graph states as resources. From a theoretical point of view, it is remarkable that graph states allow for a simple description in terms of stabilizing operators. In this contribution, we investigate the non-local properties of graph states. We derive a family of Bell inequalities which require three measurement settings for each party and are maximally violated by graph states. In turn, any graph state violates at least one of the inequalities. We show that for certain types of graph states the violation of these inequalities increases exponentially with the number of qubits. We also discuss connections to other entanglement properties such as the positively of the partial transpose or the geometric measure of entanglement. (author)
Kwon, Oh-Hyun; Crnovrsanin, Tarik; Ma, Kwan-Liu
2018-01-01
Using different methods for laying out a graph can lead to very different visual appearances, with which the viewer perceives different information. Selecting a "good" layout method is thus important for visualizing a graph. The selection can be highly subjective and dependent on the given task. A common approach to selecting a good layout is to use aesthetic criteria and visual inspection. However, fully calculating various layouts and their associated aesthetic metrics is computationally expensive. In this paper, we present a machine learning approach to large graph visualization based on computing the topological similarity of graphs using graph kernels. For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics. An important contribution of our work is the development of a new framework to design graph kernels. Our experimental study shows that our estimation calculation is considerably faster than computing the actual layouts and their aesthetic metrics. Also, our graph kernels outperform the state-of-the-art ones in both time and accuracy. In addition, we conducted a user study to demonstrate that the topological similarity computed with our graph kernel matches perceptual similarity assessed by human users.
Quantum information processing with graph states
International Nuclear Information System (INIS)
Schlingemann, Dirk-Michael
2005-04-01
Graph states are multiparticle states which are associated with graphs. Each vertex of the graph corresponds to a single system or particle. The links describe quantum correlations (entanglement) between pairs of connected particles. Graph states were initiated independently by two research groups: On the one hand, graph states were introduced by Briegel and Raussendorf as a resource for a new model of one-way quantum computing, where algorithms are implemented by a sequence of measurements at single particles. On the other hand, graph states were developed by the author of this thesis and ReinhardWerner in Braunschweig, as a tool to build quantum error correcting codes, called graph codes. The connection between the two approaches was fully realized in close cooperation of both research groups. This habilitation thesis provides a survey of the theory of graph codes, focussing mainly, but not exclusively on the author's own research work. We present the theoretical and mathematical background for the analysis of graph codes. The concept of one-way quantum computing for general graph states is discussed. We explicitly show how to realize the encoding and decoding device of a graph code on a one-way quantum computer. This kind of implementation is to be seen as a mathematical description of a quantum memory device. In addition to that, we investigate interaction processes, which enable the creation of graph states on very large systems. Particular graph states can be created, for instance, by an Ising type interaction between next neighbor particles which sits at the points of an infinitely extended cubic lattice. Based on the theory of quantum cellular automata, we give a constructive characterization of general interactions which create a translationally invariant graph state. (orig.)
Optical generation of matter qubit graph states
International Nuclear Information System (INIS)
Benjamin, S C; Eisert, J; Stace, T M
2005-01-01
We present a scheme for rapidly entangling matter qubits in order to create graph states for one-way quantum computing. The qubits can be simple three-level systems in separate cavities. Coupling involves only local fields and a static (unswitched) linear optics network. Fusion of graph-state sections occurs with, in principle, zero probability of damaging the nascent graph state. We avoid the finite thresholds of other schemes by operating on two entangled pairs, so that each generates exactly one photon. We do not require the relatively slow single qubit local flips to be applied during the growth phase: growth of the graph state can then become a purely optical process. The scheme naturally generates graph states with vertices of high degree and so is easily able to construct minimal graph states, with consequent resource savings. The most efficient approach will be to create new graph-state edges even as qubits elsewhere are measured, in a 'just in time' approach. An error analysis indicates that the scheme is relatively robust against imperfections in the apparatus
Determining X-chains in graph states
International Nuclear Information System (INIS)
Wu, Jun-Yi; Kampermann, Hermann; Bruß, Dagmar
2016-01-01
The representation of graph states in the X-basis as well as the calculation of graph state overlaps can efficiently be performed by using the concept of X-chains (Wu et al 2015 Phys. Rev. A 92 012322). We present a necessary and sufficient criterion for X-chains and show that they can efficiently be determined by the Bareiss algorithm. An analytical approach for searching X-chain groups of a graph state is proposed. Furthermore we generalize the concept of X-chains to so-called Euler chains, whose induced subgraphs are Eulerian. This approach helps to determine if a given vertex set is an X-chain and we show how Euler chains can be used in the construction of multipartite Bell inequalities for graph states. (paper)
A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.
Directory of Open Access Journals (Sweden)
Cuihong Wen
Full Text Available Optical Music Recognition (OMR has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM. The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM, which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs and Neural Networks (NNs.
A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.
Wen, Cuihong; Zhang, Jing; Rebelo, Ana; Cheng, Fanyong
2016-01-01
Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs).
Machine learning topological states
Deng, Dong-Ling; Li, Xiaopeng; Das Sarma, S.
2017-11-01
Artificial neural networks and machine learning have now reached a new era after several decades of improvement where applications are to explode in many fields of science, industry, and technology. Here, we use artificial neural networks to study an intriguing phenomenon in quantum physics—the topological phases of matter. We find that certain topological states, either symmetry-protected or with intrinsic topological order, can be represented with classical artificial neural networks. This is demonstrated by using three concrete spin systems, the one-dimensional (1D) symmetry-protected topological cluster state and the 2D and 3D toric code states with intrinsic topological orders. For all three cases, we show rigorously that the topological ground states can be represented by short-range neural networks in an exact and efficient fashion—the required number of hidden neurons is as small as the number of physical spins and the number of parameters scales only linearly with the system size. For the 2D toric-code model, we find that the proposed short-range neural networks can describe the excited states with Abelian anyons and their nontrivial mutual statistics as well. In addition, by using reinforcement learning we show that neural networks are capable of finding the topological ground states of nonintegrable Hamiltonians with strong interactions and studying their topological phase transitions. Our results demonstrate explicitly the exceptional power of neural networks in describing topological quantum states, and at the same time provide valuable guidance to machine learning of topological phases in generic lattice models.
Deterministic dense coding and faithful teleportation with multipartite graph states
International Nuclear Information System (INIS)
Huang, C.-Y.; Yu, I-C.; Lin, F.-L.; Hsu, L.-Y.
2009-01-01
We propose schemes to perform the deterministic dense coding and faithful teleportation with multipartite graph states. We also find the sufficient and necessary condition of a viable graph state for the proposed schemes. That is, for the associated graph, the reduced adjacency matrix of the Tanner-type subgraph between senders and receivers should be invertible.
Adiabatic graph-state quantum computation
International Nuclear Information System (INIS)
Antonio, B; Anders, J; Markham, D
2014-01-01
Measurement-based quantum computation (MBQC) and holonomic quantum computation (HQC) are two very different computational methods. The computation in MBQC is driven by adaptive measurements executed in a particular order on a large entangled state. In contrast in HQC the system starts in the ground subspace of a Hamiltonian which is slowly changed such that a transformation occurs within the subspace. Following the approach of Bacon and Flammia, we show that any MBQC on a graph state with generalized flow (gflow) can be converted into an adiabatically driven holonomic computation, which we call adiabatic graph-state quantum computation (AGQC). We then investigate how properties of AGQC relate to the properties of MBQC, such as computational depth. We identify a trade-off that can be made between the number of adiabatic steps in AGQC and the norm of H-dot as well as the degree of H, in analogy to the trade-off between the number of measurements and classical post-processing seen in MBQC. Finally the effects of performing AGQC with orderings that differ from standard MBQC are investigated. (paper)
Autocoding State Machine in Erlang
DEFF Research Database (Denmark)
Guo, Yu; Hoffman, Torben; Gunder, Nicholas
2008-01-01
This paper presents an autocoding tool suit, which supports development of state machine in a model-driven fashion, where models are central to all phases of the development process. The tool suit, which is built on the Eclipse platform, provides facilities for the graphical specification...... of a state machine model. Once the state machine is specified, it is used as input to a code generation engine that generates source code in Erlang....
Automatic Assignment of Methyl-NMR Spectra of Supramolecular Machines Using Graph Theory.
Pritišanac, Iva; Degiacomi, Matteo T; Alderson, T Reid; Carneiro, Marta G; Ab, Eiso; Siegal, Gregg; Baldwin, Andrew J
2017-07-19
Methyl groups are powerful probes for the analysis of structure, dynamics and function of supramolecular assemblies, using both solution- and solid-state NMR. Widespread application of the methodology has been limited due to the challenges associated with assigning spectral resonances to specific locations within a biomolecule. Here, we present Methyl Assignment by Graph Matching (MAGMA), for the automatic assignment of methyl resonances. A graph matching protocol examines all possibilities for each resonance in order to determine an exact assignment that includes a complete description of any ambiguity. MAGMA gives 100% accuracy in confident assignments when tested against both synthetic data, and 9 cross-validated examples using both solution- and solid-state NMR data. We show that this remarkable accuracy enables a user to distinguish between alternative protein structures. In a drug discovery application on HSP90, we show the method can rapidly and efficiently distinguish between possible ligand binding modes. By providing an exact and robust solution to methyl resonance assignment, MAGMA can facilitate significantly accelerated studies of supramolecular machines using methyl-based NMR spectroscopy.
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.
Two-colorable graph states with maximal Schmidt measure
International Nuclear Information System (INIS)
Severini, Simone
2006-01-01
The Schmidt measure was introduced by Eisert and Briegel for quantifying the degree of entanglement of multipartite quantum systems [J. Eisert, H.-J. Briegel, Phys. Rev. A 64 (2001) 22306]. For two-colorable graph states, the Schmidt measure is related to the spectrum of the associated graph. We observe that almost all two-colorable graph states have maximal Schmidt measure and we construct specific examples. By making appeal to a result of Ehrenfeucht et al. [A. Ehrenfeucht, T. Harju, G. Rozenberg, Discrete Math. 278 (2004) 45], we point out that the graph operations called local complementation and switching form a transitive group acting on the set of all graph states of a given dimension
Greenberger-Horne-Zeilinger paradoxes from qudit graph states.
Tang, Weidong; Yu, Sixia; Oh, C H
2013-03-08
One fascinating way of revealing quantum nonlocality is the all-versus-nothing test due to Greenberger, Horne, and Zeilinger (GHZ) known as the GHZ paradox. So far genuine multipartite and multilevel GHZ paradoxes are known to exist only in systems containing an odd number of particles. Here we shall construct GHZ paradoxes for an arbitrary number (greater than 3) of particles with the help of qudit graph states on a special kind of graphs, called GHZ graphs. Furthermore, based on the GHZ paradox arising from a GHZ graph, we derive a Bell inequality with two d-outcome observables for each observer, whose maximal violation attained by the corresponding graph state, and a Kochen-Specker inequality testing the quantum contextuality in a state-independent fashion.
Efficient growth of complex graph states via imperfect path erasure
International Nuclear Information System (INIS)
Campbell, Earl T; Fitzsimons, Joseph; Benjamin, Simon C; Kok, Pieter
2007-01-01
Given a suitably large and well connected (complex) graph state, any quantum algorithm can be implemented purely through local measurements on the individual qubits. Measurements can also be used to create the graph state: path erasure techniques allow one to entangle multiple qubits by determining only global properties of the qubits. Here, this powerful approach is extended by demonstrating that even imperfect path erasure can produce the required graph states with high efficiency. By characterizing the degree of error in each path erasure attempt, one can subsume the resulting imperfect entanglement into an extended graph state formalism. The subsequent growth of the improper graph state can be guided, through a series of strategic decisions, in such a way as to bound the growth of the error and eventually yield a high-fidelity graph state. As an implementation of these techniques, we develop an analytic model for atom (or atom-like) qubits in mismatched cavities, under the double-heralding entanglement procedure of Barrett and Kok (2005 Phys. Rev. A 71 060310). Compared to straightforward post-selection techniques our protocol offers a dramatic improvement in growing complex high-fidelity graph states
Directory of Open Access Journals (Sweden)
Matthew D Sacchet
2015-02-01
Full Text Available 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 co-morbidities.
Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory.
Khazaee, Ali; Ebrahimzadeh, Ata; Babajani-Feremi, Abbas
2015-11-01
Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation. In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject's graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD. Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%. Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD. Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease. Copyright © 2015 International Federation of Clinical Neurophysiology. All rights reserved.
Two-setting Bell inequalities for graph states
International Nuclear Information System (INIS)
Toth, Geza; Guehne, Otfried; Briegel, Hans J.
2006-01-01
We present Bell inequalities for graph states with a high violation of local realism. In particular, we show that there is a basic Bell inequality for every nontrivial graph state which is violated by the state at least by a factor of 2. This inequality needs the measurement of, at most, two operators for each qubit and involves only some of the qubits. We also show that for some families of graph states composite Bell inequalities can be constructed such that the violation of local realism increases exponentially with the number of qubits. We prove that some of our inequalities are facets of the convex polytope containing the many-body correlations consistent with local hidden variable models. Our Bell inequalities are built from stabilizing operators of graph states
Random graph states, maximal flow and Fuss-Catalan distributions
International Nuclear Information System (INIS)
Collins, BenoIt; Nechita, Ion; Zyczkowski, Karol
2010-01-01
For any graph consisting of k vertices and m edges we construct an ensemble of random pure quantum states which describe a system composed of 2m subsystems. Each edge of the graph represents a bipartite, maximally entangled state. Each vertex represents a random unitary matrix generated according to the Haar measure, which describes the coupling between subsystems. Dividing all subsystems into two parts, one may study entanglement with respect to this partition. A general technique to derive an expression for the average entanglement entropy of random pure states associated with a given graph is presented. Our technique relies on Weingarten calculus and flow problems. We analyze the statistical properties of spectra of such random density matrices and show for which cases they are described by the free Poissonian (Marchenko-Pastur) distribution. We derive a discrete family of generalized, Fuss-Catalan distributions and explicitly construct graphs which lead to ensembles of random states characterized by these novel distributions of eigenvalues.
Network-based Arbitrated Quantum Signature Scheme with Graph State
Ma, Hongling; Li, Fei; Mao, Ningyi; Wang, Yijun; Guo, Ying
2017-08-01
Implementing an arbitrated quantum signature(QAS) through complex networks is an interesting cryptography technology in the literature. In this paper, we propose an arbitrated quantum signature for the multi-user-involved networks, whose topological structures are established by the encoded graph state. The determinative transmission of the shared keys, is enabled by the appropriate stabilizers performed on the graph state. The implementation of this scheme depends on the deterministic distribution of the multi-user-shared graph state on which the encoded message can be processed in signing and verifying phases. There are four parties involved, the signatory Alice, the verifier Bob, the arbitrator Trent and Dealer who assists the legal participants in the signature generation and verification. The security is guaranteed by the entanglement of the encoded graph state which is cooperatively prepared by legal participants in complex quantum networks.
Modeling, Control and Analyze of Multi-Machine Drive Systems using Bond Graph Technique
Directory of Open Access Journals (Sweden)
J. Belhadj
2006-03-01
Full Text Available In this paper, a system viewpoint method has been investigated to study and analyze complex systems using Bond Graph technique. These systems are multimachine multi-inverter based on Induction Machine (IM, well used in industries like rolling mills, textile, and railway traction. These systems are multi-domains, multi-scales time and present very strong internal and external couplings, with non-linearity characterized by a high model order. The classical study with analytic model is difficult to manipulate and it is limited to some performances. In this study, a “systemic approach” is presented to design these kinds of systems, using an energetic representation based on Bond Graph formalism. Three types of multimachine are studied with their control strategies. The modeling is carried out by Bond Graph and results are discussed to show the performances of this methodology
Mokhtari, Fatemeh; Bakhtiari, Shahab K.; Hossein-Zadeh, Gholam Ali; Soltanian-Zadeh, Hamid
2012-02-01
Decoding techniques have opened new windows to explore the brain function and information encoding in brain activity. In the current study, we design a recursive support vector machine which is enriched by a subtree graph kernel. We apply the classifier to discriminate between attentional cueing task and resting state from a block design fMRI dataset. The classifier is trained using weighted fMRI graphs constructed from activated regions during the two mentioned states. The proposed method leads to classification accuracy of 1. It is also able to elicit discriminative regions and connectivities between the two states using a backward edge elimination algorithm. This algorithm shows the importance of regions including cerebellum, insula, left middle superior frontal gyrus, post cingulate cortex, and connectivities between them to enhance the correct classification rate.
Distributed Graph-Based State Space Generation
Blom, Stefan; Kant, Gijs; Rensink, Arend; De Lara, J.; Varro, D.
LTSMIN provides a framework in which state space generation can be distributed easily over many cores on a single compute node, as well as over multiple compute nodes. The tool works on the basis of a vector representation of the states; the individual cores are assigned the task of computing all
Scaling up graph-based semisupervised learning via prototype vector machines.
Zhang, Kai; Lan, Liang; Kwok, James T; Vucetic, Slobodan; Parvin, Bahram
2015-03-01
When the amount of labeled data are limited, semisupervised 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.
Efficient Sampling of the Structure of Crypto Generators' State Transition Graphs
Keller, Jörg
Cryptographic generators, e.g. stream cipher generators like the A5/1 used in GSM networks or pseudo-random number generators, are widely used in cryptographic network protocols. Basically, they are finite state machines with deterministic transition functions. Their state transition graphs typically cannot be analyzed analytically, nor can they be explored completely because of their size which typically is at least n = 264. Yet, their structure, i.e. number and sizes of weakly connected components, is of interest because a structure deviating significantly from expected values for random graphs may form a distinguishing attack that indicates a weakness or backdoor. By sampling, one randomly chooses k nodes, derives their distribution onto connected components by graph exploration, and extrapolates these results to the complete graph. In known algorithms, the computational cost to determine the component for one randomly chosen node is up to O(√n), which severely restricts the sample size k. We present an algorithm where the computational cost to find the connected component for one randomly chosen node is O(1), so that a much larger sample size k can be analyzed in a given time. We report on the performance of a prototype implementation, and about preliminary analysis for several generators.
Spectral stability of shifted states on star graphs
Kairzhan, Adilbek; Pelinovsky, Dmitry E.
2018-03-01
We consider the nonlinear Schrödinger (NLS) equation with the subcritical power nonlinearity on a star graph consisting of N edges and a single vertex under generalized Kirchhoff boundary conditions. The stationary NLS equation may admit a family of solitary waves parameterized by a translational parameter, which we call the shifted states. The two main examples include (i) the star graph with even N under the classical Kirchhoff boundary conditions and (ii) the star graph with one incoming edge and N - 1 outgoing edges under a single constraint on coefficients of the generalized Kirchhoff boundary conditions. We obtain the general counting results on the Morse index of the shifted states and apply them to the two examples. In the case of (i), we prove that the shifted states with even N ≥slant 4 are saddle points of the action functional which are spectrally unstable under the NLS flow. In the case of (ii), we prove that the shifted states with the monotone profiles in the N - 1 edges are spectrally stable, whereas the shifted states with non-monotone profiles in the N - 1 edges are spectrally unstable, the two families intersect at the half-soliton states which are spectrally stable but nonlinearly unstable under the NLS flow. Since the NLS equation on a star graph with shifted states can be reduced to the homogeneous NLS equation on an infinite line, the spectral instability of shifted states is due to the perturbations breaking this reduction. We give a simple argument suggesting that the spectrally stable shifted states in the case of (ii) are nonlinearly unstable under the NLS flow due to the perturbations breaking the reduction to the homogeneous NLS equation.
Learning Extended Finite State Machines
Cassel, Sofia; Howar, Falk; Jonsson, Bengt; Steffen, Bernhard
2014-01-01
We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.
Refining Nodes and Edges of State Machines
DEFF Research Database (Denmark)
Hallerstede, Stefan; Snook, Colin
2011-01-01
State machines are hierarchical automata that are widely used to structure complex behavioural specifications. We develop two notions of refinement of state machines, node refinement and edge refinement. We compare the two notions by means of examples and argue that, by adopting simple conventions...... refinement theory and UML-B state machine refinement influences the style of node refinement. Hence we propose a method with direct proof of state machine refinement avoiding the detour via Event-B that is needed by UML-B....
Graph state generation with noisy mirror-inverting spin chains
International Nuclear Information System (INIS)
Clark, Stephen R; Klein, Alexander; Bruderer, Martin; Jaksch, Dieter
2007-01-01
We investigate the influence of noise on a graph state generation scheme which exploits a mirror inverting spin chain. Within this scheme the spin chain is used repeatedly as an entanglement bus (EB) to create multi-partite entanglement. The noise model we consider comprises of each spin of this EB being exposed to independent local noise which degrades the capabilities of the EB. Here we concentrate on quantifying its performance as a single-qubit channel and as a mediator of a two-qubit entangling gate, since these are basic operations necessary for graph state generation using the EB. In particular, for the single-qubit case we numerically calculate the average channel fidelity and whether the channel becomes entanglement breaking, i.e. expunges any entanglement the transferred qubit may have with other external qubits. We find that neither local decay nor dephasing noise cause entanglement breaking. This is in contrast to local thermal and depolarizing noise where we determine a critical length and critical noise coupling, respectively, at which entanglement breaking occurs. The critical noise coupling for local depolarizing noise is found to exhibit a power-law dependence on the chain length. For two-qubits we similarly compute the average gate fidelity and whether the ability for this gate to create entanglement is maintained. The concatenation of these noisy gates for the construction of a five-qubit linear cluster state and a Greenberger-Horne-Zeilinger state indicates that the level of noise that can be tolerated for graph state generation is tightly constrained
Collaborative Systems – Finite State Machines
Directory of Open Access Journals (Sweden)
Ion IVAN
2011-01-01
Full Text Available In this paper the finite state machines are defined and formalized. There are presented the collaborative banking systems and their correspondence is done with finite state machines. It highlights the role of finite state machines in the complexity analysis and performs operations on very large virtual databases as finite state machines. It builds the state diagram and presents the commands and documents transition between the collaborative systems states. The paper analyzes the data sets from Collaborative Multicash Servicedesk application and performs a combined analysis in order to determine certain statistics. Indicators are obtained, such as the number of requests by category and the load degree of an agent in the collaborative system.
Simulation of an Asynchronous Machine by using a Pseudo Bond Graph
Romero, Gregorio; Felez, Jesus; Maroto, Joaquin; Martinez, M. Luisa
2008-11-01
For engineers, computer simulation, is a basic tool since it enables them to understand how systems work without actually needing to see them. They can learn how they work in different circumstances and optimize their design with considerably less cost in terms of time and money than if they had to carry out tests on a physical system. However, if computer simulation is to be reliable it is essential for the simulation model to be validated. There is a wide range of commercial brands on the market offering products for electrical domain simulation (SPICE, LabVIEW PSCAD,Dymola, Simulink, Simplorer,...). These are powerful tools, but require the engineer to have a perfect knowledge of the electrical field. This paper shows an alternative methodology to can simulate an asynchronous machine using the multidomain Bond Graph technique and apply it in any program that permit the simulation of models based in this technique; no extraordinary knowledge of this technique and electric field are required to understand the process .
Morisi, Rita; Manners, David Neil; Gnecco, Giorgio; Lanconelli, Nico; Testa, Claudia; Evangelisti, Stefania; Talozzi, Lia; Gramegna, Laura Ludovica; Bianchini, Claudio; Calandra-Buonaura, Giovanna; Sambati, Luisa; Giannini, Giulia; Cortelli, Pietro; Tonon, Caterina; Lodi, Raffaele
2018-02-01
In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy. When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required. The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.
An Infinite Family of Circulant Graphs with Perfect State Transfer in Discrete Quantum Walks
Zhan, Hanmeng
2017-01-01
We study perfect state transfer in a discrete quantum walk. In particular, we show that there are infinitely many $4$-regular circulant graphs that admit perfect state transfer between antipodal vertices. To the best of our knowledge, previously there was no infinite family of $k$-regular graphs with perfect state transfer, for any $k\\ge 3$.
Bell-type inequalities embedded in the subgraph of graph states
International Nuclear Information System (INIS)
Hsu, L.-Y.
2006-01-01
We investigate the Bell-type inequalities of graph states. In this paper, Bell-type inequalities can be derived based on two kinds of the associated subgraphs of the graph states. First, the star subgraphs lead to the maximal violation of the modified Seevinck-Svetlichny inequalities. Second, cycle subgraphs lead to maximal violation of Bell-type inequalities. As a result, once the associated graph of a graph state is given, the corresponding Bell operators can be immediatedly determined using stabilizing generators. In the above Bell-type inequalities, two measurement settings for each party are required
SwingStates: adding state machines to the swing toolkit
Appert , Caroline; Beaudouin-Lafon , Michel
2006-01-01
International audience; This article describes SwingStates, a library that adds state machines to the Java Swing user interface toolkit. Unlike traditional approaches, which use callbacks or listeners to define interaction, state machines provide a powerful control structure and localize all of the interaction code in one place. SwingStates takes advantage of Java's inner classes, providing programmers with a natural syntax and making it easier to follow and debug the resulting code. SwingSta...
Perfect state transfer in unitary Cayley graphs over local rings
Directory of Open Access Journals (Sweden)
Yotsanan Meemark
2014-12-01
Full Text Available In this work, using eigenvalues and eigenvectors of unitary Cayley graphs over finite local rings and elementary linear algebra, we characterize which local rings allowing PST occurring in its unitary Cayley graph. Moreover, we have some developments when $R$ is a product of local rings.
Hierarchical State Machines as Modular Horn Clauses
Directory of Open Access Journals (Sweden)
Pierre-Loïc Garoche
2016-07-01
Full Text Available In model based development, embedded systems are modeled using a mix of dataflow formalism, that capture the flow of computation, and hierarchical state machines, that capture the modal behavior of the system. For safety analysis, existing approaches rely on a compilation scheme that transform the original model (dataflow and state machines into a pure dataflow formalism. Such compilation often result in loss of important structural information that capture the modal behaviour of the system. In previous work we have developed a compilation technique from a dataflow formalism into modular Horn clauses. In this paper, we present a novel technique that faithfully compile hierarchical state machines into modular Horn clauses. Our compilation technique preserves the structural and modal behavior of the system, making the safety analysis of such models more tractable.
Directory of Open Access Journals (Sweden)
Speranza Sannino
2017-10-01
Full Text Available Visibility algorithms are a family of methods that map time series into graphs, such that the tools of graph theory and network science can be used for the characterization of time series. This approach has proved a convenient tool, and visibility graphs have found applications across several disciplines. Recently, an approach has been proposed to extend this framework to multivariate time series, allowing a novel way to describe collective dynamics. Here we test their application to fMRI time series, following two main motivations, namely that (a this approach allows vs to simultaneously capture and process relevant aspects of both local and global dynamics in an easy and intuitive way, and (b this provides a suggestive bridge between time series and network theory that nicely fits the consolidating field of network neuroscience. Our application to a large open dataset reveals differences in the similarities of temporal networks (and thus in correlated dynamics across resting-state networks, and gives indications that some differences in brain activity connected to psychiatric disorders could be picked up by this approach. Here we present the first application of multivariate visibility graphs to fMRI data. Visibility graphs are a way to represent a time series as a temporal network, evidencing specific aspects of its dynamics, such as extreme events. Multivariate time series, as those encountered in neuroscience, and in fMRI in particular, can be seen as a multiplex network, in which each layer represents a time series (a region of interest in the brain in our case. Here we report the method, we describe some relevant aspects of its application to BOLD time series, and we discuss the analogies and differences with existing methods. Finally, we present an application to a high-quality, publicly available dataset, containing healthy subjects and psychotic patients, and we discuss our findings. All the code to reproduce the analyses and the
OpenMP Parallelization and Optimization of Graph-based Machine Learning Algorithms
2016-05-01
Understanding Application Data Movement Characteristics using Intel VTune Amplifier and Software Development Emulator tools, Intel Xeon Phi User Group...sured by a summation of the weights along the graph cut) for this problem. This is equivalent to assigning a scalar or vector value ui to each i th data...graph Laplacian [9]. By projecting all vectors onto this sub-eigenspace, the iteration step reduces to a simple coefficient update. 2.2 Semi-supervised
Hively, Lee M.
2014-09-16
Data collected from devices and human condition may be used to forewarn of critical events such as machine/structural failure or events from brain/heart wave data stroke. By monitoring the data, and determining what values are indicative of a failure forewarning, one can provide adequate notice of the impending failure in order to take preventive measures. This disclosure teaches a computer-based method to convert dynamical numeric data representing physical objects (unstructured data) into discrete-phase-space states, and hence into a graph (structured data) for extraction of condition change.
PLA realizations for VLSI state machines
Gopalakrishnan, S.; Whitaker, S.; Maki, G.; Liu, K.
1990-01-01
A major problem associated with state assignment procedures for VLSI controllers is obtaining an assignment that produces minimal or near minimal logic. The key item in Programmable Logic Array (PLA) area minimization is the number of unique product terms required by the design equations. This paper presents a state assignment algorithm for minimizing the number of product terms required to implement a finite state machine using a PLA. Partition algebra with predecessor state information is used to derive a near optimal state assignment. A maximum bound on the number of product terms required can be obtained by inspecting the predecessor state information. The state assignment algorithm presented is much simpler than existing procedures and leads to the same number of product terms or less. An area-efficient PLA structure implemented in a 1.0 micron CMOS process is presented along with a summary of the performance for a controller implemented using this design procedure.
Quantum Experiments and Graphs: Multiparty States as Coherent Superpositions of Perfect Matchings
Krenn, Mario; Gu, Xuemei; Zeilinger, Anton
2017-12-01
We show a surprising link between experimental setups to realize high-dimensional multipartite quantum states and graph theory. In these setups, the paths of photons are identified such that the photon-source information is never created. We find that each of these setups corresponds to an undirected graph, and every undirected graph corresponds to an experimental setup. Every term in the emerging quantum superposition corresponds to a perfect matching in the graph. Calculating the final quantum state is in the #P-complete complexity class, thus it cannot be done efficiently. To strengthen the link further, theorems from graph theory—such as Hall's marriage problem—are rephrased in the language of pair creation in quantum experiments. We show explicitly how this link allows one to answer questions about quantum experiments (such as which classes of entangled states can be created) with graph theoretical methods, and how to potentially simulate properties of graphs and networks with quantum experiments (such as critical exponents and phase transitions).
Quantum Experiments and Graphs: Multiparty States as Coherent Superpositions of Perfect Matchings.
Krenn, Mario; Gu, Xuemei; Zeilinger, Anton
2017-12-15
We show a surprising link between experimental setups to realize high-dimensional multipartite quantum states and graph theory. In these setups, the paths of photons are identified such that the photon-source information is never created. We find that each of these setups corresponds to an undirected graph, and every undirected graph corresponds to an experimental setup. Every term in the emerging quantum superposition corresponds to a perfect matching in the graph. Calculating the final quantum state is in the #P-complete complexity class, thus it cannot be done efficiently. To strengthen the link further, theorems from graph theory-such as Hall's marriage problem-are rephrased in the language of pair creation in quantum experiments. We show explicitly how this link allows one to answer questions about quantum experiments (such as which classes of entangled states can be created) with graph theoretical methods, and how to potentially simulate properties of graphs and networks with quantum experiments (such as critical exponents and phase transitions).
Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM.
Hojjati, Seyed Hani; Ebrahimzadeh, Ata; Khazaee, Ali; Babajani-Feremi, Abbas
2017-04-15
We investigated identifying patients with mild cognitive impairment (MCI) who progress to Alzheimer's disease (AD), MCI converter (MCI-C), from those with MCI who do not progress to AD, MCI non-converter (MCI-NC), based on resting-state fMRI (rs-fMRI). Graph theory and machine learning approach were utilized to predict progress of patients with MCI to AD using rs-fMRI. Eighteen MCI converts (average age 73.6 years; 11 male) and 62 age-matched MCI non-converters (average age 73.0 years, 28 male) were included in this study. We trained and tested a support vector machine (SVM) to classify MCI-C from MCI-NC using features constructed based on the local and global graph measures. A novel feature selection algorithm was developed and utilized to select an optimal subset of features. Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. Furthermore, results of our statistical analyses were used to identify the affected brain regions in AD. To the best of our knowledge, this is the first study that combines the graph measures (constructed based on rs-fMRI) with machine learning approach and accurately classify MCI-C from MCI-NC. Results of this study demonstrate potential of the proposed approach for early AD diagnosis and demonstrate capability of rs-fMRI to predict conversion from MCI to AD by identifying affected brain regions underlying this conversion. Copyright © 2017 Elsevier B.V. All rights reserved.
Schindler, Timo
2018-01-01
Advanced Persistent Threats (APTs) are a main impendence in cyber security of computer networks. In 2015, a successful breach remains undetected 146 days on average, reported by [Fi16].With our work we demonstrate a feasible and fast way to analyse real world log data to detect breaches or breach attempts. By adapting well-known kill chain mechanisms and a combine of a time series database and an abstracted graph approach, it is possible to create flexible attack profiles. Using this approach...
Entanglement of the valence-bond-solid state on an arbitrary graph
International Nuclear Information System (INIS)
Xu Ying; Korepin, Vladimir E
2008-01-01
The Affleck-Kennedy-Lieb-Tasaki (AKLT) spin interacting model can be defined on an arbitrary graph. We explain the construction of the AKLT Hamiltonian. Given certain conditions, the ground state is unique and known as the valence-bond-solid (VBS) state. It can be used in measurement-based quantum computation as a resource state instead of the cluster state. We study the VBS ground state on an arbitrary connected graph. The graph is cut into two disconnected parts: the block and the environment. We study the entanglement between these two parts and prove that many eigenvalues of the density matrix of the block are zero. We describe a subspace of eigenvectors of the density matrix corresponding to non-zero eigenvalues. The subspace is the degenerate ground states of some Hamiltonian which we call the block Hamiltonian
Local unitary versus local Clifford equivalence of stabilizer and graph states
International Nuclear Information System (INIS)
Zeng, Bei; Chung, Hyeyoun; Cross, Andrew W.; Chuang, Isaac L.
2007-01-01
The equivalence of stabilizer states under local transformations is of fundamental interest in understanding properties and uses of entanglement. Two stabilizer states are equivalent under the usual stochastic local operations and classical communication criterion if and only if they are equivalent under local unitary (LU) operations. More surprisingly, under certain conditions, two LU-equivalent stabilizer states are also equivalent under local Clifford (LC) operations, as was shown by Van den Nest et al. [Phys. Rev. A 71, 062323 (2005)]. Here, we broaden the class of stabilizer states for which LU equivalence implies LC equivalence (LU LC) to include all stabilizer states represented by graphs with cycles of length neither 3 nor 4. To compare our result with Van den Nest et al.'s, we show that any stabilizer state of distance δ=2 is beyond their criterion. We then further prove that LU LC holds for a more general class of stabilizer states of δ=2. We also explicitly construct graphs representing δ>2 stabilizer states which are beyond their criterion: we identify all 58 graphs with up to 11 vertices and construct graphs with 2 m -1 (m≥4) vertices using quantum error-correcting codes which have non-Clifford transversal gates
Dynamic thermal analysis of machines in running state
Wang, Lihui
2014-01-01
With the increasing complexity and dynamism in today’s machine design and development, more precise, robust and practical approaches and systems are needed to support machine design. Existing design methods treat the targeted machine as stationery. Analysis and simulation are mostly performed at the component level. Although there are some computer-aided engineering tools capable of motion analysis and vibration simulation etc., the machine itself is in the dry-run state. For effective machine design, understanding its thermal behaviours is crucial in achieving the desired performance in real situation. Dynamic Thermal Analysis of Machines in Running State presents a set of innovative solutions to dynamic thermal analysis of machines when they are put under actual working conditions. The objective is to better understand the thermal behaviours of a machine in real situation while at the design stage. The book has two major sections, with the first section presenting a broad-based review of the key areas of ...
Energy Minimization of Discrete Protein Titration State Models Using Graph Theory
Purvine, Emilie; Monson, Kyle; Jurrus, Elizabeth; Star, Keith; Baker, Nathan A.
2016-01-01
There are several applications in computational biophysics which require the optimization of discrete interacting states; e.g., amino acid titration states, ligand oxidation states, or discrete rotamer angles. Such optimization can be very time-consuming as it scales exponentially in the number of sites to be optimized. In this paper, we describe a new polynomial-time algorithm for optimization of discrete states in macromolecular systems. This algorithm was adapted from image processing and uses techniques from discrete mathematics and graph theory to restate the optimization problem in terms of “maximum flow-minimum cut” graph analysis. The interaction energy graph, a graph in which vertices (amino acids) and edges (interactions) are weighted with their respective energies, is transformed into a flow network in which the value of the minimum cut in the network equals the minimum free energy of the protein, and the cut itself encodes the state that achieves the minimum free energy. Because of its deterministic nature and polynomial-time performance, this algorithm has the potential to allow for the ionization state of larger proteins to be discovered. PMID:27089174
Energy Minimization of Discrete Protein Titration State Models Using Graph Theory.
Purvine, Emilie; Monson, Kyle; Jurrus, Elizabeth; Star, Keith; Baker, Nathan A
2016-08-25
There are several applications in computational biophysics that require the optimization of discrete interacting states, for example, amino acid titration states, ligand oxidation states, or discrete rotamer angles. Such optimization can be very time-consuming as it scales exponentially in the number of sites to be optimized. In this paper, we describe a new polynomial time algorithm for optimization of discrete states in macromolecular systems. This algorithm was adapted from image processing and uses techniques from discrete mathematics and graph theory to restate the optimization problem in terms of "maximum flow-minimum cut" graph analysis. The interaction energy graph, a graph in which vertices (amino acids) and edges (interactions) are weighted with their respective energies, is transformed into a flow network in which the value of the minimum cut in the network equals the minimum free energy of the protein and the cut itself encodes the state that achieves the minimum free energy. Because of its deterministic nature and polynomial time performance, this algorithm has the potential to allow for the ionization state of larger proteins to be discovered.
State space model extraction of thermohydraulic systems – Part I: A linear graph approach
International Nuclear Information System (INIS)
Uren, K.R.; Schoor, G. van
2013-01-01
Thermohydraulic simulation codes are increasingly making use of graphical design interfaces. The user can quickly and easily design a thermohydraulic system by placing symbols on the screen resembling system components. These components can then be connected to form a system representation. Such system models may then be used to obtain detailed simulations of the physical system. Usually this kind of simulation models are too complex and not ideal for control system design. Therefore, a need exists for automated techniques to extract lumped parameter models useful for control system design. The goal of this first paper, in a two part series, is to propose a method that utilises a graphical representation of a thermohydraulic system, and a lumped parameter modelling approach, to extract state space models. In this methodology each physical domain of the thermohydraulic system is represented by a linear graph. These linear graphs capture the interaction between all components within and across energy domains – hydraulic, thermal and mechanical. These linear graphs are analysed using a graph-theoretic approach to derive reduced order state space models. These models capture the dominant dynamics of the thermohydraulic system and are ideal for control system design purposes. The proposed state space model extraction method is demonstrated by considering a U-tube system. A non-linear state space model is extracted representing both the hydraulic and thermal domain dynamics of the system. The simulated state space model is compared with a Flownex ® model of the U-tube. Flownex ® is a validated systems thermal-fluid simulation software package. - Highlights: • A state space model extraction methodology based on graph-theoretic concepts. • An energy-based approach to consider multi-domain systems in a common framework. • Allow extraction of transparent (white-box) state space models automatically. • Reduced order models containing only independent state
Dipnall, J F; Pasco, J A; Berk, M; Williams, L J; Dodd, S; Jacka, F N; Meyer, D
2017-01-01
Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through "Graphing lifestyle-environs using machine-learning methods" (GLUMM). Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six "lifestyle-environ" variables were used from the National health and nutrition examination study (2009-2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders. The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, Pdepression was found, with significant interactions with those married/living with partner (P=0.001). Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors. Copyright © 2016 Elsevier Masson SAS. All rights reserved.
Directory of Open Access Journals (Sweden)
Shuihua Wang
2016-10-01
Full Text Available (Aim Sensorineural hearing loss (SNHL is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. (Materials We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany. The subjects contain 14 patients with right-sided hearing loss (RHL, 15 patients with left-sided hearing loss (LHL, and 20 healthy controls (HC. (Method We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM. (Results The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. (Conclusions This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.
Learning molecular energies using localized graph kernels
Ferré, Grégoire; Haut, Terry; Barros, Kipton
2017-03-01
Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.
Olayan, Rawan S.
2017-12-01
Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Computational determination of DTIs is a convenient strategy for systematic screening of a large number of drugs in the attempt to identify new DTIs at low cost and with reasonable accuracy. This necessitates development of accurate computational methods that can help focus on the follow-up experimental validation on a smaller number of highly likely targets for a drug. Although many methods have been proposed for computational DTI prediction, they suffer the high false positive prediction rate or they do not predict the effect that drugs exert on targets in DTIs. In this report, first, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithm-centric perspectives. The aim is to provide a comprehensive review of computational methods for identifying DTIs, which could help in constructing more reliable methods. Then, we present DDR, an efficient method to predict the existence of DTIs. DDR achieves significantly more accurate results compared to the other state-of-theart methods. As supported by independent evidences, we verified as correct 22 out of the top 25 DDR DTIs predictions. This validation proves the practical utility of DDR, suggesting that DDR can be used as an efficient method to identify 5 correct DTIs. Finally, we present DDR-FE method that predicts the effect types of a drug on its target. On different representative datasets, under various test setups, and using different performance measures, we show that DDR-FE achieves extremely good performance. Using blind test data, we verified as correct 2,300 out of 3,076 DTIs effects predicted by DDR-FE. This suggests that DDR-FE can be used as an efficient method to identify correct effects of a drug on its target.
Hadronic equation of state in the statistical bootstrap model and linear graph theory
International Nuclear Information System (INIS)
Fre, P.; Page, R.
1976-01-01
Taking a statistical mechanical point og view, the statistical bootstrap model is discussed and, from a critical analysis of the bootstrap volume comcept, it is reached a physical ipothesis, which leads immediately to the hadronic equation of state provided by the bootstrap integral equation. In this context also the connection between the statistical bootstrap and the linear graph theory approach to interacting gases is analyzed
Reverse Engineering Integrated Circuits Using Finite State Machine Analysis
Energy Technology Data Exchange (ETDEWEB)
Oler, Kiri J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Miller, Carl H. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
2016-04-12
In this paper, we present a methodology for reverse engineering integrated circuits, including a mathematical verification of a scalable algorithm used to generate minimal finite state machine representations of integrated circuits.
Solid-state resistor for pulsed power machines
Stoltzfus, Brian; Savage, Mark E.; Hutsel, Brian Thomas; Fowler, William E.; MacRunnels, Keven Alan; Justus, David; Stygar, William A.
2016-12-06
A flexible solid-state resistor comprises a string of ceramic resistors that can be used to charge the capacitors of a linear transformer driver (LTD) used in a pulsed power machine. The solid-state resistor is able to absorb the energy of a switch prefire, thereby limiting LTD cavity damage, yet has a sufficiently low RC charge time to allow the capacitor to be recharged without disrupting the operation of the pulsed power machine.
An Approach for Implementing State Machines with Online Testability
Directory of Open Access Journals (Sweden)
P. K. Lala
2010-01-01
Full Text Available During the last two decades, significant amount of research has been performed to simplify the detection of transient or soft errors in VLSI-based digital systems. This paper proposes an approach for implementing state machines that uses 2-hot code for state encoding. State machines designed using this approach allow online detection of soft errors in registers and output logic. The 2-hot code considerably reduces the number of required flip-flops and leads to relatively straightforward implementation of next state and output logic. A new way of designing output logic for online fault detection has also been presented.
The Design of Finite State Machine for Asynchronous Replication Protocol
Wang, Yanlong; Li, Zhanhuai; Lin, Wei; Hei, Minglei; Hao, Jianhua
Data replication is a key way to design a disaster tolerance system and to achieve reliability and availability. It is difficult for a replication protocol to deal with the diverse and complex environment. This means that data is less well replicated than it ought to be. To reduce data loss and to optimize replication protocols, we (1) present a finite state machine, (2) run it to manage an asynchronous replication protocol and (3) report a simple evaluation of the asynchronous replication protocol based on our state machine. It's proved that our state machine is applicable to guarantee the asynchronous replication protocol running in the proper state to the largest extent in the event of various possible events. It also can helpful to build up replication-based disaster tolerance systems to ensure the business continuity.
Removing the Restrictions Imposed on Finite State Machines ...
African Journals Online (AJOL)
This study determines an effective method of removing the fixed and finite state amount of memory that restricts finite state machines from carrying out compilation jobs that require larger amount of memory. The study is ... The conclusion reviewed the various steps followed and made projections for further reading. Keyword: ...
Graph-state preparation and quantum computation with global addressing of optical lattices
International Nuclear Information System (INIS)
Kay, Alastair; Pachos, Jiannis K.; Adams, Charles S.
2006-01-01
We present a way to manipulate ultracold atoms where four atomic levels are trapped by appropriately tuned optical lattices. When employed to perform quantum computation via global control, this unique structure dramatically reduces the number of steps involved in the control procedures, either for the standard, network, model, or for one-way quantum computation. The use of a far-blue-detuned lattice and a magnetically insensitive computational basis makes the scheme robust against decoherence. The present scheme is a promising candidate for experimental implementation of quantum computation and for graph-state preparation in one, two, or three spatial dimensions
DEFF Research Database (Denmark)
Seiller, Thomas
2016-01-01
Interaction graphs were introduced as a general, uniform, construction of dynamic models of linear logic, encompassing all Geometry of Interaction (GoI) constructions introduced so far. This series of work was inspired from Girard's hyperfinite GoI, and develops a quantitative approach that should...... be understood as a dynamic version of weighted relational models. Until now, the interaction graphs framework has been shown to deal with exponentials for the constrained system ELL (Elementary Linear Logic) while keeping its quantitative aspect. Adapting older constructions by Girard, one can clearly define...... "full" exponentials, but at the cost of these quantitative features. We show here that allowing interpretations of proofs to use continuous (yet finite in a measure-theoretic sense) sets of states, as opposed to earlier Interaction Graphs constructions were these sets of states were discrete (and finite...
State machine operation of the MICE cooling channel
International Nuclear Information System (INIS)
Hanlet, Pierrick
2014-01-01
The Muon Ionization Cooling Experiment (MICE) is a demonstration experiment to prove the feasibility of cooling a beam of muons for use in a Neutrino Factory and/or Muon Collider. The MICE cooling channel is a section of a modified Study II cooling channel which will provide a 10% reduction in beam emittance. In order to ensure a reliable measurement, MICE will measure the beam emittance before and after the cooling channel at the level of 1%, a relative measurement of 0.001. This renders MICE a precision experiment which requires strict controls and monitoring of all experimental parameters in order to control systematic errors. The MICE Controls and Monitoring system is based on EPICS and integrates with the DAQ, Data monitoring systems, and a configuration database. The cooling channel for MICE has between 12 and 18 superconductnig solenoid coils in 3 to 7 magnets, depending on the staged development of the experiment. The magnets are coaxial and in close proximity which requires coordinated operation of the magnets when ramping, responding to quench conditions, and quench recovery. To reliably manage the operation of the magnets, MICE is implementing state machines for each magnet and an over-arching state machine for the magnets integrated in the cooling channel. The state machine transitions and operating parameters are stored/restored to/from the configuration database and coupled with MICE Run Control. Proper implementation of the state machines will not only ensure safe operation of the magnets, but will help ensure reliable data quality. A description of MICE, details of the state machines, and lessons learned from use of the state machines in recent magnet training tests will be discussed.
The Influence of Preprocessing Steps on Graph Theory Measures Derived from Resting State fMRI.
Gargouri, Fatma; Kallel, Fathi; Delphine, Sebastien; Ben Hamida, Ahmed; Lehéricy, Stéphane; Valabregue, Romain
2018-01-01
Resting state functional MRI (rs-fMRI) is an imaging technique that allows the spontaneous activity of the brain to be measured. Measures of functional connectivity highly depend on the quality of the BOLD signal data processing. In this study, our aim was to study the influence of preprocessing steps and their order of application on small-world topology and their efficiency in resting state fMRI data analysis using graph theory. We applied the most standard preprocessing steps: slice-timing, realign, smoothing, filtering, and the tCompCor method. In particular, we were interested in how preprocessing can retain the small-world economic properties and how to maximize the local and global efficiency of a network while minimizing the cost. Tests that we conducted in 54 healthy subjects showed that the choice and ordering of preprocessing steps impacted the graph measures. We found that the csr (where we applied realignment, smoothing, and tCompCor as a final step) and the scr (where we applied realignment, tCompCor and smoothing as a final step) strategies had the highest mean values of global efficiency (eg) . Furthermore, we found that the fscr strategy (where we applied realignment, tCompCor, smoothing, and filtering as a final step), had the highest mean local efficiency (el) values. These results confirm that the graph theory measures of functional connectivity depend on the ordering of the processing steps, with the best results being obtained using smoothing and tCompCor as the final steps for global efficiency with additional filtering for local efficiency.
Graph-based network analysis of resting-state functional MRI
Directory of Open Access Journals (Sweden)
Jinhui Wang
2010-06-01
Full Text Available In the past decade, resting-state functional MRI (R-fMRI measures of brain activity have attracted considerable attention. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain’s spontaneous or intrinsic (i.e., task-free activity with both high spatial and temporal resolutions. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Specifically, the topological organization of brain networks has been recently studied with graph theory. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. Among these is the knowledge that the brain’s intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. These network properties have also been found to change throughout normal development, aging and in various pathological conditions. The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. We also highlight several potential research topics in the future.
Graph-based network analysis of resting-state functional MRI.
Wang, Jinhui; Zuo, Xinian; He, Yong
2010-01-01
In the past decade, resting-state functional MRI (R-fMRI) measures of brain activity have attracted considerable attention. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain's spontaneous or intrinsic (i.e., task-free) activity with both high spatial and temporal resolutions. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Specifically, the topological organization of brain networks has been recently studied with graph theory. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. Among these is the knowledge that the brain's intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. These network properties have also been found to change throughout normal development, aging, and in various pathological conditions. The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. We also highlight several potential research topics in the future.
The Influence of Preprocessing Steps on Graph Theory Measures Derived from Resting State fMRI
Directory of Open Access Journals (Sweden)
Fatma Gargouri
2018-02-01
Full Text Available Resting state functional MRI (rs-fMRI is an imaging technique that allows the spontaneous activity of the brain to be measured. Measures of functional connectivity highly depend on the quality of the BOLD signal data processing. In this study, our aim was to study the influence of preprocessing steps and their order of application on small-world topology and their efficiency in resting state fMRI data analysis using graph theory. We applied the most standard preprocessing steps: slice-timing, realign, smoothing, filtering, and the tCompCor method. In particular, we were interested in how preprocessing can retain the small-world economic properties and how to maximize the local and global efficiency of a network while minimizing the cost. Tests that we conducted in 54 healthy subjects showed that the choice and ordering of preprocessing steps impacted the graph measures. We found that the csr (where we applied realignment, smoothing, and tCompCor as a final step and the scr (where we applied realignment, tCompCor and smoothing as a final step strategies had the highest mean values of global efficiency (eg. Furthermore, we found that the fscr strategy (where we applied realignment, tCompCor, smoothing, and filtering as a final step, had the highest mean local efficiency (el values. These results confirm that the graph theory measures of functional connectivity depend on the ordering of the processing steps, with the best results being obtained using smoothing and tCompCor as the final steps for global efficiency with additional filtering for local efficiency.
The Influence of Preprocessing Steps on Graph Theory Measures Derived from Resting State fMRI
Gargouri, Fatma; Kallel, Fathi; Delphine, Sebastien; Ben Hamida, Ahmed; Lehéricy, Stéphane; Valabregue, Romain
2018-01-01
Resting state functional MRI (rs-fMRI) is an imaging technique that allows the spontaneous activity of the brain to be measured. Measures of functional connectivity highly depend on the quality of the BOLD signal data processing. In this study, our aim was to study the influence of preprocessing steps and their order of application on small-world topology and their efficiency in resting state fMRI data analysis using graph theory. We applied the most standard preprocessing steps: slice-timing, realign, smoothing, filtering, and the tCompCor method. In particular, we were interested in how preprocessing can retain the small-world economic properties and how to maximize the local and global efficiency of a network while minimizing the cost. Tests that we conducted in 54 healthy subjects showed that the choice and ordering of preprocessing steps impacted the graph measures. We found that the csr (where we applied realignment, smoothing, and tCompCor as a final step) and the scr (where we applied realignment, tCompCor and smoothing as a final step) strategies had the highest mean values of global efficiency (eg). Furthermore, we found that the fscr strategy (where we applied realignment, tCompCor, smoothing, and filtering as a final step), had the highest mean local efficiency (el) values. These results confirm that the graph theory measures of functional connectivity depend on the ordering of the processing steps, with the best results being obtained using smoothing and tCompCor as the final steps for global efficiency with additional filtering for local efficiency. PMID:29497372
Control of discrete event systems modeled as hierarchical state machines
Brave, Y.; Heymann, M.
1991-01-01
The authors examine a class of discrete event systems (DESs) modeled as asynchronous hierarchical state machines (AHSMs). For this class of DESs, they provide an efficient method for testing reachability, which is an essential step in many control synthesis procedures. This method utilizes the asynchronous nature and hierarchical structure of AHSMs, thereby illustrating the advantage of the AHSM representation as compared with its equivalent (flat) state machine representation. An application of the method is presented where an online minimally restrictive solution is proposed for the problem of maintaining a controlled AHSM within prescribed legal bounds.
Comparison between state graphs and fault trees for sequential and repairable systems
International Nuclear Information System (INIS)
Soussan, D.; Saignes, P.
1996-01-01
In French PSA (Probabilistic Safety Assessment) 1300 for the 1300 Mwe PWR plants carried out by EDF, sequential and reparable systems are modeled with state graphs. This method is particularly convenient for modeling dynamic systems with long-term missions but induces a bad traceability and understandability of models. In the objective of providing elements for rewriting PSA 1300 with only boolean models, EDF has asked CEA to participate to a methodological study. The aim is to carry out a feasibility study of transposition of state graphs models into fault trees on Component Cooling System and Essential Service Water System (CCS/ESWS) and to draw a methodological guide for transposition. The study realized on CCS/ESWS involves two main axes: quantification of cold source loss (as an accident sequence initiating event, called H1); quantification of the CCS/ESWS missions in accident sequences. The subject of this article is to show that this transformation is applicable with minimum distortions of the results and to determine the hypotheses, the conditions and the limits of application of this conversion. (authors). 2 refs
Resting-state theta band connectivity and graph analysis in generalized social anxiety disorder.
Xing, Mengqi; Tadayonnejad, Reza; MacNamara, Annmarie; Ajilore, Olusola; DiGangi, Julia; Phan, K Luan; Leow, Alex; Klumpp, Heide
2017-01-01
Functional magnetic resonance imaging (fMRI) resting-state studies show generalized social anxiety disorder (gSAD) is associated with disturbances in networks involved in emotion regulation, emotion processing, and perceptual functions, suggesting a network framework is integral to elucidating the pathophysiology of gSAD. However, fMRI does not measure the fast dynamic interconnections of functional networks. Therefore, we examined whole-brain functional connectomics with electroencephalogram (EEG) during resting-state. Resting-state EEG data was recorded for 32 patients with gSAD and 32 demographically-matched healthy controls (HC). Sensor-level connectivity analysis was applied on EEG data by using Weighted Phase Lag Index (WPLI) and graph analysis based on WPLI was used to determine clustering coefficient and characteristic path length to estimate local integration and global segregation of networks. WPLI results showed increased oscillatory midline coherence in the theta frequency band indicating higher connectivity in the gSAD relative to HC group during rest. Additionally, WPLI values positively correlated with state anxiety levels within the gSAD group but not the HC group. Our graph theory based connectomics analysis demonstrated increased clustering coefficient and decreased characteristic path length in theta-based whole brain functional organization in subjects with gSAD compared to HC. Theta-dependent interconnectivity was associated with state anxiety in gSAD and an increase in information processing efficiency in gSAD (compared to controls). Results may represent enhanced baseline self-focused attention, which is consistent with cognitive models of gSAD and fMRI studies implicating emotion dysregulation and disturbances in task negative networks (e.g., default mode network) in gSAD.
An Embeddable Virtual Machine for State Space Generation
Weber, M.; Bosnacki, D.; Edelkamp, S.
2007-01-01
The semantics of modelling languages are not always specified in a precise and formal way, and their rather complex underlying models make it a non-trivial exercise to reuse them in newly developed tools. We report on experiments with a virtual machine-based approach for state space generation. The
Formal refinement of extended state machines
Directory of Open Access Journals (Sweden)
Thomas Fayolle
2016-06-01
Full Text Available In a traditional formal development process, e.g. using the B method, the informal user requirements are (manually translated into a global abstract formal specification. This translation is especially difficult to achieve. The Event-B method was developed to incrementally and formally construct such a specification using stepwise refinement. Each increment takes into account new properties and system aspects. In this paper, we propose to couple a graphical notation called Algebraic State-Transition Diagrams (ASTD with an Event-B specification in order to provide a better understanding of the software behaviour. The dynamic behaviour is captured by the ASTD, which is based on automata and process algebra operators, while the data model is described by means of an Event-B specification. We propose a methodology to incrementally refine such specification couplings, taking into account new refinement relations and consistency conditions between the control specification and the data specification. We compare the specifications obtained using each approach for readability and proof complexity. The advantages and drawbacks of the traditional approach and of our methodology are discussed. The whole process is illustrated by a railway CBTC-like case study. Our approach is supported by tools for translating ASTD's into B and Event-B into B.
SwingStates: Adding state machines to Java and the Swing toolkit
Appert , Caroline; Beaudouin-Lafon , Michel
2008-01-01
International audience; This article describes SwingStates, a Java toolkit designed to facilitate the development of graphical user interfaces and bring advanced interaction techniques to the Java platform. SwingStates is based on the use of finite-state machines specified directly in Java to describe the behavior of interactive systems. State machines can be used to redefine the behavior of existing Swing widgets or, in combination with a new canvas widget that features a rich graphical mode...
Automatic Test Pattern Generator for Fuzzing Based on Finite State Machine
Directory of Open Access Journals (Sweden)
Ming-Hung Wang
2017-01-01
Full Text Available With the rapid development of the Internet, several emerging technologies are adopted to construct fancy, interactive, and user-friendly websites. Among these technologies, HTML5 is a popular one and is widely used in establishing modern sites. However, the security issues in the new web technologies are also raised and are worthy of investigation. For vulnerability investigation, many previous studies used fuzzing and focused on generation-based approaches to produce test cases for fuzzing; however, these methods require a significant amount of knowledge and mental efforts to develop test patterns for generating test cases. To decrease the entry barrier of conducting fuzzing, in this study, we propose a test pattern generation algorithm based on the concept of finite state machines. We apply graph analysis techniques to extract paths from finite state machines and use these paths to construct test patterns automatically. According to the proposal, fuzzing can be completed through inputting a regular expression corresponding to the test target. To evaluate the performance of our proposal, we conduct an experiment in identifying vulnerabilities of the input attributes in HTML5. According to the results, our approach is not only efficient but also effective for identifying weak validators in HTML5.
State Machine Framework And Its Use For Driving LHC Operational states
Misiowiec, M; Solfaroli Camilloci, M
2011-01-01
The LHC follows a complex operational cycle with 12 major phases that include equipment tests, preparation, beam injection, ramping and squeezing, finally followed by the physics phase. This cycle is modelled and enforced with a state machine, whereby each operational phase is represented by a state. On each transition, before entering the next state, a series of conditions is verified to make sure the LHC is ready to move on. The State Machine framework was developed to cater for building independent or embedded state machines. They safely drive between the states executing tasks bound to transitions and broadcast related information to interested parties. The framework encourages users to program their own actions. Simple configuration management allows the operators to define and maintain complex models themselves. An emphasis was also put on easy interaction with the remote state machine instances through standard communication protocols. On top of its core functionality, the framework offers a transparen...
Olayan, Rawan S.
2017-11-23
Motivation 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. Results 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 five 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.
Directory of Open Access Journals (Sweden)
Cai Wingfield
2017-09-01
Full Text Available There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro- and magneto-encephalography (EMEG, generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.
Artificial emotional model based on finite state machine
Institute of Scientific and Technical Information of China (English)
MENG Qing-mei; WU Wei-guo
2008-01-01
According to the basic emotional theory, the artificial emotional model based on the finite state machine(FSM) was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition function was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform.And the model included three subsystems: the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input (also a state-emotion). The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.
Employing finite-state machines in data integrity problems
Directory of Open Access Journals (Sweden)
Malikov Andrey
2016-01-01
Full Text Available This paper explores the issue of group integrity of tuple subsets regarding corporate integrity constraints in relational databases. A solution may be found by applying the finite-state machine theory to guarantee group integrity of data. We present a practical guide to coding such an automaton. After creating SQL queries to manipulate data and control its integrity for real data domains, we study the issue of query performance, determine the level of transaction isolation, and generate query plans.
Complete permutation Gray code implemented by finite state machine
Directory of Open Access Journals (Sweden)
Li Peng
2014-09-01
Full Text Available An enumerating method of complete permutation array is proposed. The list of n! permutations based on Gray code defined over finite symbol set Z(n = {1, 2, …, n} is implemented by finite state machine, named as n-RPGCF. An RPGCF can be used to search permutation code and provide improved lower bounds on the maximum cardinality of a permutation code in some cases.
Endriss, U.; Grandi, U.
Graph aggregation is the process of computing a single output graph that constitutes a good compromise between several input graphs, each provided by a different source. One needs to perform graph aggregation in a wide variety of situations, e.g., when applying a voting rule (graphs as preference
TensorFlow: A system for large-scale machine learning
Abadi, Martín; Barham, Paul; Chen, Jianmin; Chen, Zhifeng; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Irving, Geoffrey; Isard, Michael; Kudlur, Manjunath; Levenberg, Josh; Monga, Rajat; Moore, Sherry; Murray, Derek G.
2016-01-01
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexib...
Ren, Jie
2017-12-01
The process by which a kinesin motor couples its ATPase activity with concerted mechanical hand-over-hand steps is a foremost topic of molecular motor physics. Two major routes toward elucidating kinesin mechanisms are the motility performance characterization of velocity and run length, and single-molecular state detection experiments. However, these two sets of experimental approaches are largely uncoupled to date. Here, we introduce an integrative motility state analysis based on a theorized kinetic graph theory for kinesin, which, on one hand, is validated by a wealth of accumulated motility data, and, on the other hand, allows for rigorous quantification of state occurrences and chemomechanical cycling probabilities. An interesting linear scaling for kinesin motility performance across species is discussed as well. An integrative kinetic graph theory analysis provides a powerful tool to bridge motility and state characterization experiments, so as to forge a unified effort for the elucidation of the working mechanisms of molecular motors.
Towards Measuring the Abstractness of State Machines based on Mutation Testing
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Thomas Baar
2017-01-01
Full Text Available Abstract. The notation of state machines is widely adopted as a formalism to describe the behaviour of systems. Usually, multiple state machine models can be developed for the very same software system. Some of these models might turn out to be equivalent, but, in many cases, different state machines describing the same system also differ in their level of abstraction. In this paper, we present an approach to actually measure the abstractness level of state machines w.r.t. a given implemented software system. A state machine is considered to be less abstract when it is conceptionally closer to the implemented system. In our approach, this distance between state machine and implementation is measured by applying coverage criteria known from software mutation testing. Abstractness of state machines can be considered as a new metric. As for other metrics as well, a known value for the abstractness of a given state machine allows to assess its quality in terms of a simple number. In model-based software development projects, the abstract metric can help to prevent model degradation since it can actually measure the semantic distance from the behavioural specification of a system in form of a state machine to the current implementation of the system. In contrast to other metrics for state machines, the abstractness cannot be statically computed based on the state machine’s structure, but requires to execute both state machine and corresponding system implementation. The article is published in the author’s wording.
Logic synthesis for FPGA-based finite state machines
Barkalov, Alexander; Kolopienczyk, Malgorzata; Mielcarek, Kamil; Bazydlo, Grzegorz
2016-01-01
This book discusses control units represented by the model of a finite state machine (FSM). It contains various original methods and takes into account the peculiarities of field-programmable gate arrays (FPGA) chips and a FSM model. It shows that one of the peculiarities of FPGA chips is the existence of embedded memory blocks (EMB). The book is devoted to the solution of problems of logic synthesis and reduction of hardware amount in control units. The book will be interesting and useful for researchers and PhD students in the area of Electrical Engineering and Computer Science, as well as for designers of modern digital systems.
Towards Integration of Object-Oriented Languages and State Machines
DEFF Research Database (Denmark)
Madsen, Ole Lehrmann
1999-01-01
The goal of this paper is to obtain a one-to-one correspondence between state machines as e.g. used in UML and object-oriented programming languages. A proposal is made for a language mechanism that makes it possible for an object to change its virtual bindings at run-time. A state of an object may...... then be represented as a set of virtual bindings.One advantage of object-orientation is that it provides an integrating perspective on many phases of software development, including analysis, design and implementation. For the static set of OO language constructs there is almost a one-to-one correspondence between...... analysis/design notations and OO programming languages. No such correspondence exists for the dynamic aspects, but the proposed state-mechanism is a contribution to a better cor respondence. The proposal is based on previous work by Antero Taivalsaari and compared to the more complex features for changing...
Zhang, L.-C.; Patone, M.
2017-01-01
We synthesise the existing theory of graph sampling. We propose a formal definition of sampling in finite graphs, and provide a classification of potential graph parameters. We develop a general approach of Horvitz–Thompson estimation to T-stage snowball sampling, and present various reformulations of some common network sampling methods in the literature in terms of the outlined graph sampling theory.
On Graph Rewriting, Reduction and Evaluation
DEFF Research Database (Denmark)
Zerny, Ian
2010-01-01
We inter-derive two prototypical styles of graph reduction: reduction machines à la Turner and graph rewriting systems à la Barendregt et al. To this end, we adapt Danvy et al.'s mechanical program derivations from the world of terms to the world of graphs. We also outline how to inter-derive a t......We inter-derive two prototypical styles of graph reduction: reduction machines à la Turner and graph rewriting systems à la Barendregt et al. To this end, we adapt Danvy et al.'s mechanical program derivations from the world of terms to the world of graphs. We also outline how to inter...
Support vector machines for nuclear reactor state estimation
Energy Technology Data Exchange (ETDEWEB)
Zavaljevski, N.; Gross, K. C.
2000-02-14
Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformed into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm.
Support vector machines for nuclear reactor state estimation
International Nuclear Information System (INIS)
Zavaljevski, N.; Gross, K. C.
2000-01-01
Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformed into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm
Machine Control System of Steady State Superconducting Tokamak-1
Energy Technology Data Exchange (ETDEWEB)
Masand, Harish, E-mail: harish@ipr.res.in; Kumar, Aveg; Bhandarkar, M.; Mahajan, K.; Gulati, H.; Dhongde, J.; Patel, K.; Chudasma, H.; Pradhan, S.
2016-11-15
Highlights: • Central Control System. • SST-1. • Machine Control System. - Abstract: Central Control System (CCS) of the Steady State Superconducting Tokamak-1 (SST-1) controls and monitors around 25 plant and experiment subsystems of SST-1 located remotely from the Central-Control room. Machine Control System (MCS) is a supervisory system that sits on the top of the CCS hierarchy and implements the CCS state diagram. MCS ensures the software interlock between the SST-1 subsystems with the CCS, any subsystem communication failure or its local error does not prohibit the execution of the MCS and in-turn the CCS operation. MCS also periodically monitors the subsystem’s status and their vital process parameters throughout the campaign. It also provides the platform for the Central Control operator to visualize and exchange remotely the operational and experimental configuration parameters with the sub-systems. MCS remains operational 24 × 7 from the commencement to the termination of the SST-1 campaign. The developed MCS has performed robustly and flawlessly during all the last campaigns of SST-1 carried out so far. This paper will describe various aspects of the development of MCS.
X-Graphs: Language and Algorithms for Heterogeneous Graph Streams
2017-09-01
are widely used by academia and industry. 15. SUBJECT TERMS Data Analytics, Graph Analytics, High-Performance Computing 16. SECURITY CLASSIFICATION...form the core of the DeepDive Knowledge Construction System. 2 INTRODUCTION The goal of the X-Graphs project was to develop computational techniques...memory multicore machine. Ringo is based on Snap.py and SNAP, and uses Python . Ringo now allows the integration of Delite DSL Framework Graph
Directory of Open Access Journals (Sweden)
Klaudius eKalcher
2015-12-01
Full Text Available Identifying venous voxels in fMRI datasets is important to increase the specificity of fMRI analyses to microvasculature in the vicinity of the neural processes triggering the BOLD response. This is, however, difficult to achieve in particular in typical studies where magnitude images of BOLD EPI are the only data available. In this study, voxelwise functional connectivity graphs were computed on minimally preprocessed low TR (333 ms multiband resting-state fMRI data, using both high positive and negative correlations to define edges between nodes (voxels. A high correlation threshold for binarization ensures that most edges in the resulting sparse graph reflect the high coherence of signals in medium to large veins. Graph clustering based on the optimization of modularity was then employed to identify clusters of coherent voxels in this graph, and all clusters of 50 or more voxels were then interpreted as corresponding to medium to large veins. Indeed, a comparison with SWI reveals that 75.6 ± 5.9% of voxels within these large clusters overlap with veins visible in the SWI image or lie outside the brain parenchyma. Some of the remainingdifferences between the two modalities can be explained by imperfect alignment or geometric distortions between the two images. Overall, the graph clustering based method for identifying venous voxels has a high specificity as well as the additional advantages of being computed in the same voxel grid as the fMRI dataset itself and not needingany additional data beyond what is usually acquired (and exported in standard fMRI experiments.
Huang, Chien-Hung; Chang, Peter Mu-Hsin; Hsu, Chia-Wei; Huang, Chi-Ying F; Ng, Ka-Lok
2016-01-11
Non-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development process of drugs, as well as reducing side effects. This work integrates two approaches--machine learning algorithms and topological parameter-based classification--to develop a novel pipeline of drug repositioning to analyze four lung cancer microarray datasets, enriched biological processes, potential therapeutic drugs and targeted genes for NSCLC treatments. A total of 7 (8) and 11 (12) promising drugs (targeted genes) were discovered for treating early- and late-stage NSCLC, respectively. The effectiveness of these drugs is supported by the literature, experimentally determined in-vitro IC50 and clinical trials. This work provides better drug prediction accuracy than competitive research according to IC50 measurements. With the novel pipeline of drug repositioning, the discovery of enriched pathways and potential drugs related to NSCLC can provide insight into the key regulators of tumorigenesis and the treatment of NSCLC. Based on the verified effectiveness of the targeted drugs predicted by this pipeline, we suggest that our drug-finding pipeline is effective for repositioning drugs.
Brouwer, A.E.; Haemers, W.H.; Brouwer, A.E.; Haemers, W.H.
2012-01-01
This chapter presents some simple results on graph spectra.We assume the reader is familiar with elementary linear algebra and graph theory. Throughout, J will denote the all-1 matrix, and 1 is the all-1 vector.
A method for independent component graph analysis of resting-state fMRI
DEFF Research Database (Denmark)
de Paula, Demetrius Ribeiro; Ziegler, Erik; Abeyasinghe, Pubuditha M.
2017-01-01
Introduction Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguou......Introduction Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non......-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data. Objective Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory. Methods First, ICA was performed at the single-subject level in 15 healthy...... parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network. Results Network graph comparison between the classically constructed network...
Developing a PLC-friendly state machine model: lessons learned
Pessemier, Wim; Deconinck, Geert; Raskin, Gert; Saey, Philippe; Van Winckel, Hans
2014-07-01
Modern Programmable Logic Controllers (PLCs) have become an attractive platform for controlling real-time aspects of astronomical telescopes and instruments due to their increased versatility, performance and standardization. Likewise, vendor-neutral middleware technologies such as OPC Unified Architecture (OPC UA) have recently demonstrated that they can greatly facilitate the integration of these industrial platforms into the overall control system. Many practical questions arise, however, when building multi-tiered control systems that consist of PLCs for low level control, and conventional software and platforms for higher level control. How should the PLC software be structured, so that it can rely on well-known programming paradigms on the one hand, and be mapped to a well-organized OPC UA interface on the other hand? Which programming languages of the IEC 61131-3 standard closely match the problem domains of the abstraction levels within this structure? How can the recent additions to the standard (such as the support for namespaces and object-oriented extensions) facilitate a model based development approach? To what degree can our applications already take advantage of the more advanced parts of the OPC UA standard, such as the high expressiveness of the semantic modeling language that it defines, or the support for events, aggregation of data, automatic discovery, ... ? What are the timing and concurrency problems to be expected for the higher level tiers of the control system due to the cyclic execution of control and communication tasks by the PLCs? We try to answer these questions by demonstrating a semantic state machine model that can readily be implemented using IEC 61131 and OPC UA. One that does not aim to capture all possible states of a system, but rather one that attempts to organize the course-grained structure and behaviour of a system. In this paper we focus on the intricacies of this seemingly simple task, and on the lessons that we
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.
Equivalence of restricted Boltzmann machines and tensor network states
Chen, Jing; Cheng, Song; Xie, Haidong; Wang, Lei; Xiang, Tao
2018-02-01
The restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep learning. RBM finds wide applications in dimensional reduction, feature extraction, and recommender systems via modeling the probability distributions of a variety of input data including natural images, speech signals, and customer ratings, etc. We build a bridge between RBM and tensor network states (TNS) widely used in quantum many-body physics research. We devise efficient algorithms to translate an RBM into the commonly used TNS. Conversely, we give sufficient and necessary conditions to determine whether a TNS can be transformed into an RBM of given architectures. Revealing these general and constructive connections can cross fertilize both deep learning and quantum many-body physics. Notably, by exploiting the entanglement entropy bound of TNS, we can rigorously quantify the expressive power of RBM on complex data sets. Insights into TNS and its entanglement capacity can guide the design of more powerful deep learning architectures. On the other hand, RBM can represent quantum many-body states with fewer parameters compared to TNS, which may allow more efficient classical simulations.
Using support vector machines in the multivariate state estimation technique
International Nuclear Information System (INIS)
Zavaljevski, N.; Gross, K.C.
1999-01-01
One approach to validate nuclear power plant (NPP) signals makes use of pattern recognition techniques. This approach often assumes that there is a set of signal prototypes that are continuously compared with the actual sensor signals. These signal prototypes are often computed based on empirical models with little or no knowledge about physical processes. A common problem of all data-based models is their limited ability to make predictions on the basis of available training data. Another problem is related to suboptimal training algorithms. Both of these potential shortcomings with conventional approaches to signal validation and sensor operability validation are successfully resolved by adopting a recently proposed learning paradigm called the support vector machine (SVM). The work presented here is a novel application of SVM for data-based modeling of system state variables in an NPP, integrated with a nonlinear, nonparametric technique called the multivariate state estimation technique (MSET), an algorithm developed at Argonne National Laboratory for a wide range of nuclear plant applications
Rethinking State Politics: The Withering of State Dominant Machines in Brazil
Directory of Open Access Journals (Sweden)
André Borges
2007-03-01
Full Text Available Research on Brazilian federalism and state politics has focused mainly on the impact of federal arrangements on national political systems, whereas comparative analyses of the workings of state political institutions and patterns of political competition and decision-making have often been neglected. The article contributes to an emerging comparative literature on state politics by developing a typology that systematizes the variation in political competitiveness and the extent of state elites’ control over the electoral arena across Brazilian states. It relies on factor analysis to create an index of “electoral dominance”, comprised of a set of indicators of party and electoral competitiveness at the state level, which measures state elites’ capacity to control the state electoral arena over time. Based on this composite index and on available case-study evidence, the article applies the typological classificatory scheme to all 27 Brazilian states. Further, the article relies on the typological classification to assess the recent evolution of state-level political competitiveness. The empirical analysis demonstrates that state politics is becoming more competitive and fragmented, including in those states that have been characterized as bastions of oligarchism and political bossism. In view of these findings, the article argues that the power of state political machines rests on fragile foundations: in Brazil’s multiparty federalism, vertical competition between the federal and state governments in the provision of social policies works as a constraint on state bosses’ machine-building strategies. It is concluded that our previous views on state political dynamics are in serious need of re-evaluation.
Cao, Hengyi; Plichta, Michael M; Schäfer, Axel; Haddad, Leila; Grimm, Oliver; Schneider, Michael; Esslinger, Christine; Kirsch, Peter; Meyer-Lindenberg, Andreas; Tost, Heike
2014-01-01
The investigation of the brain connectome with functional magnetic resonance imaging (fMRI) and graph theory analyses has recently gained much popularity, but little is known about the robustness of these properties, in particular those derived from active fMRI tasks. Here, we studied the test-retest reliability of brain graphs calculated from 26 healthy participants with three established fMRI experiments (n-back working memory, emotional face-matching, resting state) and two parcellation schemes for node definition (AAL atlas, functional atlas proposed by Power et al.). We compared the intra-class correlation coefficients (ICCs) of five different data processing strategies and demonstrated a superior reliability of task-regression methods with condition-specific regressors. The between-task comparison revealed significantly higher ICCs for resting state relative to the active tasks, and a superiority of the n-back task relative to the face-matching task for global and local network properties. While the mean ICCs were typically lower for the active tasks, overall fair to good reliabilities were detected for global and local connectivity properties, and for the n-back task with both atlases, smallworldness. For all three tasks and atlases, low mean ICCs were seen for the local network properties. However, node-specific good reliabilities were detected for node degree in regions known to be critical for the challenged functions (resting-state: default-mode network nodes, n-back: fronto-parietal nodes, face-matching: limbic nodes). Between-atlas comparison demonstrated significantly higher reliabilities for the functional parcellations for global and local network properties. Our findings can inform the choice of processing strategies, brain atlases and outcome properties for fMRI studies using active tasks, graph theory methods, and within-subject designs, in particular future pharmaco-fMRI studies. © 2013 Elsevier Inc. All rights reserved.
Graph-based modelling in engineering
Rysiński, Jacek
2017-01-01
This book presents versatile, modern and creative applications of graph theory in mechanical engineering, robotics and computer networks. Topics related to mechanical engineering include e.g. machine and mechanism science, mechatronics, robotics, gearing and transmissions, design theory and production processes. The graphs treated are simple graphs, weighted and mixed graphs, bond graphs, Petri nets, logical trees etc. The authors represent several countries in Europe and America, and their contributions show how different, elegant, useful and fruitful the utilization of graphs in modelling of engineering systems can be. .
Finite State Machine Analysis of Remote Sensor Data
International Nuclear Information System (INIS)
Barbson, John M.
1999-01-01
The use of unattended monitoring systems for monitoring the status of high value assets and processes has proven to be less costly and less intrusive than the on-site inspections which they are intended to replace. However, these systems present a classic information overload problem to anyone trying to analyze the resulting sensor data. These data are typically so voluminous and contain information at such a low level that the significance of any single reading (e.g., a door open event) is not obvious. Sophisticated, automated techniques are needed to extract expected patterns in the data and isolate and characterize the remaining patterns that are due to undeclared activities. This paper describes a data analysis engine that runs a state machine model of each facility and its sensor suite. It analyzes the raw sensor data, converting and combining the inputs from many sensors into operator domain level information. It compares the resulting activities against a set of activities declared by an inspector or operator, and then presents the differences in a form comprehensible to an inspector. Although the current analysis engine was written with international nuclear material safeguards, nonproliferation, and transparency in mind, since there is no information about any particular facility in the software, there is no reason why it cannot be applied anywhere it is important to verify processes are occurring as expected, to detect intrusion into a secured area, or to detect the diversion of valuable assets
Using Pipelined XNOR Logic to Reduce SEU Risks in State Machines
Le, Martin; Zheng, Xin; Katanyoutant, Sunant
2008-01-01
Single-event upsets (SEUs) pose great threats to avionic systems state machine control logic, which are frequently used to control sequence of events and to qualify protocols. The risks of SEUs manifest in two ways: (a) the state machine s state information is changed, causing the state machine to unexpectedly transition to another state; (b) due to the asynchronous nature of SEU, the state machine's state registers become metastable, consequently causing any combinational logic associated with the metastable registers to malfunction temporarily. Effect (a) can be mitigated with methods such as triplemodular redundancy (TMR). However, effect (b) cannot be eliminated and can degrade the effectiveness of any mitigation method of effect (a). Although there is no way to completely eliminate the risk of SEU-induced errors, the risk can be made very small by use of a combination of very fast state-machine logic and error-detection logic. Therefore, one goal of two main elements of the present method is to design the fastest state-machine logic circuitry by basing it on the fastest generic state-machine design, which is that of a one-hot state machine. The other of the two main design elements is to design fast error-detection logic circuitry and to optimize it for implementation in a field-programmable gate array (FPGA) architecture: In the resulting design, the one-hot state machine is fitted with a multiple-input XNOR gate for detection of illegal states. The XNOR gate is implemented with lookup tables and with pipelines for high speed. In this method, the task of designing all the logic must be performed manually because no currently available logic synthesis software tool can produce optimal solutions of design problems of this type. However, some assistance is provided by a script, written for this purpose in the Python language (an object-oriented interpretive computer language) to automatically generate hardware description language (HDL) code from state
Doucet, Gaelle E; Rider, Robert; Taylor, Nathan; Skidmore, Christopher; Sharan, Ashwini; Sperling, Michael; Tracy, Joseph I
2015-04-01
This study determined the ability of resting-state functional connectivity (rsFC) graph-theory measures to predict neurocognitive status postsurgery in patients with temporal lobe epilepsy (TLE) who underwent anterior temporal lobectomy (ATL). A presurgical resting-state functional magnetic resonance imaging (fMRI) condition was collected in 16 left and 16 right TLE patients who underwent ATL. In addition, patients received neuropsychological testing pre- and postsurgery in verbal and nonverbal episodic memory, language, working memory, and attention domains. Regarding the functional data, we investigated three graph-theory properties (local efficiency, distance, and participation), measuring segregation, integration and centrality, respectively. These measures were only computed in regions of functional relevance to the ictal pathology, or the cognitive domain. Linear regression analyses were computed to predict the change in each neurocognitive domain. Our analyses revealed that cognitive outcome was successfully predicted with at least 68% of the variance explained in each model, for both TLE groups. The only model not significantly predictive involved nonverbal episodic memory outcome in right TLE. Measures involving the healthy hippocampus were the most common among the predictors, suggesting that enhanced integration of this structure with the rest of the brain may improve cognitive outcomes. Regardless of TLE group, left inferior frontal regions were the best predictors of language outcome. Working memory outcome was predicted mostly by right-sided regions, in both groups. Overall, the results indicated our integration measure was the most predictive of neurocognitive outcome. In contrast, our segregation measure was the least predictive. This study provides evidence that presurgery rsFC measures may help determine neurocognitive outcomes following ATL. The results have implications for refining our understanding of compensatory reorganization and predicting
Quantum walks on quotient graphs
International Nuclear Information System (INIS)
Krovi, Hari; Brun, Todd A.
2007-01-01
A discrete-time quantum walk on a graph Γ is the repeated application of a unitary evolution operator to a Hilbert space corresponding to the graph. If this unitary evolution operator has an associated group of symmetries, then for certain initial states the walk will be confined to a subspace of the original Hilbert space. Symmetries of the original graph, given by its automorphism group, can be inherited by the evolution operator. We show that a quantum walk confined to the subspace corresponding to this symmetry group can be seen as a different quantum walk on a smaller quotient graph. We give an explicit construction of the quotient graph for any subgroup H of the automorphism group and illustrate it with examples. The automorphisms of the quotient graph which are inherited from the original graph are the original automorphism group modulo the subgroup H used to construct it. The quotient graph is constructed by removing the symmetries of the subgroup H from the original graph. We then analyze the behavior of hitting times on quotient graphs. Hitting time is the average time it takes a walk to reach a given final vertex from a given initial vertex. It has been shown in earlier work [Phys. Rev. A 74, 042334 (2006)] that the hitting time for certain initial states of a quantum walks can be infinite, in contrast to classical random walks. We give a condition which determines whether the quotient graph has infinite hitting times given that they exist in the original graph. We apply this condition for the examples discussed and determine which quotient graphs have infinite hitting times. All known examples of quantum walks with hitting times which are short compared to classical random walks correspond to systems with quotient graphs much smaller than the original graph; we conjecture that the existence of a small quotient graph with finite hitting times is necessary for a walk to exhibit a quantum speedup
Identifying student stuck states in programmingassignments using machine learning
Lindell, Johan
2014-01-01
Intelligent tutors are becoming more popular with the increased use of computersand hand held devices in the education sphere. An area of research isinvestigating how machine learning can be used to improve the precision andfeedback of the tutor. This thesis compares machine learning clustering algorithmswith various distance functions in an attempt to cluster together codesnapshots of students solving a programming task. It investigates whethera general non-problem specific implementation of...
DEFF Research Database (Denmark)
Vestergaard, Preben Dahl; Hartnell, Bert L.
2006-01-01
There are many results dealing with the problem of decomposing a fixed graph into isomorphic subgraphs. There has also been work on characterizing graphs with the property that one can delete the edges of a number of edge disjoint copies of the subgraph and, regardless of how that is done, the gr...
A Modal-Logic Based Graph Abstraction
Bauer, J.; Boneva, I.B.; Kurban, M.E.; Rensink, Arend; Ehrig, H; Heckel, R.; Rozenberg, G.; Taentzer, G.
2008-01-01
Infinite or very large state spaces often prohibit the successful verification of graph transformation systems. Abstract graph transformation is an approach that tackles this problem by abstracting graphs to abstract graphs of bounded size and by lifting application of productions to abstract
Twentieth Century evolution of machining in the United States – An ...
Indian Academy of Sciences (India)
R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22
beginning of the Industrial Revolution in the late 1700's, virtually no ... expected that, by the middle of the 19th Century, as machine tools began to be manufactured .... Twentieth Century evolution of machining in the United States. 873. DESIGN ... Merchant M E 1961 The manufacturing system concept in production ...
Machine Learning Applications to Resting-State Functional MR Imaging Analysis.
Billings, John M; Eder, Maxwell; Flood, William C; Dhami, Devendra Singh; Natarajan, Sriraam; Whitlow, Christopher T
2017-11-01
Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances. Copyright © 2017 Elsevier Inc. All rights reserved.
Graph-theoretic techniques for web content mining
Schenker, Adam; Bunke, Horst; Last, Mark
2005-01-01
This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors.
Online State Space Model Parameter Estimation in Synchronous Machines
Directory of Open Access Journals (Sweden)
Z. Gallehdari
2014-06-01
The suggested approach is evaluated for a sample synchronous machine model. Estimated parameters are tested for different inputs at different operating conditions. The effect of noise is also considered in this study. Simulation results show that the proposed approach provides good accuracy for parameter estimation.
International Nuclear Information System (INIS)
Vincze, Arpad; Nemeth, Andras
2013-01-01
According to a recent statement, the IAEA seeks to develop a more effective safeguards system to achieve greater deterrence, because deterrence of proliferation is much more effective than detection. To achieve this goal, a less predictive safeguards system is being developed based on the advanced state-level approach that is driven by all available safeguards-relevant information. The 'directed graph analysis' is recommended as a possible methodology to implement acquisition path analysis by the IAEA to support the State evaluation process. The basic methodology is simple, well established, powerful, and its adaptation to the modelling of the nuclear profile of a State requires minimum software development. Based on this methodology the material flow network model has been developed under the Hungarian Support Programme to the IAEA, which is described in detail. In the proposed model, materials in different chemical and physical form can flow through pipes representing declared processes, material transports, diversions or undeclared processes. The nodes of the network are the material types, while the edges of the network are the pipes. A state parameter (p) is assigned to each node and edge representing the probability of their existence in the State. The possible application of this model in the State-level analytical approach will be discussed and outlook for further work will be given. The paper is followed by the slides of the presentation
Underlying finite state machine for the social engineering attack detection model
CSIR Research Space (South Africa)
Mouton, Francois
2017-08-01
Full Text Available one to have a clearer overview of the mental processing performed within the model. While the current model provides a general procedural template for implementing detection mechanisms for social engineering attacks, the finite state machine provides a...
State Authorization Tracking System (StATS) - Data, Charts and Graphs
U.S. Environmental Protection Agency — The State Authorization Tracking System (StATS) is an information management system designed to document the progress of each state and territory in establishing and...
Bollobás, Béla
1998-01-01
The time has now come when graph theory should be part of the education of every serious student of mathematics and computer science, both for its own sake and to enhance the appreciation of mathematics as a whole. This book is an in-depth account of graph theory, written with such a student in mind; it reflects the current state of the subject and emphasizes connections with other branches of pure mathematics. The volume grew out of the author's earlier book, Graph Theory -- An Introductory Course, but its length is well over twice that of its predecessor, allowing it to reveal many exciting new developments in the subject. Recognizing that graph theory is one of several courses competing for the attention of a student, the book contains extensive descriptive passages designed to convey the flavor of the subject and to arouse interest. In addition to a modern treatment of the classical areas of graph theory such as coloring, matching, extremal theory, and algebraic graph theory, the book presents a detailed ...
Three Syntactic Theories for Combinatory Graph Reduction
DEFF Research Database (Denmark)
Danvy, Olivier; Zerny, Ian
2011-01-01
in a third syntactic theory. The structure of the store-based abstract machine corresponding to this third syntactic theory oincides with that of Turner's original reduction machine. The three syntactic theories presented here The three syntactic heories presented here therefore have the following......We present a purely syntactic theory of graph reduction for the canonical combinators S, K, and I, where graph vertices are represented with evaluation contexts and let expressions. We express this syntactic theory as a reduction semantics, which we refocus into the first storeless abstract machine...... for combinatory graph reduction, which we refunctionalize into the first storeless natural semantics for combinatory graph reduction.We then factor out the introduction of let expressions to denote as many graph vertices as possible upfront instead of on demand, resulting in a second syntactic theory, this one...
Three Syntactic Theories for Combinatory Graph Reduction
DEFF Research Database (Denmark)
Danvy, Olivier; Zerny, Ian
2013-01-01
, as a store-based reduction semantics of combinatory term graphs. We then refocus this store-based reduction semantics into a store-based abstract machine. The architecture of this store-based abstract machine coincides with that of Turner's original reduction machine. The three syntactic theories presented......We present a purely syntactic theory of graph reduction for the canonical combinators S, K, and I, where graph vertices are represented with evaluation contexts and let expressions. We express this rst syntactic theory as a storeless reduction semantics of combinatory terms. We then factor out...... the introduction of let expressions to denote as many graph vertices as possible upfront instead of on demand . The factored terms can be interpreted as term graphs in the sense of Barendregt et al. We express this second syntactic theory, which we prove equivalent to the rst, as a storeless reduction semantics...
Directory of Open Access Journals (Sweden)
Michael González-Durruthy
2017-11-01
Full Text Available This study presents the impact of carbon nanotubes (CNTs on mitochondrial oxygen mass flux (Jm under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra star graph transform (spectral moments and perturbation theory. The experimental measures of Jm showed that no tested CNT family can inhibit the oxygen consumption profiles of mitochondria. The best model for the prediction of Jm for other CNTs was provided by random forest using eight features, obtaining test R-squared (R2 of 0.863 and test root-mean-square error (RMSE of 0.0461. The results demonstrate the capability of encoding CNT information into spectral moments of the Raman star graphs (SG transform with a potential applicability as predictive tools in nanotechnology and material risk assessments.
Trudeau, Richard J
1994-01-01
Preface1. Pure Mathematics Introduction; Euclidean Geometry as Pure Mathematics; Games; Why Study Pure Mathematics?; What's Coming; Suggested Reading2. Graphs Introduction; Sets; Paradox; Graphs; Graph diagrams; Cautions; Common Graphs; Discovery; Complements and Subgraphs; Isomorphism; Recognizing Isomorphic Graphs; Semantics The Number of Graphs Having a Given nu; Exercises; Suggested Reading3. Planar Graphs Introduction; UG, K subscript 5, and the Jordan Curve Theorem; Are there More Nonplanar Graphs?; Expansions; Kuratowski's Theorem; Determining Whether a Graph is Planar or
State of the Art Review on Theoretical Tribology of Fluid Power Displacement Machines
DEFF Research Database (Denmark)
Cerimagic, Remzija; Johansen, Per; Andersen, Torben O.
2016-01-01
machines, and also the work done to validate the theoretical models. This review is not a complete historical account, but aim to describe current trends in fluid power displacement machine tribology. The review considers the rheological models used in the theoretical approaches, the modeling...... and wear mechanisms in the lubricating gaps in fluid power machines is confined to simulation models, as experimental treatments of these mechanisms are very difficult. The aim of this paper is a state of the art review on the theoretical work for the design and optimization of fluid power displacement...... of elastohydrodynamic effects, the modeling of thermal effects, and finally the experimental validation of the theoretical models....
State Machine Modeling of the Space Launch System Solid Rocket Boosters
Harris, Joshua A.; Patterson-Hine, Ann
2013-01-01
The Space Launch System is a Shuttle-derived heavy-lift vehicle currently in development to serve as NASA's premiere launch vehicle for space exploration. The Space Launch System is a multistage rocket with two Solid Rocket Boosters and multiple payloads, including the Multi-Purpose Crew Vehicle. Planned Space Launch System destinations include near-Earth asteroids, the Moon, Mars, and Lagrange points. The Space Launch System is a complex system with many subsystems, requiring considerable systems engineering and integration. To this end, state machine analysis offers a method to support engineering and operational e orts, identify and avert undesirable or potentially hazardous system states, and evaluate system requirements. Finite State Machines model a system as a finite number of states, with transitions between states controlled by state-based and event-based logic. State machines are a useful tool for understanding complex system behaviors and evaluating "what-if" scenarios. This work contributes to a state machine model of the Space Launch System developed at NASA Ames Research Center. The Space Launch System Solid Rocket Booster avionics and ignition subsystems are modeled using MATLAB/Stateflow software. This model is integrated into a larger model of Space Launch System avionics used for verification and validation of Space Launch System operating procedures and design requirements. This includes testing both nominal and o -nominal system states and command sequences.
Static Object Detection Based on a Dual Background Model and a Finite-State Machine
Directory of Open Access Journals (Sweden)
Heras Evangelio Rubén
2011-01-01
Full Text Available Detecting static objects in video sequences has a high relevance in many surveillance applications, such as the detection of abandoned objects in public areas. In this paper, we present a system for the detection of static objects in crowded scenes. Based on the detection of two background models learning at different rates, pixels are classified with the help of a finite-state machine. The background is modelled by two mixtures of Gaussians with identical parameters except for the learning rate. The state machine provides the meaning for the interpretation of the results obtained from background subtraction; it can be implemented as a look-up table with negligible computational cost and it can be easily extended. Due to the definition of the states in the state machine, the system can be used either full automatically or interactively, making it extremely suitable for real-life surveillance applications. The system was successfully validated with several public datasets.
Diestel, Reinhard
2017-01-01
This standard textbook of modern graph theory, now in its fifth edition, combines the authority of a classic with the engaging freshness of style that is the hallmark of active mathematics. It covers the core material of the subject with concise yet reliably complete proofs, while offering glimpses of more advanced methods in each field by one or two deeper results, again with proofs given in full detail. The book can be used as a reliable text for an introductory course, as a graduate text, and for self-study. From the reviews: “This outstanding book cannot be substituted with any other book on the present textbook market. It has every chance of becoming the standard textbook for graph theory.”Acta Scientiarum Mathematiciarum “Deep, clear, wonderful. This is a serious book about the heart of graph theory. It has depth and integrity. ”Persi Diaconis & Ron Graham, SIAM Review “The book has received a very enthusiastic reception, which it amply deserves. A masterly elucidation of modern graph theo...
Resting-state theta band connectivity and graph analysis in generalized social anxiety disorder
Directory of Open Access Journals (Sweden)
Mengqi Xing
2017-01-01
Conclusions: Theta-dependent interconnectivity was associated with state anxiety in gSAD and an increase in information processing efficiency in gSAD (compared to controls. Results may represent enhanced baseline self-focused attention, which is consistent with cognitive models of gSAD and fMRI studies implicating emotion dysregulation and disturbances in task negative networks (e.g., default mode network in gSAD.
Information Graph Flow: A Geometric Approximation of Quantum and Statistical Systems
Vanchurin, Vitaly
2018-05-01
Given a quantum (or statistical) system with a very large number of degrees of freedom and a preferred tensor product factorization of the Hilbert space (or of a space of distributions) we describe how it can be approximated with a very low-dimensional field theory with geometric degrees of freedom. The geometric approximation procedure consists of three steps. The first step is to construct weighted graphs (we call information graphs) with vertices representing subsystems (e.g., qubits or random variables) and edges representing mutual information (or the flow of information) between subsystems. The second step is to deform the adjacency matrices of the information graphs to that of a (locally) low-dimensional lattice using the graph flow equations introduced in the paper. (Note that the graph flow produces very sparse adjacency matrices and thus might also be used, for example, in machine learning or network science where the task of graph sparsification is of a central importance.) The third step is to define an emergent metric and to derive an effective description of the metric and possibly other degrees of freedom. To illustrate the procedure we analyze (numerically and analytically) two information graph flows with geometric attractors (towards locally one- and two-dimensional lattices) and metric perturbations obeying a geometric flow equation. Our analysis also suggests a possible approach to (a non-perturbative) quantum gravity in which the geometry (a secondary object) emerges directly from a quantum state (a primary object) due to the flow of the information graphs.
Non-parametric Bayesian graph models reveal community structure in resting state fMRI
DEFF Research Database (Denmark)
Andersen, Kasper Winther; Madsen, Kristoffer H.; Siebner, Hartwig Roman
2014-01-01
Modeling of resting state functional magnetic resonance imaging (rs-fMRI) data using network models is of increasing interest. It is often desirable to group nodes into clusters to interpret the communication patterns between nodes. In this study we consider three different nonparametric Bayesian...... models for node clustering in complex networks. In particular, we test their ability to predict unseen data and their ability to reproduce clustering across datasets. The three generative models considered are the Infinite Relational Model (IRM), Bayesian Community Detection (BCD), and the Infinite...... between clusters. BCD restricts the between-cluster link probabilities to be strictly lower than within-cluster link probabilities to conform to the community structure typically seen in social networks. IDM only models a single between-cluster link probability, which can be interpreted as a background...
Steady State Advanced Tokamak (SSAT): The mission and the machine
International Nuclear Information System (INIS)
Thomassen, K.; Goldston, R.; Nevins, B.; Neilson, H.; Shannon, T.; Montgomery, B.
1992-03-01
Extending the tokamak concept to the steady state regime and pursuing advances in tokamak physics are important and complementary steps for the magnetic fusion energy program. The required transition away from inductive current drive will provide exciting opportunities for advances in tokamak physics, as well as important impetus to drive advances in fusion technology. Recognizing this, the Fusion Policy Advisory Committee and the US National Energy Strategy identified the development of steady state tokamak physics and technology, and improvements in the tokamak concept, as vital elements in the magnetic fusion energy development plan. Both called for the construction of a steady state tokamak facility to address these plan elements. Advances in physics that produce better confinement and higher pressure limits are required for a similar unit size reactor. Regimes with largely self-driven plasma current are required to permit a steady-state tokamak reactor with acceptable recirculating power. Reliable techniques of disruption control will be needed to achieve the availability goals of an economic reactor. Thus the central role of this new tokamak facility is to point the way to a more attractive demonstration reactor (DEMO) than the present data base would support. To meet the challenges, we propose a new ''Steady State Advanced Tokamak'' (SSAT) facility that would develop and demonstrate optimized steady state tokamak operating mode. While other tokamaks in the world program employ superconducting toroidal field coils, SSAT would be the first major tokamak to operate with a fully superconducting coil set in the elongated, divertor geometry planned for ITER and DEMO
A rule-based approach to model checking of UML state machines
Grobelna, Iwona; Grobelny, Michał; Stefanowicz, Łukasz
2016-12-01
In the paper a new approach to formal verification of control process specification expressed by means of UML state machines in version 2.x is proposed. In contrast to other approaches from the literature, we use the abstract and universal rule-based logical model suitable both for model checking (using the nuXmv model checker), but also for logical synthesis in form of rapid prototyping. Hence, a prototype implementation in hardware description language VHDL can be obtained that fully reflects the primary, already formally verified specification in form of UML state machines. Presented approach allows to increase the assurance that implemented system meets the user-defined requirements.
Gould, Ronald
2012-01-01
This introduction to graph theory focuses on well-established topics, covering primary techniques and including both algorithmic and theoretical problems. The algorithms are presented with a minimum of advanced data structures and programming details. This thoroughly corrected 1988 edition provides insights to computer scientists as well as advanced undergraduates and graduate students of topology, algebra, and matrix theory. Fundamental concepts and notation and elementary properties and operations are the first subjects, followed by examinations of paths and searching, trees, and networks. S
Chartrand, Gary; Zhang, Ping
2010-01-01
Gary Chartrand has influenced the world of Graph Theory for almost half a century. He has supervised more than a score of Ph.D. dissertations and written several books on the subject. The most widely known of these texts, Graphs and Digraphs, … has much to recommend it, with clear exposition, and numerous challenging examples [that] make it an ideal textbook for the advanced undergraduate or beginning graduate course. The authors have updated their notation to reflect the current practice in this still-growing area of study. By the authors' estimation, the 5th edition is approximately 50% longer than the 4th edition. … the legendary Frank Harary, author of the second graph theory text ever produced, is one of the figures profiled. His book was the standard in the discipline for several decades. Chartrand, Lesniak and Zhang have produced a worthy successor.-John T. Saccoman, MAA Reviews, June 2012 (This book is in the MAA's basic library list.)As with the earlier editions, the current text emphasizes clear...
Implementation of a Microcode-controlled State Machine and Simulator in AVR Microcontrollers (MICoSS
Directory of Open Access Journals (Sweden)
S. Korbel
2005-01-01
Full Text Available This paper describes the design of a microcode-controlled state machine and its software implementation in Atmel AVR microcontrollers. In particular, ATmega103 and ATmega128 microcontrollers are used. This design is closely related to the software implementation of a simulator in AVR microcontrollers. This simulator communicates with the designed state machine and presents a complete design environment for microcode development and debugging. These two devices can be interconnected by a flat cable and linked to a computer through a serial or USB interface.Both devices share the control software that allows us to create and edit microprograms and to control the whole state machine. It is possible to start, cancel or step through the execution of the microprograms. The operator can also observe the current state of the state machine. The second part of the control software enables the operator to create and compile simulating programs. The control software communicates with both devices using commands. All the results of this communication are well arranged in dialog boxes and windows.
Using Expert Systems in Evaluation of the State of High Voltage Machine Insulation Systems
Directory of Open Access Journals (Sweden)
K. Záliš
2000-01-01
Full Text Available Expert systems are used for evaluating the actual state and future behavior of insulating systems of high voltage electrical machines and equipment. Several rule-based expert systems have been developed in cooperation with top diagnostic workplaces in the Czech Republic for this purpose. The IZOLEX expert system evaluates diagnostic measurement data from commonly used offline diagnostic methods for the diagnostic of high voltage insulation of rotating machines, non-rotating machines and insulating oils. The CVEX expert system evaluates the discharge activity on high voltage electrical machines and equipment by means of an off-line measurement. The CVEXON expert system is for evaluating the discharge activity by on-line measurement, and the ALTONEX expert system is the expert system for on-line monitoring of rotating machines. These developed expert systems are also used for educating students (in bachelor, master and post-graduate studies and in courses which are organized for practicing engineers and technicians and for specialists in the electrical power engineering branch. A complex project has recently been set up to evaluate the measurement of partial discharges. Two parallel expert systems for evaluating partial dischatge activity on high voltage electrical machines will work at the same time in this complex evaluating system.
Laser Beam Machining (LBM), State of the Art and New Opportunities
Meijer, J.
2004-01-01
An overview is given of the state of the art of laser beam machining in general with special emphasis on applications of short and ultrashort lasers. In laser welding the trend is to apply optical sensors for process control. Laser surface treatment is mostly used to apply corrosion and wear
Constrained state-feedback control of an externally excited synchronous machine
Carpiuc, S.C.; Lazar, M.
2013-01-01
State-feedback control of externally excited synchronous machines employed in applications such as hybrid electric vehicles and full electric vehicles is a challenging problem. Indeed, these applications are characterized by fast dynamics that are subject to hard physical and control constraints.
On Coding the States of Sequential Machines with the Use of Partition Pairs
DEFF Research Database (Denmark)
Zahle, Torben U.
1966-01-01
This article introduces a new technique of making state assignment for sequential machines. The technique is in line with the approach used by Hartmanis [l], Stearns and Hartmanis [3], and Curtis [4]. It parallels the work of Dolotta and McCluskey [7], although it was developed independently...
Practical programmable circuits a guide to PLDs, state machines, and microcontrollers
Broesch, James D
1991-01-01
This is a practical guide to programmable logic devices. It covers all devices related to PLD: PALs, PGAs, state machines, and microcontrollers. Usefulness is evaluated; support needed in order to effectively use the devices is discussed. All examples are based on real-world circuits.
Abellán-Nebot, J. V.; Liu, J.; Romero, F.
2009-11-01
The State Space modelling approach has been recently proposed as an engineering-driven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.
Implementing finite state machines in a computer-based teaching system
Hacker, Charles H.; Sitte, Renate
1999-09-01
Finite State Machines (FSM) are models for functions commonly implemented in digital circuits such as timers, remote controls, and vending machines. Teaching FSM is core in the curriculum of many university digital electronic or discrete mathematics subjects. Students often have difficulties grasping the theoretical concepts in the design and analysis of FSM. This has prompted the author to develop an MS-WindowsTM compatible software, WinState, that provides a tutorial style teaching aid for understanding the mechanisms of FSM. The animated computer screen is ideal for visually conveying the required design and analysis procedures. WinState complements other software for combinatorial logic previously developed by the author, and enhances the existing teaching package by adding sequential logic circuits. WinState enables the construction of a students own FSM, which can be simulated, to test the design for functionality and possible errors.
Mandrà, Salvatore; Zhu, Zheng; Katzgraber, Helmut G
2017-02-17
We study the performance of the D-Wave 2X quantum annealing machine on systems with well-controlled ground-state degeneracy. While obtaining the ground state of a spin-glass benchmark instance represents a difficult task, the gold standard for any optimization algorithm or machine is to sample all solutions that minimize the Hamiltonian with more or less equal probability. Our results show that while naive transverse-field quantum annealing on the D-Wave 2X device can find the ground-state energy of the problems, it is not well suited in identifying all degenerate ground-state configurations associated with a particular instance. Even worse, some states are exponentially suppressed, in agreement with previous studies on toy model problems [New J. Phys. 11, 073021 (2009)NJOPFM1367-263010.1088/1367-2630/11/7/073021]. These results suggest that more complex driving Hamiltonians are needed in future quantum annealing machines to ensure a fair sampling of the ground-state manifold.
Bipartite separability and nonlocal quantum operations on graphs
Dutta, Supriyo; Adhikari, Bibhas; Banerjee, Subhashish; Srikanth, R.
2016-07-01
In this paper we consider the separability problem for bipartite quantum states arising from graphs. Earlier it was proved that the degree criterion is the graph-theoretic counterpart of the familiar positive partial transpose criterion for separability, although there are entangled states with positive partial transpose for which the degree criterion fails. Here we introduce the concept of partially symmetric graphs and degree symmetric graphs by using the well-known concept of partial transposition of a graph and degree criteria, respectively. Thus, we provide classes of bipartite separable states of dimension m ×n arising from partially symmetric graphs. We identify partially asymmetric graphs that lack the property of partial symmetry. We develop a combinatorial procedure to create a partially asymmetric graph from a given partially symmetric graph. We show that this combinatorial operation can act as an entanglement generator for mixed states arising from partially symmetric graphs.
International Nuclear Information System (INIS)
Heranudin; Rajiman; Parwanto; Edy Slamet R
2015-01-01
Software programming for the new solid target transfer control system referred to the working principle of the whole each sub system. System modeling with state machine diagram was chosen because this simplified a complex design of the control system. State machine implementation of this system was performed by creating basic state drawn from the working system of each sub system. All states with their described inputs, outputs and algorithms were compiled in the sequential state machine diagram. In order to ease the operation, three modes namely automatic, major states and micro states were created. Testing of the system has been conducted and as a result, the system worked properly. The implementation of State machine based on LabView has several advantages such as faster, easier programming and the capability for further developments. (author)
Chartrand, Gary; Rosen, Kenneth H
2008-01-01
Beginning with the origin of the four color problem in 1852, the field of graph colorings has developed into one of the most popular areas of graph theory. Introducing graph theory with a coloring theme, Chromatic Graph Theory explores connections between major topics in graph theory and graph colorings as well as emerging topics. This self-contained book first presents various fundamentals of graph theory that lie outside of graph colorings, including basic terminology and results, trees and connectivity, Eulerian and Hamiltonian graphs, matchings and factorizations, and graph embeddings. The remainder of the text deals exclusively with graph colorings. It covers vertex colorings and bounds for the chromatic number, vertex colorings of graphs embedded on surfaces, and a variety of restricted vertex colorings. The authors also describe edge colorings, monochromatic and rainbow edge colorings, complete vertex colorings, several distinguishing vertex and edge colorings, and many distance-related vertex coloring...
Scaling up liquid state machines to predict over address events from dynamic vision sensors.
Kaiser, Jacques; Stal, Rainer; Subramoney, Anand; Roennau, Arne; Dillmann, Rüdiger
2017-09-01
Short-term visual prediction is important both in biology and robotics. It allows us to anticipate upcoming states of the environment and therefore plan more efficiently. In theoretical neuroscience, liquid state machines have been proposed as a biologically inspired method to perform asynchronous prediction without a model. However, they have so far only been demonstrated in simulation or small scale pre-processed camera images. In this paper, we use a liquid state machine to predict over the whole [Formula: see text] event stream provided by a real dynamic vision sensor (DVS, or silicon retina). Thanks to the event-based nature of the DVS, the liquid is constantly fed with data when an object is in motion, fully embracing the asynchronicity of spiking neural networks. We propose a smooth continuous representation of the event stream for the short-term visual prediction task. Moreover, compared to previous works (2002 Neural Comput. 2525 282-93 and Burgsteiner H et al 2007 Appl. Intell. 26 99-109), we scale the input dimensionality that the liquid operates on by two order of magnitudes. We also expose the current limits of our method by running experiments in a challenging environment where multiple objects are in motion. This paper is a step towards integrating biologically inspired algorithms derived in theoretical neuroscience to real world robotic setups. We believe that liquid state machines could complement current prediction algorithms used in robotics, especially when dealing with asynchronous sensors.
A Finite State Machine Approach to Algorithmic Lateral Inhibition for Real-Time Motion Detection †
Directory of Open Access Journals (Sweden)
María T. López
2018-05-01
Full Text Available Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best-characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The neurally-inspired lateral inhibition method, and its application to motion detection tasks, have been successfully implemented in recent years. In this paper, control knowledge of the algorithmic lateral inhibition (ALI method is described and applied by means of finite state machines, in which the state space is constituted from the set of distinguishable cases of accumulated charge in a local memory. The article describes an ALI implementation for a motion detection task. For the implementation, we have chosen to use one of the members of the 16-nm Kintex UltraScale+ family of Xilinx FPGAs. FPGAs provide the necessary accuracy, resolution, and precision to run neural algorithms alongside current sensor technologies. The results offered in this paper demonstrate that this implementation provides accurate object tracking performance on several datasets, obtaining a high F-score value (0.86 for the most complex sequence used. Moreover, it outperforms implementations of a complete ALI algorithm and a simplified version of the ALI algorithm—named “accumulative computation”—which was run about ten years ago, now reaching real-time processing times that were simply not achievable at that time for ALI.
Yuldashev, M. N.; Vlasov, A. I.; Novikov, A. N.
2018-05-01
This paper focuses on the development of an energy-efficient algorithm for classification of states of a wireless sensor network using machine learning methods. The proposed algorithm reduces energy consumption by: 1) elimination of monitoring of parameters that do not affect the state of the sensor network, 2) reduction of communication sessions over the network (the data are transmitted only if their values can affect the state of the sensor network). The studies of the proposed algorithm have shown that at classification accuracy close to 100%, the number of communication sessions can be reduced by 80%.
Ahmed, Yassmin Seid; Fox-Rabinovich, German; Paiva, Jose Mario; Wagg, Terry; Veldhuis, Stephen Clarence
2017-10-25
During machining of stainless steels at low cutting -speeds, workpiece material tends to adhere to the cutting tool at the tool-chip interface, forming built-up edge (BUE). BUE has a great importance in machining processes; it can significantly modify the phenomenon in the cutting zone, directly affecting the workpiece surface integrity, cutting tool forces, and chip formation. The American Iron and Steel Institute (AISI) 304 stainless steel has a high tendency to form an unstable BUE, leading to deterioration of the surface quality. Therefore, it is necessary to understand the nature of the surface integrity induced during machining operations. Although many reports have been published on the effect of tool wear during machining of AISI 304 stainless steel on surface integrity, studies on the influence of the BUE phenomenon in the stable state of wear have not been investigated so far. The main goal of the present work is to investigate the close link between the BUE formation, surface integrity and cutting forces in the stable sate of wear for uncoated cutting tool during the cutting tests of AISI 304 stainless steel. The cutting parameters were chosen to induce BUE formation during machining. X-ray diffraction (XRD) method was used for measuring superficial residual stresses of the machined surface through the stable state of wear in the cutting and feed directions. In addition, surface roughness of the machined surface was investigated using the Alicona microscope and Scanning Electron Microscopy (SEM) was used to reveal the surface distortions created during the cutting process, combined with chip undersurface analyses. The investigated BUE formation during the stable state of wear showed that the BUE can cause a significant improvement in the surface integrity and cutting forces. Moreover, it can be used to compensate for tool wear through changing the tool geometry, leading to the protection of the cutting tool from wear.
Fast implementation of the 1\\rightarrow3 orbital state quantum cloning machine
Lin, Jin-Zhong
2018-05-01
We present a scheme to implement a 1→3 orbital state quantum cloning machine assisted by quantum Zeno dynamics. By constructing shortcuts to adiabatic passage with transitionless quantum driving, we can complete this scheme effectively and quickly in one step. The effects of decoherence, including spontaneous emission and the decay of the cavity, are also discussed. The numerical simulation results show that high fidelity can be obtained and the feasibility analysis indicates that this can also be realized in experiments.
Approximate multi-state reliability expressions using a new machine learning technique
International Nuclear Information System (INIS)
Rocco S, Claudio M.; Muselli, Marco
2005-01-01
The machine-learning-based methodology, previously proposed by the authors for approximating binary reliability expressions, is now extended to develop a new algorithm, based on the procedure of Hamming Clustering, which is capable to deal with multi-state systems and any success criterion. The proposed technique is presented in details and verified on literature cases: experiment results show that the new algorithm yields excellent predictions
Parallel algorithms for testing finite state machines:Generating UIO sequences
Hierons, RM; Turker, UC
2016-01-01
This paper describes an efficient parallel algorithm that uses many-core GPUs for automatically deriving Unique Input Output sequences (UIOs) from Finite State Machines. The proposed algorithm uses the global scope of the GPU's global memory through coalesced memory access and minimises the transfer between CPU and GPU memory. The results of experiments indicate that the proposed method yields considerably better results compared to a single core UIO construction algorithm. Our algorithm is s...
Diagnostics of the Technical State of Bearings of Mining Machines Base Assemblies
Gerike, Boris L.; Mokrushev, Andrey A.
2017-10-01
The article reviews the methods of technical diagnostics of equipment used during maintenance of mining machines in accordance with their actual technical state, and considers the basics of vibration parameters measuring. The classification of existing methods for diagnosing the technical condition of rolling bearings is given. The advantages and disadvantages of these methods are considered. The main defects of rolling bearings arising during manufacturing, transportation, storage, and operation are considered.
Distributed state machine supervision for long-baseline gravitational-wave detectors
International Nuclear Information System (INIS)
Rollins, Jameson Graef
2016-01-01
The Laser Interferometer Gravitational-wave Observatory (LIGO) consists of two identical yet independent, widely separated, long-baseline gravitational-wave detectors. Each Advanced LIGO detector consists of complex optical-mechanical systems isolated from the ground by multiple layers of active seismic isolation, all controlled by hundreds of fast, digital, feedback control systems. This article describes a novel state machine-based automation platform developed to handle the automation and supervisory control challenges of these detectors. The platform, called Guardian, consists of distributed, independent, state machine automaton nodes organized hierarchically for full detector control. User code is written in standard Python and the platform is designed to facilitate the fast-paced development process associated with commissioning the complicated Advanced LIGO instruments. While developed specifically for the Advanced LIGO detectors, Guardian is a generic state machine automation platform that is useful for experimental control at all levels, from simple table-top setups to large-scale multi-million dollar facilities.
Distributed state machine supervision for long-baseline gravitational-wave detectors
Energy Technology Data Exchange (ETDEWEB)
Rollins, Jameson Graef, E-mail: jameson.rollins@ligo.org [LIGO Laboratory, California Institute of Technology, Pasadena, California 91125 (United States)
2016-09-15
The Laser Interferometer Gravitational-wave Observatory (LIGO) consists of two identical yet independent, widely separated, long-baseline gravitational-wave detectors. Each Advanced LIGO detector consists of complex optical-mechanical systems isolated from the ground by multiple layers of active seismic isolation, all controlled by hundreds of fast, digital, feedback control systems. This article describes a novel state machine-based automation platform developed to handle the automation and supervisory control challenges of these detectors. The platform, called Guardian, consists of distributed, independent, state machine automaton nodes organized hierarchically for full detector control. User code is written in standard Python and the platform is designed to facilitate the fast-paced development process associated with commissioning the complicated Advanced LIGO instruments. While developed specifically for the Advanced LIGO detectors, Guardian is a generic state machine automation platform that is useful for experimental control at all levels, from simple table-top setups to large-scale multi-million dollar facilities.
Scarnati, James T.; Tice, Craig J.
1992-01-01
Describes how students can make and use Hooey Machines to learn how mechanical energy can be transferred from one object to another within a system. The Hooey Machine is made using a pencil, eight thumbtacks, one pushpin, tape, scissors, graph paper, and a plastic lid. (PR)
Quantum walk on a chimera graph
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.
On a conjecture concerning helly circle graphs
Directory of Open Access Journals (Sweden)
Durán Guillermo
2003-01-01
Full Text Available We say that G is an e-circle graph if there is a bijection between its vertices and straight lines on the cartesian plane such that two vertices are adjacent in G if and only if the corresponding lines intersect inside the circle of radius one. This definition suggests a method for deciding whether a given graph G is an e-circle graph, by constructing a convenient system S of equations and inequations which represents the structure of G, in such a way that G is an e-circle graph if and only if S has a solution. In fact, e-circle graphs are exactly the circle graphs (intersection graphs of chords in a circle, and thus this method provides an analytic way for recognizing circle graphs. A graph G is a Helly circle graph if G is a circle graph and there exists a model of G by chords such that every three pairwise intersecting chords intersect at the same point. A conjecture by Durán (2000 states that G is a Helly circle graph if and only if G is a circle graph and contains no induced diamonds (a diamond is a graph formed by four vertices and five edges. Many unsuccessful efforts - mainly based on combinatorial and geometrical approaches - have been done in order to validate this conjecture. In this work, we utilize the ideas behind the definition of e-circle graphs and restate this conjecture in terms of an equivalence between two systems of equations and inequations, providing a new, analytic tool to deal with it.
Mutual proximity graphs for improved reachability in music recommendation.
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.
Estimation of the Dynamic States of Synchronous Machines Using an Extended Particle Filter
Energy Technology Data Exchange (ETDEWEB)
Zhou, Ning; Meng, Da; Lu, Shuai
2013-11-11
In this paper, an extended particle filter (PF) is proposed to estimate the dynamic states of a synchronous machine using phasor measurement unit (PMU) data. A PF propagates the mean and covariance of states via Monte Carlo simulation, is easy to implement, and can be directly applied to a non-linear system with non-Gaussian noise. The extended PF modifies a basic PF to improve robustness. Using Monte Carlo simulations with practical noise and model uncertainty considerations, the extended PF’s performance is evaluated and compared with the basic PF and an extended Kalman filter (EKF). The extended PF results showed high accuracy and robustness against measurement and model noise.
Chromatic polynomials of random graphs
International Nuclear Information System (INIS)
Van Bussel, Frank; Fliegner, Denny; Timme, Marc; Ehrlich, Christoph; Stolzenberg, Sebastian
2010-01-01
Chromatic polynomials and related graph invariants are central objects in both graph theory and statistical physics. Computational difficulties, however, have so far restricted studies of such polynomials to graphs that were either very small, very sparse or highly structured. Recent algorithmic advances (Timme et al 2009 New J. Phys. 11 023001) now make it possible to compute chromatic polynomials for moderately sized graphs of arbitrary structure and number of edges. Here we present chromatic polynomials of ensembles of random graphs with up to 30 vertices, over the entire range of edge density. We specifically focus on the locations of the zeros of the polynomial in the complex plane. The results indicate that the chromatic zeros of random graphs have a very consistent layout. In particular, the crossing point, the point at which the chromatic zeros with non-zero imaginary part approach the real axis, scales linearly with the average degree over most of the density range. While the scaling laws obtained are purely empirical, if they continue to hold in general there are significant implications: the crossing points of chromatic zeros in the thermodynamic limit separate systems with zero ground state entropy from systems with positive ground state entropy, the latter an exception to the third law of thermodynamics.
Towards an automatic model transformation mechanism from UML state machines to DEVS models
Directory of Open Access Journals (Sweden)
Ariel González
2015-08-01
Full Text Available The development of complex event-driven systems requires studies and analysis prior to deployment with the goal of detecting unwanted behavior. UML is a language widely used by the software engineering community for modeling these systems through state machines, among other mechanisms. Currently, these models do not have appropriate execution and simulation tools to analyze the real behavior of systems. Existing tools do not provide appropriate libraries (sampling from a probability distribution, plotting, etc. both to build and to analyze models. Modeling and simulation for design and prototyping of systems are widely used techniques to predict, investigate and compare the performance of systems. In particular, the Discrete Event System Specification (DEVS formalism separates the modeling and simulation; there are several tools available on the market that run and collect information from DEVS models. This paper proposes a model transformation mechanism from UML state machines to DEVS models in the Model-Driven Development (MDD context, through the declarative QVT Relations language, in order to perform simulations using tools, such as PowerDEVS. A mechanism to validate the transformation is proposed. Moreover, examples of application to analyze the behavior of an automatic banking machine and a control system of an elevator are presented.
Graph visualization (Invited talk)
Wijk, van J.J.; Kreveld, van M.J.; Speckmann, B.
2012-01-01
Black and white node link diagrams are the classic method to depict graphs, but these often fall short to give insight in large graphs or when attributes of nodes and edges play an important role. Graph visualization aims obtaining insight in such graphs using interactive graphical representations.
Analysis of the steady-state operation of vacuum systems for fusion machines
International Nuclear Information System (INIS)
Roose, T.R.; Hoffman, M.A.; Carlson, G.A.
1975-01-01
A computer code named GASBAL was written to calculate the steady-state vacuum system performance of multi-chamber mirror machines as well as rather complex conventional multichamber vacuum systems. Application of the code, with some modifications, to the quasi-steady tokamak operating period should also be possible. Basically, GASBAL analyzes free molecular gas flow in a system consisting of a central chamber (the plasma chamber) connected by conductances to an arbitrary number of one- or two-chamber peripheral tanks. Each of the peripheral tanks may have vacuum pumping capability (pumping speed), sources of cold gas, and sources of energetic atoms. The central chamber may have actual vacuum pumping capability, as well as a plasma capable of ionizing injected atoms and impinging gas molecules and ''pumping'' them to a peripheral chamber. The GASBAL code was used in the preliminary design of a large mirror machine experiment--LLL's MX
A generic finite state machine framework for the ACNET control system
International Nuclear Information System (INIS)
Carmichael, L.; Warner, A.
2009-01-01
A significant level of automation and flexibility has been added to the ACNET control system through the development of a Java-based Finite State Machine (FSM) infrastructure. These FSMs are integrated into ACNET and allow users to easily build, test and execute scripts that have full access to ACNET's functionality. In this paper, a description will be given of the FSM design and its ties to the Java-based Data Acquisition Engine (DAE) framework. Each FSM is part of a client-server model with FSM display clients using Remote Method Invocation (RMI) to communicate with DAE servers heavily coupled to ACNET. A web-based monitoring system that allows users to utilize browsers to observe persistent FSMs will also be discussed. Finally, some key implementations such as the crash recovery FSM developed for the Electron Cooling machine protection system will be presented.
Some remarks on definability of process graphs
Grabmayer, C.A.; Klop, J.W.; Luttik, B.; Baier, C.; Hermanns, H.
2006-01-01
We propose the notions of "density" and "connectivity" of infinite process graphs and investigate them in the context of the wellknown process algebras BPA and BPP. For a process graph G, the density function in a state s maps a natural number n to the number of states of G with distance less or
Pragmatic Graph Rewriting Modifications
Rodgers, Peter; Vidal, Natalia
1999-01-01
We present new pragmatic constructs for easing programming in visual graph rewriting programming languages. The first is a modification to the rewriting process for nodes the host graph, where nodes specified as 'Once Only' in the LHS of a rewrite match at most once with a corresponding node in the host graph. This reduces the previously common use of tags to indicate the progress of matching in the graph. The second modification controls the application of LHS graphs, where those specified a...
Algorithm for determining two-periodic steady-states in AC machines directly in time domain
Directory of Open Access Journals (Sweden)
Sobczyk Tadeusz J.
2016-09-01
Full Text Available This paper describes an algorithm for finding steady states in AC machines for the cases of their two-periodic nature. The algorithm enables to specify the steady-state solution identified directly in time domain despite of the fact that two-periodic waveforms are not repeated in any finite time interval. The basis for such an algorithm is a discrete differential operator that specifies the temporary values of the derivative of the two-periodic function in the selected set of points on the basis of the values of that function in the same set of points. It allows to develop algebraic equations defining the steady state solution reached in a chosen point set for the nonlinear differential equations describing the AC machines when electrical and mechanical equations should be solved together. That set of those values allows determining the steady state solution at any time instant up to infinity. The algorithm described in this paper is competitive with respect to the one known in literature an approach based on the harmonic balance method operated in frequency domain.
Directory of Open Access Journals (Sweden)
Shahin Mehdipour Ataee
2018-01-01
Full Text Available We identify significant weaknesses in the original Abstract State Machine (ASM based choreography algorithm of Web Service Modeling Ontology (WSMO, which make it impractical for use in semantic web service choreography engines. We present an improved algorithm which rectifies the weaknesses of the original algorithm, as well as a practical, fully functional choreography engine implementation in Flora-2 based on the improved algorithm. Our improvements to the choreography algorithm include (i the linking of the initial state of the ASM to the precondition of the goal, (ii the introduction of the concept of a final state in the execution of the ASM and its linking to the postcondition of the goal, and (iii modification to the execution of the ASM so that it stops when the final state condition is satisfied by the current configuration of the machine. Our choreography engine takes as input semantic web service specifications written in the Flora-2 dialect of F-logic. Furthermore, we prove the equivalence of ASMs (evolving algebras and evolving ontologies in the sense that one can simulate the other, a first in literature. Finally, we present a visual editor which facilitates the design and deployment of our F-logic based web service and goal specifications.
Quick Mining of Isomorphic Exact Large Patterns from Large Graphs
Almasri, Islam
2014-12-01
The applications of the sub graph isomorphism search are growing with the growing number of areas that model their systems using graphs or networks. Specifically, many biological systems, such as protein interaction networks, molecular structures and protein contact maps, are modeled as graphs. The sub graph isomorphism search is concerned with finding all sub graphs that are isomorphic to a relevant query graph, the existence of such sub graphs can reflect on the characteristics of the modeled system. The most computationally expensive step in the search for isomorphic sub graphs is the backtracking algorithm that traverses the nodes of the target graph. In this paper, we propose a pruning approach that is inspired by the minimum remaining value heuristic that achieves greater scalability over large query and target graphs. Our testing on various biological networks shows that performance enhancement of our approach over existing state-of-the-art approaches varies between 6x and 53x. © 2014 IEEE.
Quick Mining of Isomorphic Exact Large Patterns from Large Graphs
Almasri, Islam; Gao, Xin; Fedoroff, Nina V.
2014-01-01
The applications of the sub graph isomorphism search are growing with the growing number of areas that model their systems using graphs or networks. Specifically, many biological systems, such as protein interaction networks, molecular structures and protein contact maps, are modeled as graphs. The sub graph isomorphism search is concerned with finding all sub graphs that are isomorphic to a relevant query graph, the existence of such sub graphs can reflect on the characteristics of the modeled system. The most computationally expensive step in the search for isomorphic sub graphs is the backtracking algorithm that traverses the nodes of the target graph. In this paper, we propose a pruning approach that is inspired by the minimum remaining value heuristic that achieves greater scalability over large query and target graphs. Our testing on various biological networks shows that performance enhancement of our approach over existing state-of-the-art approaches varies between 6x and 53x. © 2014 IEEE.
International Nuclear Information System (INIS)
Rosmanis, Ansis
2011-01-01
I introduce a continuous-time quantum walk on graphs called the quantum snake walk, the basis states of which are fixed-length paths (snakes) in the underlying graph. First, I analyze the quantum snake walk on the line, and I show that, even though most states stay localized throughout the evolution, there are specific states that most likely move on the line as wave packets with momentum inversely proportional to the length of the snake. Next, I discuss how an algorithm based on the quantum snake walk might potentially be able to solve an extended version of the glued trees problem, which asks to find a path connecting both roots of the glued trees graph. To the best of my knowledge, no efficient quantum algorithm solving this problem is known yet.
Lin, Y.; Zhang, W. J.
2005-02-01
This paper presents an approach to human-machine interface design for control room operators of nuclear power plants. The first step in designing an interface for a particular application is to determine information content that needs to be displayed. The design methodology for this step is called the interface design framework (called framework ). Several frameworks have been proposed for applications at varying levels, including process plants. However, none is based on the design and manufacture of a plant system for which the interface is designed. This paper presents an interface design framework which originates from design theory and methodology for general technical systems. Specifically, the framework is based on a set of core concepts of a function-behavior-state model originally proposed by the artificial intelligence research community and widely applied in the design research community. Benefits of this new framework include the provision of a model-based fault diagnosis facility, and the seamless integration of the design (manufacture, maintenance) of plants and the design of human-machine interfaces. The missing linkage between design and operation of a plant was one of the causes of the Three Mile Island nuclear reactor incident. A simulated plant system is presented to explain how to apply this framework in designing an interface. The resulting human-machine interface is discussed; specifically, several fault diagnosis examples are elaborated to demonstrate how this interface could support operators' fault diagnosis in an unanticipated situation.
An Elgamal Encryption Scheme of Fibonacci Q-Matrix and Finite State Machine
Directory of Open Access Journals (Sweden)
B. Ravi Kumar
2015-12-01
Full Text Available Cryptography is the science of writing messages in unknown form using mathematical models. In Cryptography, several ciphers were introduced for the encryption schemes. Recent research focusing on designing various mathematical models in such a way that tracing the inverse of the designed mathematical models is infeasible for the eve droppers. In the present work, the ELGamal encryption scheme is executed using the generator of a cyclic group formed by the points on choosing elliptic curve, finite state machines and key matrices obtained from the Fibonacci sequences.
Bapat, Ravindra B
2014-01-01
This new edition illustrates the power of linear algebra in the study of graphs. The emphasis on matrix techniques is greater than in other texts on algebraic graph theory. Important matrices associated with graphs (for example, incidence, adjacency and Laplacian matrices) are treated in detail. Presenting a useful overview of selected topics in algebraic graph theory, early chapters of the text focus on regular graphs, algebraic connectivity, the distance matrix of a tree, and its generalized version for arbitrary graphs, known as the resistance matrix. Coverage of later topics include Laplacian eigenvalues of threshold graphs, the positive definite completion problem and matrix games based on a graph. Such an extensive coverage of the subject area provides a welcome prompt for further exploration. The inclusion of exercises enables practical learning throughout the book. In the new edition, a new chapter is added on the line graph of a tree, while some results in Chapter 6 on Perron-Frobenius theory are reo...
Directory of Open Access Journals (Sweden)
Wi Hoon eJung
2013-10-01
Full Text Available One major characteristic of experts is intuitive judgment, which is an automatic process whereby patterns stored in memory through long-term training are recognized. Indeed, long-term training may influence brain structure and function. A recent study revealed that chess experts at rest showed differences in structure and functional connectivity (FC in the head of caudate, which is associated with rapid best next-move generation. However, less is known about the structure and function of the brains of Baduk experts compared with those of experts in other strategy games. Therefore, we performed voxel-based morphometry and FC analyses in Baduk experts to investigate structural brain differences and to clarify the influence of these differences on functional interactions. We also conducted graph theoretical analysis to explore the topological organization of whole-brain functional networks. Compared to novices, Baduk experts exhibited decreased and increased gray matter volume in the amygdala and nucleus accumbens, respectively. We also found increased FC between the amygdala and medial orbitofrontal cortex and decreased FC between the nucleus accumbens and medial prefrontal cortex. Further graph theoretical analysis revealed differences in measures of the integration of the network and in the regional nodal characteristics of various brain regions activated during Baduk. This study provides evidence for structural and functional differences as well as altered topological organization of the whole-brain functional networks in Baduk experts. Our findings also offer novel suggestions about the cognitive mechanisms behind Baduk expertise, which involves intuitive decision-making mediated by somatic marker circuitry and visuospatial processing.
Adaptive Graph Convolutional Neural Networks
Li, Ruoyu; Wang, Sheng; Zhu, Feiyun; Huang, Junzhou
2018-01-01
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for eac...
Directory of Open Access Journals (Sweden)
C. Dalfo
2015-10-01
Full Text Available We study a family of graphs related to the $n$-cube. The middle cube graph of parameter k is the subgraph of $Q_{2k-1}$ induced by the set of vertices whose binary representation has either $k-1$ or $k$ number of ones. The middle cube graphs can be obtained from the well-known odd graphs by doubling their vertex set. Here we study some of the properties of the middle cube graphs in the light of the theory of distance-regular graphs. In particular, we completely determine their spectra (eigenvalues and their multiplicities, and associated eigenvectors.
Directory of Open Access Journals (Sweden)
Ying Zhang
2018-01-01
Full Text Available With the development of autonomous unmanned intelligent systems, such as the unmanned boats, unmanned planes and autonomous underwater vehicles, studies on Wireless Sensor-Actor Networks (WSANs have attracted more attention. Network connectivity algorithms play an important role in data exchange, collaborative detection and information fusion. Due to the harsh application environment, abnormal nodes often appear, and the network connectivity will be prone to be lost. Network self-healing mechanisms have become critical for these systems. In order to decrease the movement overhead of the sensor-actor nodes, an autonomous connectivity restoration algorithm based on finite state machine is proposed. The idea is to identify whether a node is a critical node by using a finite state machine, and update the connected dominating set in a timely way. If an abnormal node is a critical node, the nearest non-critical node will be relocated to replace the abnormal node. In the case of multiple node abnormality, a regional network restoration algorithm is introduced. It is designed to reduce the overhead of node movements while restoration happens. Simulation results indicate the proposed algorithm has better performance on the total moving distance and the number of total relocated nodes compared with some other representative restoration algorithms.
Zhang, Ying; Wang, Jun; Hao, Guan
2018-01-08
With the development of autonomous unmanned intelligent systems, such as the unmanned boats, unmanned planes and autonomous underwater vehicles, studies on Wireless Sensor-Actor Networks (WSANs) have attracted more attention. Network connectivity algorithms play an important role in data exchange, collaborative detection and information fusion. Due to the harsh application environment, abnormal nodes often appear, and the network connectivity will be prone to be lost. Network self-healing mechanisms have become critical for these systems. In order to decrease the movement overhead of the sensor-actor nodes, an autonomous connectivity restoration algorithm based on finite state machine is proposed. The idea is to identify whether a node is a critical node by using a finite state machine, and update the connected dominating set in a timely way. If an abnormal node is a critical node, the nearest non-critical node will be relocated to replace the abnormal node. In the case of multiple node abnormality, a regional network restoration algorithm is introduced. It is designed to reduce the overhead of node movements while restoration happens. Simulation results indicate the proposed algorithm has better performance on the total moving distance and the number of total relocated nodes compared with some other representative restoration algorithms.
Zhang, Ying; Wang, Jun; Hao, Guan
2018-01-01
With the development of autonomous unmanned intelligent systems, such as the unmanned boats, unmanned planes and autonomous underwater vehicles, studies on Wireless Sensor-Actor Networks (WSANs) have attracted more attention. Network connectivity algorithms play an important role in data exchange, collaborative detection and information fusion. Due to the harsh application environment, abnormal nodes often appear, and the network connectivity will be prone to be lost. Network self-healing mechanisms have become critical for these systems. In order to decrease the movement overhead of the sensor-actor nodes, an autonomous connectivity restoration algorithm based on finite state machine is proposed. The idea is to identify whether a node is a critical node by using a finite state machine, and update the connected dominating set in a timely way. If an abnormal node is a critical node, the nearest non-critical node will be relocated to replace the abnormal node. In the case of multiple node abnormality, a regional network restoration algorithm is introduced. It is designed to reduce the overhead of node movements while restoration happens. Simulation results indicate the proposed algorithm has better performance on the total moving distance and the number of total relocated nodes compared with some other representative restoration algorithms. PMID:29316702
Wang, J; Hao, Z; Wang, H
2018-01-01
The human brain can be characterized as functional networks. Therefore, it is important to subdivide the brain appropriately in order to construct reliable networks. Resting-state functional connectivity-based parcellation is a commonly used technique to fulfill this goal. Here we propose a novel individual subject-level parcellation approach based on whole-brain resting-state functional magnetic resonance imaging (fMRI) data. We first used a supervoxel method known as simple linear iterative clustering directly on resting-state fMRI time series to generate supervoxels, and then combined similar supervoxels to generate clusters using a clustering method known as graph-without-cut (GWC). The GWC approach incorporates spatial information and multiple features of the supervoxels by energy minimization, simultaneously yielding an optimal graph and brain parcellation. Meanwhile, it theoretically guarantees that the actual cluster number is exactly equal to the initialized cluster number. By comparing the results of the GWC approach and those of the random GWC approach, we demonstrated that GWC does not rely heavily on spatial structures, thus avoiding the challenges encountered in some previous whole-brain parcellation approaches. In addition, by comparing the GWC approach to two competing approaches, we showed that GWC achieved better parcellation performances in terms of different evaluation metrics. The proposed approach can be used to generate individualized brain atlases for applications related to cognition, development, aging, disease, personalized medicine, etc. The major source codes of this study have been made publicly available at https://github.com/yuzhounh/GWC.
Directory of Open Access Journals (Sweden)
J. Wang
2018-05-01
Full Text Available The human brain can be characterized as functional networks. Therefore, it is important to subdivide the brain appropriately in order to construct reliable networks. Resting-state functional connectivity-based parcellation is a commonly used technique to fulfill this goal. Here we propose a novel individual subject-level parcellation approach based on whole-brain resting-state functional magnetic resonance imaging (fMRI data. We first used a supervoxel method known as simple linear iterative clustering directly on resting-state fMRI time series to generate supervoxels, and then combined similar supervoxels to generate clusters using a clustering method known as graph-without-cut (GWC. The GWC approach incorporates spatial information and multiple features of the supervoxels by energy minimization, simultaneously yielding an optimal graph and brain parcellation. Meanwhile, it theoretically guarantees that the actual cluster number is exactly equal to the initialized cluster number. By comparing the results of the GWC approach and those of the random GWC approach, we demonstrated that GWC does not rely heavily on spatial structures, thus avoiding the challenges encountered in some previous whole-brain parcellation approaches. In addition, by comparing the GWC approach to two competing approaches, we showed that GWC achieved better parcellation performances in terms of different evaluation metrics. The proposed approach can be used to generate individualized brain atlases for applications related to cognition, development, aging, disease, personalized medicine, etc. The major source codes of this study have been made publicly available at https://github.com/yuzhounh/GWC.
Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface
Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert
The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.
Indian Academy of Sciences (India)
1National Centre for Advanced Research in Discrete Mathematics ... 3Department of Computer Science, Ball State University, Muncie, IN, USA .... The corona of two disjoint graphs G1 and G2 is defined to be the graph G = G1 ◦ G2,.
Heat-machine control by quantum-state preparation: from quantum engines to refrigerators.
Gelbwaser-Klimovsky, D; Kurizki, G
2014-08-01
We explore the dependence of the performance bounds of heat engines and refrigerators on the initial quantum state and the subsequent evolution of their piston, modeled by a quantized harmonic oscillator. Our goal is to provide a fully quantized treatment of self-contained (autonomous) heat machines, as opposed to their prevailing semiclassical description that consists of a quantum system alternately coupled to a hot or a cold heat bath and parametrically driven by a classical time-dependent piston or field. Here, by contrast, there is no external time-dependent driving. Instead, the evolution is caused by the stationary simultaneous interaction of two heat baths (having distinct spectra and temperatures) with a single two-level system that is in turn coupled to the quantum piston. The fully quantized treatment we put forward allows us to investigate work extraction and refrigeration by the tools of quantum-optical amplifier and dissipation theory, particularly, by the analysis of amplified or dissipated phase-plane quasiprobability distributions. Our main insight is that quantum states may be thermodynamic resources and can provide a powerful handle, or control, on the efficiency of the heat machine. In particular, a piston initialized in a coherent state can cause the engine to produce work at an efficiency above the Carnot bound in the linear amplification regime. In the refrigeration regime, the coefficient of performance can transgress the Carnot bound if the piston is initialized in a Fock state. The piston may be realized by a vibrational mode, as in nanomechanical setups, or an electromagnetic field mode, as in cavity-based scenarios.
Automation of a universal machine
International Nuclear Information System (INIS)
Rodriguez S, J.
1997-01-01
The development of the hardware and software of a control system for a servo-hydraulic machine is presented. The universal machine is an Instron, model 1331, used to make mechanical tests. The software includes the acquisition of data from the measurements, processing and graphic presentation of the results in the assay of the 'tension' type. The control is based on a PPI (Programmable Peripheral Interface) 8255, in which the different states of the machine are set. The control functions of the machine are: a) Start of an assay, b) Pause in the assay, c) End of the assay, d) Choice of the control mode of the machine, that they could be in load, stroke or strain modes. For the data acquisition, a commercial card, National Products, model DAS-16, plugged in a slot of a Pc was used. Three transducers provide the analog signals, a cell of load, a LVDT and a extensometer. All the data are digitalized and handled in order to get the results in the appropriate working units. A stress-strain graph is obtained in the screen of the Pc for a tension test for a specific material. The points of maximum stress, rupture stress and the yield stress of the material under test are shown. (Author)
Using Canonical Forms for Isomorphism Reduction in Graph-based Model Checking
Kant, Gijs
Graph isomorphism checking can be used in graph-based model checking to achieve symmetry reduction. Instead of one-to-one comparing the graph representations of states, canonical forms of state graphs can be computed. These canonical forms can be used to store and compare states. However, computing
Survey of Approaches to Generate Realistic Synthetic Graphs
Energy Technology Data Exchange (ETDEWEB)
Lim, Seung-Hwan [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Lee, Sangkeun [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Powers, Sarah S [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Shankar, Mallikarjun [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Imam, Neena [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2016-10-01
A graph is a flexible data structure that can represent relationships between entities. As with other data analysis tasks, the use of realistic graphs is critical to obtaining valid research results. Unfortunately, using the actual ("real-world") graphs for research and new algorithm development is difficult due to the presence of sensitive information in the data or due to the scale of data. This results in practitioners developing algorithms and systems that employ synthetic graphs instead of real-world graphs. Generating realistic synthetic graphs that provide reliable statistical confidence to algorithmic analysis and system evaluation involves addressing technical hurdles in a broad set of areas. This report surveys the state of the art in approaches to generate realistic graphs that are derived from fitted graph models on real-world graphs.
Soetevent, A.R.
2010-01-01
This paper extends Hotelling's model of price competition with quadratic transportation costs from a line to graphs. I propose an algorithm to calculate firm-level demand for any given graph, conditional on prices and firm locations. One feature of graph models of price competition is that spatial
Graphing Inequalities, Connecting Meaning
Switzer, J. Matt
2014-01-01
Students often have difficulty with graphing inequalities (see Filloy, Rojano, and Rubio 2002; Drijvers 2002), and J. Matt Switzer's students were no exception. Although students can produce graphs for simple inequalities, they often struggle when the format of the inequality is unfamiliar. Even when producing a correct graph of an…
Directory of Open Access Journals (Sweden)
Amine Labriji
2017-07-01
Full Text Available The topic of identifying the similarity of graphs was considered as highly recommended research field in the Web semantic, artificial intelligence, the shape recognition and information research. One of the fundamental problems of graph databases is finding similar graphs to a graph query. Existing approaches dealing with this problem are usually based on the nodes and arcs of the two graphs, regardless of parental semantic links. For instance, a common connection is not identified as being part of the similarity of two graphs in cases like two graphs without common concepts, the measure of similarity based on the union of two graphs, or the one based on the notion of maximum common sub-graph (SCM, or the distance of edition of graphs. This leads to an inadequate situation in the context of information research. To overcome this problem, we suggest a new measure of similarity between graphs, based on the similarity measure of Wu and Palmer. We have shown that this new measure satisfies the properties of a measure of similarities and we applied this new measure on examples. The results show that our measure provides a run time with a gain of time compared to existing approaches. In addition, we compared the relevance of the similarity values obtained, it appears that this new graphs measure is advantageous and offers a contribution to solving the problem mentioned above.
van Dam, Edwin R.; Koolen, Jack H.; Tanaka, Hajime
2016-01-01
This is a survey of distance-regular graphs. We present an introduction to distance-regular graphs for the reader who is unfamiliar with the subject, and then give an overview of some developments in the area of distance-regular graphs since the monograph 'BCN'[Brouwer, A.E., Cohen, A.M., Neumaier,
Fuzzy Graph Language Recognizability
Kalampakas , Antonios; Spartalis , Stefanos; Iliadis , Lazaros
2012-01-01
Part 5: Fuzzy Logic; International audience; Fuzzy graph language recognizability is introduced along the lines of the established theory of syntactic graph language recognizability by virtue of the algebraic structure of magmoids. The main closure properties of the corresponding class are investigated and several interesting examples of fuzzy graph languages are examined.
Brouwer, A.E.; Haemers, W.H.
2012-01-01
This book gives an elementary treatment of the basic material about graph spectra, both for ordinary, and Laplace and Seidel spectra. The text progresses systematically, by covering standard topics before presenting some new material on trees, strongly regular graphs, two-graphs, association
Graph topologies on closed multifunctions
Directory of Open Access Journals (Sweden)
Giuseppe Di Maio
2003-10-01
Full Text Available In this paper we study function space topologies on closed multifunctions, i.e. closed relations on X x Y using various hypertopologies. The hypertopologies are in essence, graph topologies i.e topologies on functions considered as graphs which are subsets of X x Y . We also study several topologies, including one that is derived from the Attouch-Wets filter on the range. We state embedding theorems which enable us to generalize and prove some recent results in the literature with the use of known results in the hyperspace of the range space and in the function space topologies of ordinary functions.
Hell, Pavol
2004-01-01
This is a book about graph homomorphisms. Graph theory is now an established discipline but the study of graph homomorphisms has only recently begun to gain wide acceptance and interest. The subject gives a useful perspective in areas such as graph reconstruction, products, fractional and circular colourings, and has applications in complexity theory, artificial intelligence, telecommunication, and, most recently, statistical physics.Based on the authors' lecture notes for graduate courses, this book can be used as a textbook for a second course in graph theory at 4th year or master's level an
Tracking an open quantum system using a finite state machine: Stability analysis
International Nuclear Information System (INIS)
Karasik, R. I.; Wiseman, H. M.
2011-01-01
A finite-dimensional Markovian open quantum system will undergo quantum jumps between pure states, if we can monitor the bath to which it is coupled with sufficient precision. In general these jumps, plus the between-jump evolution, create a trajectory which passes through infinitely many different pure states, even for ergodic systems. However, as shown recently by us [Phys. Rev. Lett. 106, 020406 (2011)], it is possible to construct adaptive monitorings which restrict the system to jumping between a finite number of states. That is, it is possible to track the system using a finite state machine as the apparatus. In this paper we consider the question of the stability of these monitoring schemes. Restricting to cyclic jumps for a qubit, we give a strong analytical argument that these schemes are always stable and supporting analytical and numerical evidence for the example of resonance fluorescence. This example also enables us to explore a range of behaviors in the evolution of individual trajectories, for several different monitoring schemes.
High-Density Liquid-State Machine Circuitry for Time-Series Forecasting.
Rosselló, Josep L; Alomar, Miquel L; Morro, Antoni; Oliver, Antoni; Canals, Vincent
2016-08-01
Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the intrinsic parallelism of these computing systems are more efficiently exploited. Liquid state machines (LSM) have arisen as a strategic technique to implement recurrent designs of SNN with a simple learning methodology. In this work, we show a new low-cost methodology to implement high-density LSM by using Boolean gates. The proposed method is based on the use of probabilistic computing concepts to reduce hardware requirements, thus considerably increasing the neuron count per chip. The result is a highly functional system that is applied to high-speed time series forecasting.
Classification of fMRI resting-state maps using machine learning techniques: A comparative study
Gallos, Ioannis; Siettos, Constantinos
2017-11-01
We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.
Directory of Open Access Journals (Sweden)
Héctor Herrero
2017-05-01
Full Text Available This paper presents a state machine-based architecture, which enhances the flexibility and reusability of industrial robots, more concretely dual-arm multisensor robots. The proposed architecture, in addition to allowing absolute control of the execution, eases the programming of new applications by increasing the reusability of the developed modules. Through an easy-to-use graphical user interface, operators are able to create, modify, reuse and maintain industrial processes, increasing the flexibility of the cell. Moreover, the proposed approach is applied in a real use case in order to demonstrate its capabilities and feasibility in industrial environments. A comparative analysis is presented for evaluating the presented approach versus traditional robot programming techniques.
Simplicial complexes of graphs
Jonsson, Jakob
2008-01-01
A graph complex is a finite family of graphs closed under deletion of edges. Graph complexes show up naturally in many different areas of mathematics, including commutative algebra, geometry, and knot theory. Identifying each graph with its edge set, one may view a graph complex as a simplicial complex and hence interpret it as a geometric object. This volume examines topological properties of graph complexes, focusing on homotopy type and homology. Many of the proofs are based on Robin Forman's discrete version of Morse theory. As a byproduct, this volume also provides a loosely defined toolbox for attacking problems in topological combinatorics via discrete Morse theory. In terms of simplicity and power, arguably the most efficient tool is Forman's divide and conquer approach via decision trees; it is successfully applied to a large number of graph and digraph complexes.
Introduction to quantum graphs
Berkolaiko, Gregory
2012-01-01
A "quantum graph" is a graph considered as a one-dimensional complex and equipped with a differential operator ("Hamiltonian"). Quantum graphs arise naturally as simplified models in mathematics, physics, chemistry, and engineering when one considers propagation of waves of various nature through a quasi-one-dimensional (e.g., "meso-" or "nano-scale") system that looks like a thin neighborhood of a graph. Works that currently would be classified as discussing quantum graphs have been appearing since at least the 1930s, and since then, quantum graphs techniques have been applied successfully in various areas of mathematical physics, mathematics in general and its applications. One can mention, for instance, dynamical systems theory, control theory, quantum chaos, Anderson localization, microelectronics, photonic crystals, physical chemistry, nano-sciences, superconductivity theory, etc. Quantum graphs present many non-trivial mathematical challenges, which makes them dear to a mathematician's heart. Work on qu...
Zhang, Yong; Li, Peng; Jin, Yingyezhe; Choe, Yoonsuck
2015-11-01
This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel VLSI implementation. We use subsets of the TI46 speech corpus to benchmark the bioinspired digital LSM. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks.
Exploiting graph kernels for high performance biomedical relation extraction.
Panyam, Nagesh C; Verspoor, Karin; Cohn, Trevor; Ramamohanarao, Kotagiri
2018-01-30
Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence. In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences. Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM
Wang, Jin-Hui; Zuo, Xi-Nian; Gohel, Suril; Milham, Michael P.; Biswal, Bharat B.; He, Yong
2011-01-01
Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest. PMID:21818285
Statistical and machine learning approaches for network analysis
Dehmer, Matthias
2012-01-01
Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation
Integer Flows and Circuit Covers of Graphs and Signed Graphs
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
Graphing trillions of triangles.
Burkhardt, Paul
2017-07-01
The increasing size of Big Data is often heralded but how data are transformed and represented is also profoundly important to knowledge discovery, and this is exemplified in Big Graph analytics. Much attention has been placed on the scale of the input graph but the product of a graph algorithm can be many times larger than the input. This is true for many graph problems, such as listing all triangles in a graph. Enabling scalable graph exploration for Big Graphs requires new approaches to algorithms, architectures, and visual analytics. A brief tutorial is given to aid the argument for thoughtful representation of data in the context of graph analysis. Then a new algebraic method to reduce the arithmetic operations in counting and listing triangles in graphs is introduced. Additionally, a scalable triangle listing algorithm in the MapReduce model will be presented followed by a description of the experiments with that algorithm that led to the current largest and fastest triangle listing benchmarks to date. Finally, a method for identifying triangles in new visual graph exploration technologies is proposed.
Directory of Open Access Journals (Sweden)
Gan Zhang
2015-11-01
Full Text Available Since the air-gap field of flux-switching permanent magnet (FSPM machines is difficult to regulate as it is produced by the stator-magnets alone, a type of hybrid-excited flux-switching (HEFS machine is obtained by reducing the magnet length of an original FSPM machine and introducing a set of field windings into the saved space. In this paper, the steady-state characteristics, especially for the loaded performances of four prototyped HEFS machines, namely, PM-top, PM-middle-1, PM-middle-2, and PM-bottom, are comprehensively compared and evaluated based on both 2D and 3D finite element analysis. Also, the influences of PM materials including ferrite and NdFeB, respectively, on the characteristics of HEFS machines are covered. Particularly, the impacts of magnet movement in the corresponding slot on flux-regulating performances are studied in depth. The best overall performances employing NdFeB can be obtained when magnets are located near the air-gap. The FEA predictions are validated by experimental measurements on corresponding machine prototypes.
Pixels to Graphs by Associative Embedding
Newell, Alejandro
2017-06-22
Graphs are a useful abstraction of image content. Not only can graphs represent details about individual objects in a scene but they can capture the interactions between pairs of objects. We present a method for training a convolutional neural network such that it takes in an input image and produces a full graph. This is done end-to-end in a single stage with the use of associative embeddings. The network learns to simultaneously identify all of the elements that make up a graph and piece them together. We benchmark on the Visual Genome dataset, and report a Recall@50 of 9.7% compared to the prior state-of-the-art at 3.4%, a nearly threefold improvement on the challenging task of scene graph generation.
Chartrand, Gary
1984-01-01
Graph theory is used today in the physical sciences, social sciences, computer science, and other areas. Introductory Graph Theory presents a nontechnical introduction to this exciting field in a clear, lively, and informative style. Author Gary Chartrand covers the important elementary topics of graph theory and its applications. In addition, he presents a large variety of proofs designed to strengthen mathematical techniques and offers challenging opportunities to have fun with mathematics. Ten major topics - profusely illustrated - include: Mathematical Models, Elementary Concepts of Grap
Adriaan R. Soetevent
2010-01-01
This paper extends Hotelling's model of price competition with quadratic transportation costs from a line to graphs. I propose an algorithm to calculate firm-level demand for any given graph, conditional on prices and firm locations. One feature of graph models of price competition is that spatial discontinuities in firm-level demand may occur. I show that the existence result of D'Aspremont et al. (1979) does not extend to simple star graphs. I conjecture that this non-existence result holds...
Pim Heijnen; Adriaan Soetevent
2014-01-01
This paper extends Hotelling's model of price competition with quadratic transportation costs from a line to graphs. We derive an algorithm to calculate firm-level demand for any given graph, conditional on prices and firm locations. These graph models of price competition may lead to spatial discontinuities in firm-level demand. We show that the existence result of D'Aspremont et al. (1979) does not extend to simple star graphs and conjecture that this non-existence result holds more general...
Directory of Open Access Journals (Sweden)
Aleks Kissinger
2014-03-01
Full Text Available String diagrams are a powerful tool for reasoning about physical processes, logic circuits, tensor networks, and many other compositional structures. Dixon, Duncan and Kissinger introduced string graphs, which are a combinatoric representations of string diagrams, amenable to automated reasoning about diagrammatic theories via graph rewrite systems. In this extended abstract, we show how the power of such rewrite systems can be greatly extended by introducing pattern graphs, which provide a means of expressing infinite families of rewrite rules where certain marked subgraphs, called !-boxes ("bang boxes", on both sides of a rule can be copied any number of times or removed. After reviewing the string graph formalism, we show how string graphs can be extended to pattern graphs and how pattern graphs and pattern rewrite rules can be instantiated to concrete string graphs and rewrite rules. We then provide examples demonstrating the expressive power of pattern graphs and how they can be applied to study interacting algebraic structures that are central to categorical quantum mechanics.
Gelfand, I M; Shnol, E E
1969-01-01
The second in a series of systematic studies by a celebrated mathematician I. M. Gelfand and colleagues, this volume presents students with a well-illustrated sequence of problems and exercises designed to illuminate the properties of functions and graphs. Since readers do not have the benefit of a blackboard on which a teacher constructs a graph, the authors abandoned the customary use of diagrams in which only the final form of the graph appears; instead, the book's margins feature step-by-step diagrams for the complete construction of each graph. The first part of the book employs simple fu
Creating more effective graphs
Robbins, Naomi B
2012-01-01
A succinct and highly readable guide to creating effective graphs The right graph can be a powerful tool for communicating information, improving a presentation, or conveying your point in print. If your professional endeavors call for you to present data graphically, here's a book that can help you do it more effectively. Creating More Effective Graphs gives you the basic knowledge and techniques required to choose and create appropriate graphs for a broad range of applications. Using real-world examples everyone can relate to, the author draws on her years of experience in gr
Energy Technology Data Exchange (ETDEWEB)
Lothian, Joshua [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Powers, Sarah S. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Sullivan, Blair D. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Baker, Matthew B. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Schrock, Jonathan [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Poole, Stephen W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2013-10-01
The benchmarking effort within the Extreme Scale Systems Center at Oak Ridge National Laboratory seeks to provide High Performance Computing benchmarks and test suites of interest to the DoD sponsor. The work described in this report is a part of the effort focusing on graph generation. A previously developed benchmark, SystemBurn, allowed the emulation of different application behavior profiles within a single framework. To complement this effort, similar capabilities are desired for graph-centric problems. This report examines existing synthetic graph generator implementations in preparation for further study on the properties of their generated synthetic graphs.
DEFF Research Database (Denmark)
Mansutti, Alessio; Miculan, Marino; Peressotti, Marco
2017-01-01
We introduce loose graph simulations (LGS), a new notion about labelled graphs which subsumes in an intuitive and natural way subgraph isomorphism (SGI), regular language pattern matching (RLPM) and graph simulation (GS). Being a unification of all these notions, LGS allows us to express directly...... also problems which are “mixed” instances of previous ones, and hence which would not fit easily in any of them. After the definition and some examples, we show that the problem of finding loose graph simulations is NP-complete, we provide formal translation of SGI, RLPM, and GS into LGSs, and we give...
Directory of Open Access Journals (Sweden)
Alberto Apostolico
2009-08-01
Full Text Available The Web Graph is a large-scale graph that does not fit in main memory, so that lossless compression methods have been proposed for it. This paper introduces a compression scheme that combines efficient storage with fast retrieval for the information in a node. The scheme exploits the properties of the Web Graph without assuming an ordering of the URLs, so that it may be applied to more general graphs. Tests on some datasets of use achieve space savings of about 10% over existing methods.
FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET.
N Ahmed, Malik; Abdullah, Abdul Hanan; Kaiwartya, Omprakash
2016-01-01
Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs) significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS) and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM) for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV) protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-the-art techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks.
FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET.
Directory of Open Access Journals (Sweden)
Malik N Ahmed
Full Text Available Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-the-art techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks.
Chriqui, Jamie F; Eidson, Shelby S; Bates, Hannalori; Kowalczyk, Shelly; Chaloupka, Frank J
2008-07-01
Junk food consumption is associated with rising obesity rates in the United States. While a "junk food" specific tax is a potential public health intervention, a majority of states already impose sales taxes on certain junk food and soft drinks. This study reviews the state sales tax variance for soft drinks and selected snack products sold through grocery stores and vending machines as of January 2007. Sales taxes vary by state, intended retail location (grocery store vs. vending machine), and product. Vended snacks and soft drinks are taxed at a higher rate than grocery items and other food products, generally, indicative of a "disfavored" tax status attributed to vended items. Soft drinks, candy, and gum are taxed at higher rates than are other items examined. Similar tax schemes in other countries and the potential implications of these findings relative to the relationship between price and consumption are discussed.
Graph Theory. 1. Fragmentation of Structural Graphs
Directory of Open Access Journals (Sweden)
Lorentz JÄNTSCHI
2002-12-01
Full Text Available The investigation of structural graphs has many fields of applications in engineering, especially in applied sciences like as applied chemistry and physics, computer sciences and automation, electronics and telecommunication. The main subject of the paper is to express fragmentation criteria in graph using a new method of investigation: terminal paths. Using terminal paths are defined most of the fragmentation criteria that are in use in molecular topology, but the fields of applications are more generally than that, as I mentioned before. Graphical examples of fragmentation are given for every fragmentation criteria. Note that all fragmentation is made with a computer program that implements a routine for every criterion.[1] A web routine for tracing all terminal paths in graph can be found at the address: http://vl.academicdirect.ro/molecular_topology/tpaths/ [1] M. V. Diudea, I. Gutman, L. Jäntschi, Molecular Topology, Nova Science, Commack, New York, 2001, 2002.
Kalaji, AS; Hierons, RM; Swift, S
2009-01-01
The extended finite state machine (EFSM) is a powerful approach for modeling state-based systems. However, testing from EFSMs is complicated by the existence of infeasible paths. One important problem is the existence of a transition with a guard that references a counter variable whose value depends on previous transitions. The presence of such transitions in paths often leads to infeasible paths. This paper proposes a novel approach to bypass the counter problem. The proposed approach is ev...
A coherent Ising machine for 2000-node optimization problems
Inagaki, Takahiro; Haribara, Yoshitaka; Igarashi, Koji; Sonobe, Tomohiro; Tamate, Shuhei; Honjo, Toshimori; Marandi, Alireza; McMahon, Peter L.; Umeki, Takeshi; Enbutsu, Koji; Tadanaga, Osamu; Takenouchi, Hirokazu; Aihara, Kazuyuki; Kawarabayashi, Ken-ichi; Inoue, Kyo; Utsunomiya, Shoko; Takesue, Hiroki
2016-11-01
The analysis and optimization of complex systems can be reduced to mathematical problems collectively known as combinatorial optimization. Many such problems can be mapped onto ground-state search problems of the Ising model, and various artificial spin systems are now emerging as promising approaches. However, physical Ising machines have suffered from limited numbers of spin-spin couplings because of implementations based on localized spins, resulting in severe scalability problems. We report a 2000-spin network with all-to-all spin-spin couplings. Using a measurement and feedback scheme, we coupled time-multiplexed degenerate optical parametric oscillators to implement maximum cut problems on arbitrary graph topologies with up to 2000 nodes. Our coherent Ising machine outperformed simulated annealing in terms of accuracy and computation time for a 2000-node complete graph.
High Dimensional Spectral Graph Theory and Non-backtracking Random Walks on Graphs
Kempton, Mark
This thesis has two primary areas of focus. First we study connection graphs, which are weighted graphs in which each edge is associated with a d-dimensional rotation matrix for some fixed dimension d, in addition to a scalar weight. Second, we study non-backtracking random walks on graphs, which are random walks with the additional constraint that they cannot return to the immediately previous state at any given step. Our work in connection graphs is centered on the notion of consistency, that is, the product of rotations moving from one vertex to another is independent of the path taken, and a generalization called epsilon-consistency. We present higher dimensional versions of the combinatorial Laplacian matrix and normalized Laplacian matrix from spectral graph theory, and give results characterizing the consistency of a connection graph in terms of the spectra of these matrices. We generalize several tools from classical spectral graph theory, such as PageRank and effective resistance, to apply to connection graphs. We use these tools to give algorithms for sparsification, clustering, and noise reduction on connection graphs. In non-backtracking random walks, we address the question raised by Alon et. al. concerning how the mixing rate of a non-backtracking random walk to its stationary distribution compares to the mixing rate for an ordinary random walk. Alon et. al. address this question for regular graphs. We take a different approach, and use a generalization of Ihara's Theorem to give a new proof of Alon's result for regular graphs, and to extend the result to biregular graphs. Finally, we give a non-backtracking version of Polya's Random Walk Theorem for 2-dimensional grids.
Directory of Open Access Journals (Sweden)
Kryukov Igor Yu.
2017-01-01
Full Text Available Present article is devoted to the development of the mathematical model, which describes thermal state and crystallization process of the rectangular cross-section blank while continious process of extraction from a horysontal continious casting machine (HCCM.The developed model took cue for the heat-transfer properties of non-iron metal teeming; its temperature on entry to the casting mold; cooling conditions of blank in the carbon molds in the presence of a copper water cooler. Besides, has been considered the asymmetry of heat interchange from blank`s head and drag at mold, coming out from fluid contraction and features of the horizontal casting mold. The developed mathematical model allows to determine alterations in crystallizing blank of the following factors with respect to time: temperature pattern of crystallizing blank under different technical working regimes of HCCM; boundaries of solid two-phase field and liquid two-phase filed; blank`s thickness variation under shrinkage of the ingot`s material
A graph rewriting programming language for graph drawing
Rodgers, Peter
1998-01-01
This paper describes Grrr, a prototype visual graph drawing tool. Previously there were no visual languages for programming graph drawing algorithms despite the inherently visual nature of the process. The languages which gave a diagrammatic view of graphs were not computationally complete and so could not be used to implement complex graph drawing algorithms. Hence current graph drawing tools are all text based. Recent developments in graph rewriting systems have produced computationally com...
Graph Processing on GPUs: A Survey
DEFF Research Database (Denmark)
Shi, Xuanhua; Zheng, Zhigao; Zhou, Yongluan
2018-01-01
hundreds of billions, has attracted much attention in both industry and academia. It still remains a great challenge to process such large-scale graphs. Researchers have been seeking for new possible solutions. Because of the massive degree of parallelism and the high memory access bandwidth in GPU......, utilizing GPU to accelerate graph processing proves to be a promising solution. This article surveys the key issues of graph processing on GPUs, including data layout, memory access pattern, workload mapping, and specific GPU programming. In this article, we summarize the state-of-the-art research on GPU...
de Mol, M.J.; Rensink, Arend; Hunt, James J.
This paper introduces an approach for adding graph transformation-based functionality to existing JAVA programs. The approach relies on a set of annotations to identify the intended graph structure, as well as on user methods to manipulate that structure, within the user’s own JAVA class
Cohen, A.M.; Beineke, L.W.; Wilson, R.J.; Cameron, P.J.
2004-01-01
In this chapter we investigate the classification of distance-transitive graphs: these are graphs whose automorphism groups are transitive on each of the sets of pairs of vertices at distance i, for i = 0, 1,.... We provide an introduction into the field. By use of the classification of finite
Joyner, W David
2017-01-01
This textbook acts as a pathway to higher mathematics by seeking and illuminating the connections between graph theory and diverse fields of mathematics, such as calculus on manifolds, group theory, algebraic curves, Fourier analysis, cryptography and other areas of combinatorics. An overview of graph theory definitions and polynomial invariants for graphs prepares the reader for the subsequent dive into the applications of graph theory. To pique the reader’s interest in areas of possible exploration, recent results in mathematics appear throughout the book, accompanied with examples of related graphs, how they arise, and what their valuable uses are. The consequences of graph theory covered by the authors are complicated and far-reaching, so topics are always exhibited in a user-friendly manner with copious graphs, exercises, and Sage code for the computation of equations. Samples of the book’s source code can be found at github.com/springer-math/adventures-in-graph-theory. The text is geared towards ad...
Perepelitsa, VA; Sergienko, [No Value; Kochkarov, AM
1999-01-01
Definitions of prefractal and fractal graphs are introduced, and they are used to formulate mathematical models in different fields of knowledge. The topicality of fractal-graph recognition from the point of view, of fundamental improvement in the efficiency of the solution of algorithmic problems
DEFF Research Database (Denmark)
Husfeldt, Thore
2015-01-01
This chapter presents an introduction to graph colouring algorithms. The focus is on vertex-colouring algorithms that work for general classes of graphs with worst-case performance guarantees in a sequential model of computation. The presentation aims to demonstrate the breadth of available...
Packing Degenerate Graphs Greedily
Czech Academy of Sciences Publication Activity Database
Allen, P.; Böttcher, J.; Hladký, J.; Piguet, Diana
2017-01-01
Roč. 61, August (2017), s. 45-51 ISSN 1571-0653 R&D Projects: GA ČR GJ16-07822Y Institutional support: RVO:67985807 Keywords : tree packing conjecture * graph packing * graph processes Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics
McKeith, Charles F A; Rock, Adam J; Clark, Gavin I
2017-06-01
In Australia, poker-machine gamblers represent a disproportionate number of problem gamblers. To cultivate a greater understanding of the psychological mechanisms involved in poker-machine gambling, a repeated measures cue-reactivity protocol was administered. A community sample of 38 poker-machine gamblers was assessed for problem-gambling severity and trait mindfulness. Participants were also assessed regarding altered state of awareness (ASA) and urge to gamble at baseline, following a neutral cue, and following a gambling cue. Results indicated that: (a) urge to gamble significantly increased from neutral cue to gambling cue, while controlling for baseline urge; (b) cue-reactive ASA did not significantly mediate the relationship between problem-gambling severity and cue-reactive urge (from neutral cue to gambling cue); (c) trait mindfulness was significantly negatively associated with both problem-gambling severity and cue-reactive urge (i.e., from neutral cue to gambling cue, while controlling for baseline urge); and (d) trait mindfulness did not significantly moderate the effect of problem-gambling severity on cue-reactive urge (from neutral cue to gambling cue). This is the first study to demonstrate a negative association between trait mindfulness and cue-reactive urge to gamble in a population of poker-machine gamblers. Thus, this association merits further evaluation both in relation to poker-machine gambling and other gambling modalities.
b-tree facets for the simple graph partitioning polytope
DEFF Research Database (Denmark)
Sørensen, Michael Malmros
2004-01-01
The simple graph partitioning problem is to partition an edge-weighted graph into mutually disjoint subgraphs, each consisting of no more than b nodes, such that the sum of the weights of all edges in the subgraphs is maximal. In this paper we introduce a large class of facet defining inequalities...... for the simple graph partitioning polytopes P_n(b), b >= 3, associated with the complete graph on n nodes. These inequalities are induced by a graph configuration which is built upon trees of cardinality b. We provide a closed-form theorem that states all necessary and sufficient conditions for the facet...... defining property of the inequalities. Udgivelsesdato: JUN...
Low-Rank Matrix Factorization With Adaptive Graph Regularizer.
Lu, Gui-Fu; Wang, Yong; Zou, Jian
2016-05-01
In this paper, we present a novel low-rank matrix factorization algorithm with adaptive graph regularizer (LMFAGR). We extend the recently proposed low-rank matrix with manifold regularization (MMF) method with an adaptive regularizer. Different from MMF, which constructs an affinity graph in advance, LMFAGR can simultaneously seek graph weight matrix and low-dimensional representations of data. That is, graph construction and low-rank matrix factorization are incorporated into a unified framework, which results in an automatically updated graph rather than a predefined one. The experimental results on some data sets demonstrate that the proposed algorithm outperforms the state-of-the-art low-rank matrix factorization methods.
Generalized connectivity of graphs
Li, Xueliang
2016-01-01
Noteworthy results, proof techniques, open problems and conjectures in generalized (edge-) connectivity are discussed in this book. Both theoretical and practical analyses for generalized (edge-) connectivity of graphs are provided. Topics covered in this book include: generalized (edge-) connectivity of graph classes, algorithms, computational complexity, sharp bounds, Nordhaus-Gaddum-type results, maximum generalized local connectivity, extremal problems, random graphs, multigraphs, relations with the Steiner tree packing problem and generalizations of connectivity. This book enables graduate students to understand and master a segment of graph theory and combinatorial optimization. Researchers in graph theory, combinatorics, combinatorial optimization, probability, computer science, discrete algorithms, complexity analysis, network design, and the information transferring models will find this book useful in their studies.
Incremental View Maintenance for Deductive Graph Databases Using Generalized Discrimination Networks
Directory of Open Access Journals (Sweden)
Thomas Beyhl
2016-12-01
Full Text Available Nowadays, graph databases are employed when relationships between entities are in the scope of database queries to avoid performance-critical join operations of relational databases. Graph queries are used to query and modify graphs stored in graph databases. Graph queries employ graph pattern matching that is NP-complete for subgraph isomorphism. Graph database views can be employed that keep ready answers in terms of precalculated graph pattern matches for often stated and complex graph queries to increase query performance. However, such graph database views must be kept consistent with the graphs stored in the graph database. In this paper, we describe how to use incremental graph pattern matching as technique for maintaining graph database views. We present an incremental maintenance algorithm for graph database views, which works for imperatively and declaratively specified graph queries. The evaluation shows that our maintenance algorithm scales when the number of nodes and edges stored in the graph database increases. Furthermore, our evaluation shows that our approach can outperform existing approaches for the incremental maintenance of graph query results.
Autoregressive Moving Average Graph Filtering
Isufi, Elvin; Loukas, Andreas; Simonetto, Andrea; Leus, Geert
2016-01-01
One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation. The design phi...
Superconducting rotating machines
International Nuclear Information System (INIS)
Smith, J.L. Jr.; Kirtley, J.L. Jr.; Thullen, P.
1975-01-01
The opportunities and limitations of the applications of superconductors in rotating electric machines are given. The relevant properties of superconductors and the fundamental requirements for rotating electric machines are discussed. The current state-of-the-art of superconducting machines is reviewed. Key problems, future developments and the long range potential of superconducting machines are assessed
Graph Treewidth and Geometric Thickness Parameters
Dujmović, Vida; Wood, David R.
2005-01-01
Consider a drawing of a graph $G$ in the plane such that crossing edges are coloured differently. The minimum number of colours, taken over all drawings of $G$, is the classical graph parameter "thickness". By restricting the edges to be straight, we obtain the "geometric thickness". By further restricting the vertices to be in convex position, we obtain the "book thickness". This paper studies the relationship between these parameters and treewidth. Our first main result states that for grap...
Directory of Open Access Journals (Sweden)
Elmer P. Dadios
2009-01-01
Full Text Available This paper presents a new algorithm for real time event detection using Finite State Machines with multiple Fuzzy Logic Probability Evaluators (FLPEs. A machine referee for a robot soccer game is developed and is used as the platform to test the proposed algorithm. A novel technique to detect collisions and other events in microrobot soccer game under inaccurate and insufficient information is presented. The robots' collision is used to determine goalkeeper charging and goal score events which are crucial for the machine referee's decisions. The Main State Machine (MSM handles the schedule of event activation. The FLPE calculates the probabilities of the true occurrence of the events. Final decisions about the occurrences of events are evaluated and compared through threshold crisp probability values. The outputs of FLPEs can be combined to calculate the probability of an event composed of subevents. Using multiple fuzzy logic system, the FLPE utilizes minimal number of rules and can be tuned individually. Experimental results show the accuracy and robustness of the proposed algorithm.
Subgraph detection using graph signals
Chepuri, Sundeep Prabhakar
2017-03-06
In this paper we develop statistical detection theory for graph signals. In particular, given two graphs, namely, a background graph that represents an usual activity and an alternative graph that represents some unusual activity, we are interested in answering the following question: To which of the two graphs does the observed graph signal fit the best? To begin with, we assume both the graphs are known, and derive an optimal Neyman-Pearson detector. Next, we derive a suboptimal detector for the case when the alternative graph is not known. The developed theory is illustrated with numerical experiments.
Subgraph detection using graph signals
Chepuri, Sundeep Prabhakar; Leus, Geert
2017-01-01
In this paper we develop statistical detection theory for graph signals. In particular, given two graphs, namely, a background graph that represents an usual activity and an alternative graph that represents some unusual activity, we are interested in answering the following question: To which of the two graphs does the observed graph signal fit the best? To begin with, we assume both the graphs are known, and derive an optimal Neyman-Pearson detector. Next, we derive a suboptimal detector for the case when the alternative graph is not known. The developed theory is illustrated with numerical experiments.
Pre-use anesthesia machine check; certified anesthesia technician based quality improvement audit.
Al Suhaibani, Mazen; Al Malki, Assaf; Al Dosary, Saad; Al Barmawi, Hanan; Pogoku, Mahdhav
2014-01-01
Quality assurance of providing a work ready machine in multiple theatre operating rooms in a tertiary modern medical center in Riyadh. The aim of the following study is to keep high quality environment for workers and patients in surgical operating rooms. Technicians based audit by using key performance indicators to assure inspection, passing test of machine worthiness for use daily and in between cases and in case of unexpected failure to provide quick replacement by ready to use another anesthetic machine. The anesthetic machines in all operating rooms are daily and continuously inspected and passed as ready by technicians and verified by anesthesiologist consultant or assistant consultant. The daily records of each machines were collected then inspected for data analysis by quality improvement committee department for descriptive analysis and report the degree of staff compliance to daily inspection as "met" items. Replaced machine during use and overall compliance. Distractive statistic using Microsoft Excel 2003 tables and graphs of sums and percentages of item studied in this audit. Audit obtained highest compliance percentage and low rate of replacement of machine which indicate unexpected machine state of use and quick machine switch. The authors are able to conclude that following regular inspection and running self-check recommended by the manufacturers can contribute to abort any possibility of hazard of anesthesia machine failure during operation. Furthermore in case of unexpected reason to replace the anesthesia machine in quick maneuver contributes to high assured operative utilization of man machine inter-phase in modern surgical operating rooms.
Du, Liang; Yang, Yi; Harley, Ronald Gordon; Habetler, Thomas G.; He, Dawei
2016-08-09
A system is for a plurality of different electric load types. The system includes a plurality of sensors structured to sense a voltage signal and a current signal for each of the different electric loads; and a processor. The processor acquires a voltage and current waveform from the sensors for a corresponding one of the different electric load types; calculates a power or current RMS profile of the waveform; quantizes the power or current RMS profile into a set of quantized state-values; evaluates a state-duration for each of the quantized state-values; evaluates a plurality of state-types based on the power or current RMS profile and the quantized state-values; generates a state-sequence that describes a corresponding finite state machine model of a generalized load start-up or transient profile for the corresponding electric load type; and identifies the corresponding electric load type.
Chyzhyk, Darya; Graña, Manuel; Öngür, Döst; Shinn, Ann K
2015-05-01
Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mechanisms underlying AH in schizophrenia may offer insight into the pathophysiology associated with AH more broadly across multiple neuropsychiatric disease conditions. In this paper, we address the problem of classifying schizophrenia patients with and without a history of AH, and healthy control (HC) subjects. To this end, we performed feature extraction from resting state functional magnetic resonance imaging (rsfMRI) data and applied machine learning classifiers, testing two kinds of neuroimaging features: (a) functional connectivity (FC) measures computed by lattice auto-associative memories (LAAM), and (b) local activity (LA) measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF). We show that it is possible to perform classification within each pair of subject groups with high accuracy. Discrimination between patients with and without lifetime AH was highest, while discrimination between schizophrenia patients and HC participants was worst, suggesting that classification according to the symptom dimension of AH may be more valid than discrimination on the basis of traditional diagnostic categories. FC measures seeded in right Heschl's gyrus (RHG) consistently showed stronger discriminative power than those seeded in left Heschl's gyrus (LHG), a finding that appears to support AH models focusing on right hemisphere abnormalities. The cortical brain localizations derived from the features with strong classification performance are consistent with proposed AH models, and include left inferior frontal gyrus (IFG), parahippocampal gyri, the cingulate cortex, as well as several temporal and prefrontal cortical brain regions. Overall, the observed findings suggest that
Bollobas, Bela
2004-01-01
The ever-expanding field of extremal graph theory encompasses a diverse array of problem-solving methods, including applications to economics, computer science, and optimization theory. This volume, based on a series of lectures delivered to graduate students at the University of Cambridge, presents a concise yet comprehensive treatment of extremal graph theory.Unlike most graph theory treatises, this text features complete proofs for almost all of its results. Further insights into theory are provided by the numerous exercises of varying degrees of difficulty that accompany each chapter. A
Energy Technology Data Exchange (ETDEWEB)
Qi, Junjian; Sun, Kai; Wang, Jianhui; Liu, Hui
2018-03-01
In this paper, in order to enhance the numerical stability of the unscented Kalman filter (UKF) used for power system dynamic state estimation, a new UKF with guaranteed positive semidifinite estimation error covariance (UKFGPS) is proposed and compared with five existing approaches, including UKFschol, UKF-kappa, UKFmodified, UKF-Delta Q, and the squareroot UKF (SRUKF). These methods and the extended Kalman filter (EKF) are tested by performing dynamic state estimation on WSCC 3-machine 9-bus system and NPCC 48-machine 140-bus system. For WSCC system, all methods obtain good estimates. However, for NPCC system, both EKF and the classic UKF fail. It is found that UKFschol, UKF-kappa, and UKF-Delta Q do not work well in some estimations while UKFGPS works well in most cases. UKFmodified and SRUKF can always work well, indicating their better scalability mainly due to the enhanced numerical stability.
Taber, Daniel R.; Chriqui, Jamie F.; Vuillaume, Renee; Chaloupka, Frank J.
2014-01-01
Background: Sodas are widely sold in vending machines and other school venues in the United States, particularly in high school. Research suggests that policy changes have reduced soda access, but the impact of reduced access on consumption is unclear. This study was designed to identify student, environmental, or policy characteristics that modify the associations between school vending machines and student dietary behaviors. Methods: Data on school vending machine access and student diet we...
A hierarchical approach to reducing communication in parallel graph algorithms
Harshvardhan,
2015-01-01
Large-scale graph computing has become critical due to the ever-increasing size of data. However, distributed graph computations are limited in their scalability and performance due to the heavy communication inherent in such computations. This is exacerbated in scale-free networks, such as social and web graphs, which contain hub vertices that have large degrees and therefore send a large number of messages over the network. Furthermore, many graph algorithms and computations send the same data to each of the neighbors of a vertex. Our proposed approach recognizes this, and reduces communication performed by the algorithm without change to user-code, through a hierarchical machine model imposed upon the input graph. The hierarchical model takes advantage of locale information of the neighboring vertices to reduce communication, both in message volume and total number of bytes sent. It is also able to better exploit the machine hierarchy to further reduce the communication costs, by aggregating traffic between different levels of the machine hierarchy. Results of an implementation in the STAPL GL shows improved scalability and performance over the traditional level-synchronous approach, with 2.5 × - 8× improvement for a variety of graph algorithms at 12, 000+ cores.
Kinch, Martin W.; Melis, Wim J.C.; Keates, Simeon
2017-01-01
This paper will consider the current state of Machine Learning for Artificial Intelligence, more specifically for applications, such as: Speech Recognition, Game Playing and Image Processing. The artificial world tends to make limited use of context in comparison to what currently happens in human life, while it would benefit from improvements in this area. Additionally, the process of transferring knowledge between application domains is another important area where artificial system can imp...
Probabilistic graphs as a conceptual and computational tool in hydrology and water management
Schoups, Gerrit
2014-05-01
Originally developed in the fields of machine learning and artificial intelligence, probabilistic graphs constitute a general framework for modeling complex systems in the presence of uncertainty. The framework consists of three components: 1. Representation of the model as a graph (or network), with nodes depicting random variables in the model (e.g. parameters, states, etc), which are joined together by factors. Factors are local probabilistic or deterministic relations between subsets of variables, which, when multiplied together, yield the joint distribution over all variables. 2. Consistent use of probability theory for quantifying uncertainty, relying on basic rules of probability for assimilating data into the model and expressing unknown variables as a function of observations (via the posterior distribution). 3. Efficient, distributed approximation of the posterior distribution using general-purpose algorithms that exploit model structure encoded in the graph. These attributes make probabilistic graphs potentially useful as a conceptual and computational tool in hydrology and water management (and beyond). Conceptually, they can provide a common framework for existing and new probabilistic modeling approaches (e.g. by drawing inspiration from other fields of application), while computationally they can make probabilistic inference feasible in larger hydrological models. The presentation explores, via examples, some of these benefits.
Use of Attack Graphs in Security Systems
Directory of Open Access Journals (Sweden)
Vivek Shandilya
2014-01-01
Full Text Available Attack graphs have been used to model the vulnerabilities of the systems and their potential exploits. The successful exploits leading to the partial/total failure of the systems are subject of keen security interest. Considerable effort has been expended in exhaustive modeling, analyses, detection, and mitigation of attacks. One prominent methodology involves constructing attack graphs of the pertinent system for analysis and response strategies. This not only gives the simplified representation of the system, but also allows prioritizing the security properties whose violations are of greater concern, for both detection and repair. We present a survey and critical study of state-of-the-art technologies in attack graph generation and use in security system. Based on our research, we identify the potential, challenges, and direction of the current research in using attack graphs.
Directory of Open Access Journals (Sweden)
Haynes Teresa W.
2014-08-01
Full Text Available A path π = (v1, v2, . . . , vk+1 in a graph G = (V,E is a downhill path if for every i, 1 ≤ i ≤ k, deg(vi ≥ deg(vi+1, where deg(vi denotes the degree of vertex vi ∈ V. The downhill domination number equals the minimum cardinality of a set S ⊆ V having the property that every vertex v ∈ V lies on a downhill path originating from some vertex in S. We investigate downhill domination numbers of graphs and give upper bounds. In particular, we show that the downhill domination number of a graph is at most half its order, and that the downhill domination number of a tree is at most one third its order. We characterize the graphs obtaining each of these bounds
Tailored Random Graph Ensembles
International Nuclear Information System (INIS)
Roberts, E S; Annibale, A; Coolen, A C C
2013-01-01
Tailored graph ensembles are a developing bridge between biological networks and statistical mechanics. The aim is to use this concept to generate a suite of rigorous tools that can be used to quantify and compare the topology of cellular signalling networks, such as protein-protein interaction networks and gene regulation networks. We calculate exact and explicit formulae for the leading orders in the system size of the Shannon entropies of random graph ensembles constrained with degree distribution and degree-degree correlation. We also construct an ergodic detailed balance Markov chain with non-trivial acceptance probabilities which converges to a strictly uniform measure and is based on edge swaps that conserve all degrees. The acceptance probabilities can be generalized to define Markov chains that target any alternative desired measure on the space of directed or undirected graphs, in order to generate graphs with more sophisticated topological features.
Alspach, BR
1985-01-01
This volume deals with a variety of problems involving cycles in graphs and circuits in digraphs. Leading researchers in this area present here 3 survey papers and 42 papers containing new results. There is also a collection of unsolved problems.
Wilson, Robin J
1985-01-01
Graph Theory has recently emerged as a subject in its own right, as well as being an important mathematical tool in such diverse subjects as operational research, chemistry, sociology and genetics. This book provides a comprehensive introduction to the subject.
Hyperbolicity in median graphs
Indian Academy of Sciences (India)
mic problems in hyperbolic spaces and hyperbolic graphs have been .... that in general the main obstacle is that we do not know the location of ...... [25] Jonckheere E and Lohsoonthorn P, A hyperbolic geometry approach to multipath routing,.
Uniform Single Valued Neutrosophic Graphs
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S. Broumi
2017-09-01
Full Text Available In this paper, we propose a new concept named the uniform single valued neutrosophic graph. An illustrative example and some properties are examined. Next, we develop an algorithmic approach for computing the complement of the single valued neutrosophic graph. A numerical example is demonstrated for computing the complement of single valued neutrosophic graphs and uniform single valued neutrosophic graph.
Collective Rationality in Graph Aggregation
Endriss, U.; Grandi, U.; Schaub, T.; Friedrich, G.; O'Sullivan, B.
2014-01-01
Suppose a number of agents each provide us with a directed graph over a common set of vertices. Graph aggregation is the problem of computing a single “collective” graph that best represents the information inherent in this profile of individual graphs. We consider this aggregation problem from the
International Nuclear Information System (INIS)
Barra, F.; Gaspard, P.
2001-01-01
We consider the classical evolution of a particle on a graph by using a time-continuous Frobenius-Perron operator that generalizes previous propositions. In this way, the relaxation rates as well as the chaotic properties can be defined for the time-continuous classical dynamics on graphs. These properties are given as the zeros of some periodic-orbit zeta functions. We consider in detail the case of infinite periodic graphs where the particle undergoes a diffusion process. The infinite spatial extension is taken into account by Fourier transforms that decompose the observables and probability densities into sectors corresponding to different values of the wave number. The hydrodynamic modes of diffusion are studied by an eigenvalue problem of a Frobenius-Perron operator corresponding to a given sector. The diffusion coefficient is obtained from the hydrodynamic modes of diffusion and has the Green-Kubo form. Moreover, we study finite but large open graphs that converge to the infinite periodic graph when their size goes to infinity. The lifetime of the particle on the open graph is shown to correspond to the lifetime of a system that undergoes a diffusion process before it escapes
Development of a finite state machine for the automates operation of the LLRF control at FLASH
Energy Technology Data Exchange (ETDEWEB)
Brandt, A.
2007-07-15
The entry of digital signal processors in modern control systems not only allows for extended diagnostics compared to analog systems but also for sophisticated and tricky extensions of the control algorithms. With modern DSP- and FPGA-technology, the processing speed of digital systems is no longer inferior to analog systems in many applications. A higher degree of digitalization leads to an increased complexity of the systems and hence to higher requirements on their operators. The focus of research and development in the field of high frequency control has changed in the last few years and moved towards the direction of software development and complexity management. In the presented thesis, a frame for an automation concept of modern high frequency control systems is developed. The developed automation is based on the concept of finite state machines (FSM), which is established in industry for years. A flexible framework was developed, in which procedures communicate using standardized interfaces and can be exchanged easily. With that, the developer of high frequency control components as well as the operator on shift shall be empowered to improve and adapt the automation to changed conditions without special programming skills required. Along the automation concept a number of algorithms addressing various problems were developed which satisfy the needs of modern high frequency control systems. Among the developed and successfully tested algorithms are the calibration of incident and reflected wave of resonators without antennas, the fast adaptive compensation of repetitive errors, the robust estimation of the phase advance in the control loop and the latency adjustment for the rejection of instabilities caused by passband modes. During the development of the resonator theory, high value was set on the usability of the equation in algorithms for high frequency control. The usage of the common nomenclature of control theory emphasizes the underlying mathematical
Development of a finite state machine for the automated operation of the LLRF control at FLASH
International Nuclear Information System (INIS)
Brandt, A.
2007-07-01
The entry of digital signal processors in modern control systems not only allows for extended diagnostics compared to analog systems but also for sophisticated and tricky extensions of the control algorithms. With modern DSP- and FPGA-technology, the processing speed of digital systems is no longer inferior to analog systems in many applications. A higher degree of digitalization leads to an increased complexity of the systems and hence to higher requirements on their operators. The focus of research and development in the field of high frequency control has changed in the last few years and moved towards the direction of software development and complexity management. In the presented thesis, a frame for an automation concept of modern high frequency control systems is developed. The developed automation is based on the concept of finite state machines (FSM), which is established in industry for years. A flexible framework was developed, in which procedures communicate using standardized interfaces and can be exchanged easily. With that, the developer of high frequency control components as well as the operator on shift shall be empowered to improve and adapt the automation to changed conditions without special programming skills required. Along the automation concept a number of algorithms addressing various problems were developed which satisfy the needs of modern high frequency control systems. Among the developed and successfully tested algorithms are the calibration of incident and reflected wave of resonators without antennas, the fast adaptive compensation of repetitive errors, the robust estimation of the phase advance in the control loop and the latency adjustment for the rejection of instabilities caused by passband modes. During the development of the resonator theory, high value was set on the usability of the equation in algorithms for high frequency control. The usage of the common nomenclature of control theory emphasizes the underlying mathematical
Performance optimization of a CNC machine through exploration of the timed state space
Mota, M.A. Mujica; Piera, Miquel Angel
2010-01-01
Flexible production units provide very efficient mechanisms to adapt the type and production rate according to fluctuations in demand. The optimal sequence of the different manufacturing tasks in each machine is a challenging problem that can deal with important productivity benefits.
Online Graph Completion: Multivariate Signal Recovery in Computer Vision.
Kim, Won Hwa; Jalal, Mona; Hwang, Seongjae; Johnson, Sterling C; Singh, Vikas
2017-07-01
The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e.g., human supervision) and the underlying inference algorithms are closely interwined. While classical work in active learning provides effective solutions when the learning module involves classification and regression tasks, many practical issues such as partially observed measurements, financial constraints and even additional distributional or structural aspects of the data typically fall outside the scope of this treatment. For instance, with sequential acquisition of partial measurements of data that manifest as a matrix (or tensor), novel strategies for completion (or collaborative filtering) of the remaining entries have only been studied recently. Motivated by vision problems where we seek to annotate a large dataset of images via a crowdsourced platform or alternatively, complement results from a state-of-the-art object detector using human feedback, we study the "completion" problem defined on graphs, where requests for additional measurements must be made sequentially. We design the optimization model in the Fourier domain of the graph describing how ideas based on adaptive submodularity provide algorithms that work well in practice. On a large set of images collected from Imgur, we see promising results on images that are otherwise difficult to categorize. We also show applications to an experimental design problem in neuroimaging.
Vector control of induction machines
Robyns, Benoit
2012-01-01
After a brief introduction to the main law of physics and fundamental concepts inherent in electromechanical conversion, ""Vector Control of Induction Machines"" introduces the standard mathematical models for induction machines - whichever rotor technology is used - as well as several squirrel-cage induction machine vector-control strategies. The use of causal ordering graphs allows systematization of the design stage, as well as standardization of the structure of control devices. ""Vector Control of Induction Machines"" suggests a unique approach aimed at reducing parameter sensitivity for
Proxy Graph: Visual Quality Metrics of Big Graph Sampling.
Nguyen, Quan Hoang; Hong, Seok-Hee; Eades, Peter; Meidiana, Amyra
2017-06-01
Data sampling has been extensively studied for large scale graph mining. Many analyses and tasks become more efficient when performed on graph samples of much smaller size. The use of proxy objects is common in software engineering for analysis and interaction with heavy objects or systems. In this paper, we coin the term 'proxy graph' and empirically investigate how well a proxy graph visualization can represent a big graph. Our investigation focuses on proxy graphs obtained by sampling; this is one of the most common proxy approaches. Despite the plethora of data sampling studies, this is the first evaluation of sampling in the context of graph visualization. For an objective evaluation, we propose a new family of quality metrics for visual quality of proxy graphs. Our experiments cover popular sampling techniques. Our experimental results lead to guidelines for using sampling-based proxy graphs in visualization.
On some covering graphs of a graph
Directory of Open Access Journals (Sweden)
Shariefuddin Pirzada
2016-10-01
Full Text Available For a graph $G$ with vertex set $V(G=\\{v_1, v_2, \\dots, v_n\\}$, let $S$ be the covering set of $G$ having the maximum degree over all the minimum covering sets of $G$. Let $N_S[v]=\\{u\\in S : uv \\in E(G \\}\\cup \\{v\\}$ be the closed neighbourhood of the vertex $v$ with respect to $S.$ We define a square matrix $A_S(G= (a_{ij},$ by $a_{ij}=1,$ if $\\left |N_S[v_i]\\cap N_S[v_j] \\right| \\geq 1, i\
International Nuclear Information System (INIS)
Landrum, G.A.Gregory A.; Genin, Hugh
2003-01-01
Machine-learning methods are a collection of techniques for building predictive models from experimental data. The algorithms are problem-independent: the chemistry and physics of the problem being studied are contained in the descriptors used to represent the known data. The application of a variety of machine-learning methods to the prediction of ferromagnetism in ordered and disordered transition metal alloys is presented. Applying a decision tree algorithm to build a predictive model for ordered phases results in a model that is 100% accurate. The same algorithm achieves 99% accuracy when trained on a data set containing both ordered and disordered phases. Details of the descriptor sets for both applications are also presented
Fundamentals of algebraic graph transformation
Ehrig, Hartmut; Prange, Ulrike; Taentzer, Gabriele
2006-01-01
Graphs are widely used to represent structural information in the form of objects and connections between them. Graph transformation is the rule-based manipulation of graphs, an increasingly important concept in computer science and related fields. This is the first textbook treatment of the algebraic approach to graph transformation, based on algebraic structures and category theory. Part I is an introduction to the classical case of graph and typed graph transformation. In Part II basic and advanced results are first shown for an abstract form of replacement systems, so-called adhesive high-level replacement systems based on category theory, and are then instantiated to several forms of graph and Petri net transformation systems. Part III develops typed attributed graph transformation, a technique of key relevance in the modeling of visual languages and in model transformation. Part IV contains a practical case study on model transformation and a presentation of the AGG (attributed graph grammar) tool envir...
The STAPL Parallel Graph Library
Harshvardhan,
2013-01-01
This paper describes the stapl Parallel Graph Library, a high-level framework that abstracts the user from data-distribution and parallelism details and allows them to concentrate on parallel graph algorithm development. It includes a customizable distributed graph container and a collection of commonly used parallel graph algorithms. The library introduces pGraph pViews that separate algorithm design from the container implementation. It supports three graph processing algorithmic paradigms, level-synchronous, asynchronous and coarse-grained, and provides common graph algorithms based on them. Experimental results demonstrate improved scalability in performance and data size over existing graph libraries on more than 16,000 cores and on internet-scale graphs containing over 16 billion vertices and 250 billion edges. © Springer-Verlag Berlin Heidelberg 2013.
Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs
Jamour, Fuad Tarek
2017-10-17
Betweenness centrality quantifies the importance of nodes in a graph in many applications, including network analysis, community detection and identification of influential users. Typically, graphs in such applications evolve over time. Thus, the computation of betweenness centrality should be performed incrementally. This is challenging because updating even a single edge may trigger the computation of all-pairs shortest paths in the entire graph. Existing approaches cannot scale to large graphs: they either require excessive memory (i.e., quadratic to the size of the input graph) or perform unnecessary computations rendering them prohibitively slow. We propose iCentral; a novel incremental algorithm for computing betweenness centrality in evolving graphs. We decompose the graph into biconnected components and prove that processing can be localized within the affected components. iCentral is the first algorithm to support incremental betweeness centrality computation within a graph component. This is done efficiently, in linear space; consequently, iCentral scales to large graphs. We demonstrate with real datasets that the serial implementation of iCentral is up to 3.7 times faster than existing serial methods. Our parallel implementation that scales to large graphs, is an order of magnitude faster than the state-of-the-art parallel algorithm, while using an order of magnitude less computational resources.
White, AT
1985-01-01
The field of topological graph theory has expanded greatly in the ten years since the first edition of this book appeared. The original nine chapters of this classic work have therefore been revised and updated. Six new chapters have been added, dealing with: voltage graphs, non-orientable imbeddings, block designs associated with graph imbeddings, hypergraph imbeddings, map automorphism groups and change ringing.Thirty-two new problems have been added to this new edition, so that there are now 181 in all; 22 of these have been designated as ``difficult'''' and 9 as ``unsolved''''. Three of the four unsolved problems from the first edition have been solved in the ten years between editions; they are now marked as ``difficult''''.
Ribes, Luis
2017-01-01
This book offers a detailed introduction to graph theoretic methods in profinite groups and applications to abstract groups. It is the first to provide a comprehensive treatment of the subject. The author begins by carefully developing relevant notions in topology, profinite groups and homology, including free products of profinite groups, cohomological methods in profinite groups, and fixed points of automorphisms of free pro-p groups. The final part of the book is dedicated to applications of the profinite theory to abstract groups, with sections on finitely generated subgroups of free groups, separability conditions in free and amalgamated products, and algorithms in free groups and finite monoids. Profinite Graphs and Groups will appeal to students and researchers interested in profinite groups, geometric group theory, graphs and connections with the theory of formal languages. A complete reference on the subject, the book includes historical and bibliographical notes as well as a discussion of open quest...
Subdominant pseudoultrametric on graphs
Energy Technology Data Exchange (ETDEWEB)
Dovgoshei, A A; Petrov, E A [Institute of Applied Mathematics and Mechanics, National Academy of Sciences of Ukraine, Donetsk (Ukraine)
2013-08-31
Let (G,w) be a weighted graph. We find necessary and sufficient conditions under which the weight w:E(G)→R{sup +} can be extended to a pseudoultrametric on V(G), and establish a criterion for the uniqueness of such an extension. We demonstrate that (G,w) is a complete k-partite graph, for k≥2, if and only if for any weight that can be extended to a pseudoultrametric, among all such extensions one can find the least pseudoultrametric consistent with w. We give a structural characterization of graphs for which the subdominant pseudoultrametric is an ultrametric for any strictly positive weight that can be extended to a pseudoultrametric. Bibliography: 14 titles.
Transduction on Directed Graphs via Absorbing Random Walks.
De, Jaydeep; Zhang, Xiaowei; Lin, Feng; Cheng, Li
2017-08-11
In this paper we consider the problem of graph-based transductive classification, and we are particularly interested in the directed graph scenario which is a natural form for many real world applications.Different from existing research efforts that either only deal with undirected graphs or circumvent directionality by means of symmetrization, we propose a novel random walk approach on directed graphs using absorbing Markov chains, which can be regarded as maximizing the accumulated expected number of visits from the unlabeled transient states. Our algorithm is simple, easy to implement, and works with large-scale graphs on binary, multiclass, and multi-label prediction problems. Moreover, it is capable of preserving the graph structure even when the input graph is sparse and changes over time, as well as retaining weak signals presented in the directed edges. We present its intimate connections to a number of existing methods, including graph kernels, graph Laplacian based methods, and interestingly, spanning forest of graphs. Its computational complexity and the generalization error are also studied. Empirically our algorithm is systematically evaluated on a wide range of applications, where it has shown to perform competitively comparing to a suite of state-of-the-art methods. In particular, our algorithm is shown to work exceptionally well with large sparse directed graphs with e.g. millions of nodes and tens of millions of edges, where it significantly outperforms other state-of-the-art methods. In the dynamic graph setting involving insertion or deletion of nodes and edge-weight changes over time, it also allows efficient online updates that produce the same results as of the batch update counterparts.
Quantitative graph theory mathematical foundations and applications
Dehmer, Matthias
2014-01-01
The first book devoted exclusively to quantitative graph theory, Quantitative Graph Theory: Mathematical Foundations and Applications presents and demonstrates existing and novel methods for analyzing graphs quantitatively. Incorporating interdisciplinary knowledge from graph theory, information theory, measurement theory, and statistical techniques, this book covers a wide range of quantitative-graph theoretical concepts and methods, including those pertaining to real and random graphs such as:Comparative approaches (graph similarity or distance)Graph measures to characterize graphs quantitat
Cheung, King Sing
2014-01-01
Petri nets are a formal and theoretically rich model for the modelling and analysis of systems. A subclass of Petri nets, augmented marked graphs possess a structure that is especially desirable for the modelling and analysis of systems with concurrent processes and shared resources.This monograph consists of three parts: Part I provides the conceptual background for readers who have no prior knowledge on Petri nets; Part II elaborates the theory of augmented marked graphs; finally, Part III discusses the application to system integration. The book is suitable as a first self-contained volume
Dayal, Amit; Brock, David
2018-01-01
Prashant Chandrasekar, a lead developer for the Social Interactome project, has tasked the team with creating a graph representation of the data collected from the social networks involved in that project. The data is currently stored in a MySQL database. The client requested that the graph database be Cayley, but after a literature review, Neo4j was chosen. The reasons for this shift will be explained in the design section. Secondarily, the team was tasked with coming up with three scena...
Stevanovic, Dragan
2015-01-01
Spectral Radius of Graphs provides a thorough overview of important results on the spectral radius of adjacency matrix of graphs that have appeared in the literature in the preceding ten years, most of them with proofs, and including some previously unpublished results of the author. The primer begins with a brief classical review, in order to provide the reader with a foundation for the subsequent chapters. Topics covered include spectral decomposition, the Perron-Frobenius theorem, the Rayleigh quotient, the Weyl inequalities, and the Interlacing theorem. From this introduction, the
Graph embedding with rich information through heterogeneous graph
Sun, Guolei
2017-01-01
Graph embedding, aiming to learn low-dimensional representations for nodes in graphs, has attracted increasing attention due to its critical application including node classification, link prediction and clustering in social network analysis. Most
The circumference of the square of a connected graph
DEFF Research Database (Denmark)
Brandt, S.; Muttel, J.; Rautenbach, D.
2014-01-01
The celebrated result of Fleischner states that the square of every 2-connected graph is Hamiltonian. We investigate what happens if the graph is just connected. For every n a parts per thousand yen 3, we determine the smallest length c(n) of a longest cycle in the square of a connected graph of ...... of order n and show that c(n) is a logarithmic function in n. Furthermore, for every c a parts per thousand yen 3, we characterize the connected graphs of largest order whose square contains no cycle of length at least c....
Using Graph and Vertex Entropy to Compare Empirical Graphs with Theoretical Graph Models
Directory of Open Access Journals (Sweden)
Tomasz Kajdanowicz
2016-09-01
Full Text Available Over the years, several theoretical graph generation models have been proposed. Among the most prominent are: the Erdős–Renyi random graph model, Watts–Strogatz small world model, Albert–Barabási preferential attachment model, Price citation model, and many more. Often, researchers working with real-world data are interested in understanding the generative phenomena underlying their empirical graphs. They want to know which of the theoretical graph generation models would most probably generate a particular empirical graph. In other words, they expect some similarity assessment between the empirical graph and graphs artificially created from theoretical graph generation models. Usually, in order to assess the similarity of two graphs, centrality measure distributions are compared. For a theoretical graph model this means comparing the empirical graph to a single realization of a theoretical graph model, where the realization is generated from the given model using an arbitrary set of parameters. The similarity between centrality measure distributions can be measured using standard statistical tests, e.g., the Kolmogorov–Smirnov test of distances between cumulative distributions. However, this approach is both error-prone and leads to incorrect conclusions, as we show in our experiments. Therefore, we propose a new method for graph comparison and type classification by comparing the entropies of centrality measure distributions (degree centrality, betweenness centrality, closeness centrality. We demonstrate that our approach can help assign the empirical graph to the most similar theoretical model using a simple unsupervised learning method.
Directory of Open Access Journals (Sweden)
Mladen Skelin
2014-03-01
Full Text Available Scenario-aware dataflow (SADF is a prominent tool for modeling and analysis of dynamic embedded dataflow applications. In SADF the application is represented as a finite collection of synchronous dataflow (SDF graphs, each of which represents one possible application behaviour or scenario. A finite state machine (FSM specifies the possible orders of scenario occurrences. The SADF model renders the tightest possible performance guarantees, but is limited by its finiteness. This means that from a practical point of view, it can only handle dynamic dataflow applications that are characterized by a reasonably sized set of possible behaviours or scenarios. In this paper we remove this limitation for a class of SADF graphs by means of SADF model parametrization in terms of graph port rates and actor execution times. First, we formally define the semantics of the model relevant for throughput analysis based on (max,+ linear system theory and (max,+ automata. Second, by generalizing some of the existing results, we give the algorithms for worst-case throughput analysis of parametric rate and parametric actor execution time acyclic SADF graphs with a fully connected, possibly infinite state transition system. Third, we demonstrate our approach on a few realistic applications from digital signal processing (DSP domain mapped onto an embedded multi-processor architecture.
Handbook of graph grammars and computing by graph transformation
Engels, G; Kreowski, H J; Rozenberg, G
1999-01-01
Graph grammars originated in the late 60s, motivated by considerations about pattern recognition and compiler construction. Since then, the list of areas which have interacted with the development of graph grammars has grown quite impressively. Besides the aforementioned areas, it includes software specification and development, VLSI layout schemes, database design, modeling of concurrent systems, massively parallel computer architectures, logic programming, computer animation, developmental biology, music composition, visual languages, and many others.The area of graph grammars and graph tran
Topics in graph theory graphs and their Cartesian product
Imrich, Wilfried; Rall, Douglas F
2008-01-01
From specialists in the field, you will learn about interesting connections and recent developments in the field of graph theory by looking in particular at Cartesian products-arguably the most important of the four standard graph products. Many new results in this area appear for the first time in print in this book. Written in an accessible way, this book can be used for personal study in advanced applications of graph theory or for an advanced graph theory course.
Executable Architecture of Net Enabled Operations: State Machine of Federated Nodes
2009-11-01
verbal descriptions from operators) of the current Command and Control (C2) practices into model form. In theory these should be Standard Operating...faudra une grande quantité de données pour faire en sorte que le modèle reflète les processus véritables, les auteurs recommandent que la machine à...descriptions from operators) of the current C2 practices into model form. In theory these should be SOPs that execute as a thread from start to finish. The
Hierarchical graphs for rule-based modeling of biochemical systems
Directory of Open Access Journals (Sweden)
Hu Bin
2011-02-01
Full Text Available Abstract 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
Bisseling, R.H.; Byrka, J.; Cerav-Erbas, S.; Gvozdenovic, N.; Lorenz, M.; Pendavingh, R.A.; Reeves, C.; Röger, M.; Verhoeven, A.; Berg, van den J.B.; Bhulai, S.; Hulshof, J.; Koole, G.; Quant, C.; Williams, J.F.
2006-01-01
Splitting a large software system into smaller and more manageable units has become an important problem for many organizations. The basic structure of a software system is given by a directed graph with vertices representing the programs of the system and arcs representing calls from one program to
Coloring geographical threshold graphs
Energy Technology Data Exchange (ETDEWEB)
Bradonjic, Milan [Los Alamos National Laboratory; Percus, Allon [Los Alamos National Laboratory; Muller, Tobias [EINDHOVEN UNIV. OF TECH
2008-01-01
We propose a coloring algorithm for sparse random graphs generated by the geographical threshold graph (GTG) model, a generalization of random geometric graphs (RGG). In a GTG, nodes are distributed in a Euclidean space, and edges are assigned according to a threshold function involving the distance between nodes as well as randomly chosen node weights. The motivation for analyzing this model is that many real networks (e.g., wireless networks, the Internet, etc.) need to be studied by using a 'richer' stochastic model (which in this case includes both a distance between nodes and weights on the nodes). Here, we analyze the GTG coloring algorithm together with the graph's clique number, showing formally that in spite of the differences in structure between GTG and RGG, the asymptotic behavior of the chromatic number is identical: {chi}1n 1n n / 1n n (1 + {omicron}(1)). Finally, we consider the leading corrections to this expression, again using the coloring algorithm and clique number to provide bounds on the chromatic number. We show that the gap between the lower and upper bound is within C 1n n / (1n 1n n){sup 2}, and specify the constant C.
Budhiraja, A.S.; Mukherjee, D.; Wu, R.
2017-01-01
We consider a variation of the supermarket model in which the servers can communicate with their neighbors and where the neighborhood relationships are described in terms of a suitable graph. Tasks with unit-exponential service time distributions arrive at each vertex as independent Poisson
DEFF Research Database (Denmark)
Kucharik, Marcel; Hofacker, Ivo; Stadler, Peter
2014-01-01
of the folding free energy landscape, however, can provide the relevant information. Results We introduce the basin hopping graph (BHG) as a novel coarse-grained model of folding landscapes. Each vertex of the BHG is a local minimum, which represents the corresponding basin in the landscape. Its edges connect...
Improving Machining Accuracy of CNC Machines with Innovative Design Methods
Yemelyanov, N. V.; Yemelyanova, I. V.; Zubenko, V. L.
2018-03-01
The article considers achieving the machining accuracy of CNC machines by applying innovative methods in modelling and design of machining systems, drives and machine processes. The topological method of analysis involves visualizing the system as matrices of block graphs with a varying degree of detail between the upper and lower hierarchy levels. This approach combines the advantages of graph theory and the efficiency of decomposition methods, it also has visual clarity, which is inherent in both topological models and structural matrices, as well as the resiliency of linear algebra as part of the matrix-based research. The focus of the study is on the design of automated machine workstations, systems, machines and units, which can be broken into interrelated parts and presented as algebraic, topological and set-theoretical models. Every model can be transformed into a model of another type, and, as a result, can be interpreted as a system of linear and non-linear equations which solutions determine the system parameters. This paper analyses the dynamic parameters of the 1716PF4 machine at the stages of design and exploitation. Having researched the impact of the system dynamics on the component quality, the authors have developed a range of practical recommendations which have enabled one to reduce considerably the amplitude of relative motion, exclude some resonance zones within the spindle speed range of 0...6000 min-1 and improve machining accuracy.
The STAPL Parallel Graph Library
Harshvardhan,; Fidel, Adam; Amato, Nancy M.; Rauchwerger, Lawrence
2013-01-01
This paper describes the stapl Parallel Graph Library, a high-level framework that abstracts the user from data-distribution and parallelism details and allows them to concentrate on parallel graph algorithm development. It includes a customizable
Directory of Open Access Journals (Sweden)
Marc-Thorsten Hütt
2012-06-01
Full Text Available Cellular automata (CA are a remarkably efficient tool for exploring general properties of complex systems and spatiotemporal patterns arising from local rules. Totalistic cellular automata, where the update rules depend only on the density of neighboring states, are at the same time a versatile tool for exploring dynamical processes on graphs. Here we briefly review our previous results on cellular automata on graphs, emphasizing some systematic relationships between network architecture and dynamics identified in this way. We then extend the investigation towards graphs obtained in a simulated-evolution procedure, starting from Erdő s–Rényi (ER graphs and selecting for low entropies of the CA dynamics. Our key result is a strong association of low Shannon entropies with a broadening of the graph’s degree distribution.
State of the art in nuclear telerobotics: focus on the man/machine connection
Greaves, Amna E.
1995-12-01
The interface between the human controller and remotely operated device is a crux of telerobotic investigation today. This human-to-machine connection is the means by which we communicate our commands to the device, as well as the medium for decision-critical feedback to the operator. The amount of information transferred through the user interface is growing. This can be seen as a direct result of our need to support added complexities, as well as a rapidly expanding domain of applications. A user interface, or UI, is therefore subject to increasing demands to present information in a meaningful manner to the user. Virtual reality, and multi degree-of-freedom input devices lend us the ability to augment the man/machine interface, and handle burgeoning amounts of data in a more intuitive and anthropomorphically correct manner. Along with the aid of 3-D input and output devices, there are several visual tools that can be employed as part of a graphical UI that enhance and accelerate our comprehension of the data being presented. Thus an advanced UI that features these improvements would reduce the amount of fatigue on the teleoperator, increase his level of safety, facilitate learning, augment his control, and potentially reduce task time. This paper investigates the cutting edge concepts and enhancements that lead to the next generation of telerobotic interface systems.
Energy Technology Data Exchange (ETDEWEB)
Winlaw, Manda [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); De Sterck, Hans [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Sanders, Geoffrey [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2015-10-26
In very simple terms a network can be de ned as a collection of points joined together by lines. Thus, networks can be used to represent connections between entities in a wide variety of elds including engi- neering, science, medicine, and sociology. Many large real-world networks share a surprising number of properties, leading to a strong interest in model development research and techniques for building synthetic networks have been developed, that capture these similarities and replicate real-world graphs. Modeling these real-world networks serves two purposes. First, building models that mimic the patterns and prop- erties of real networks helps to understand the implications of these patterns and helps determine which patterns are important. If we develop a generative process to synthesize real networks we can also examine which growth processes are plausible and which are not. Secondly, high-quality, large-scale network data is often not available, because of economic, legal, technological, or other obstacles [7]. Thus, there are many instances where the systems of interest cannot be represented by a single exemplar network. As one example, consider the eld of cybersecurity, where systems require testing across diverse threat scenarios and validation across diverse network structures. In these cases, where there is no single exemplar network, the systems must instead be modeled as a collection of networks in which the variation among them may be just as important as their common features. By developing processes to build synthetic models, so-called graph generators, we can build synthetic networks that capture both the essential features of a system and realistic variability. Then we can use such synthetic graphs to perform tasks such as simulations, analysis, and decision making. We can also use synthetic graphs to performance test graph analysis algorithms, including clustering algorithms and anomaly detection algorithms.
Groupies in multitype random graphs
Shang, Yilun
2016-01-01
A groupie in a graph is a vertex whose degree is not less than the average degree of its neighbors. Under some mild conditions, we show that the proportion of groupies is very close to 1/2 in multitype random graphs (such as stochastic block models), which include Erd?s-R?nyi random graphs, random bipartite, and multipartite graphs as special examples. Numerical examples are provided to illustrate the theoretical results.
Groupies in multitype random graphs.
Shang, Yilun
2016-01-01
A groupie in a graph is a vertex whose degree is not less than the average degree of its neighbors. Under some mild conditions, we show that the proportion of groupies is very close to 1/2 in multitype random graphs (such as stochastic block models), which include Erdős-Rényi random graphs, random bipartite, and multipartite graphs as special examples. Numerical examples are provided to illustrate the theoretical results.
Temporal Representation in Semantic Graphs
Energy Technology Data Exchange (ETDEWEB)
Levandoski, J J; Abdulla, G M
2007-08-07
A wide range of knowledge discovery and analysis applications, ranging from business to biological, make use of semantic graphs when modeling relationships and concepts. Most of the semantic graphs used in these applications are assumed to be static pieces of information, meaning temporal evolution of concepts and relationships are not taken into account. Guided by the need for more advanced semantic graph queries involving temporal concepts, this paper surveys the existing work involving temporal representations in semantic graphs.
Young, Sean Gregory
The complex interactions between human health and the physical landscape and environment have been recognized, if not fully understood, since the ancient Greeks. Landscape epidemiology, sometimes called spatial epidemiology, is a sub-discipline of medical geography that uses environmental conditions as explanatory variables in the study of disease or other health phenomena. This theory suggests that pathogenic organisms (whether germs or larger vector and host species) are subject to environmental conditions that can be observed on the landscape, and by identifying where such organisms are likely to exist, areas at greatest risk of the disease can be derived. Machine learning is a sub-discipline of artificial intelligence that can be used to create predictive models from large and complex datasets. West Nile virus (WNV) is a relatively new infectious disease in the United States, and has a fairly well-understood transmission cycle that is believed to be highly dependent on environmental conditions. This study takes a geospatial approach to the study of WNV risk, using both landscape epidemiology and machine learning techniques. A combination of remotely sensed and in situ variables are used to predict WNV incidence with a correlation coefficient as high as 0.86. A novel method of mitigating the small numbers problem is also tested and ultimately discarded. Finally a consistent spatial pattern of model errors is identified, indicating the chosen variables are capable of predicting WNV disease risk across most of the United States, but are inadequate in the northern Great Plains region of the US.
Reder, Leonard J.; Booth, Andrew; Hsieh, Jonathan; Summers, Kellee R.
2004-09-01
This paper presents a discussion of the evolution of a sequencer from a simple Experimental Physics and Industrial Control System (EPICS) based sequencer into a complex implementation designed utilizing UML (Unified Modeling Language) methodologies and a Computer Aided Software Engineering (CASE) tool approach. The main purpose of the Interferometer Sequencer (called the IF Sequencer) is to provide overall control of the Keck Interferometer to enable science operations to be carried out by a single operator (and/or observer). The interferometer links the two 10m telescopes of the W. M. Keck Observatory at Mauna Kea, Hawaii. The IF Sequencer is a high-level, multi-threaded, Harel finite state machine software program designed to orchestrate several lower-level hardware and software hard real-time subsystems that must perform their work in a specific and sequential order. The sequencing need not be done in hard real-time. Each state machine thread commands either a high-speed real-time multiple mode embedded controller via CORBA, or slower controllers via EPICS Channel Access interfaces. The overall operation of the system is simplified by the automation. The UML is discussed and our use of it to implement the sequencer is presented. The decision to use the Rhapsody product as our CASE tool is explained and reflected upon. Most importantly, a section on lessons learned is presented and the difficulty of integrating CASE tool automatically generated C++ code into a large control system consisting of multiple infrastructures is presented.
A generalization of total graphs
Indian Academy of Sciences (India)
M Afkhami
2018-04-12
Apr 12, 2018 ... product of any lower triangular matrix with the transpose of any element of U belongs to U. The ... total graph of R, which is denoted by T( (R)), is a simple graph with all elements of R as vertices, and ...... [9] Badawi A, On dot-product graph of a commutative ring, Communications in Algebra 43 (2015). 43–50.
Graph transformation tool contest 2008
Rensink, Arend; van Gorp, Pieter
This special section is the outcome of the graph transformation tool contest organised during the Graph-Based Tools (GraBaTs) 2008 workshop, which took place as a satellite event of the International Conference on Graph Transformation (ICGT) 2008. The contest involved two parts: three “off-line case
On dominator colorings in graphs
Indian Academy of Sciences (India)
colors required for a dominator coloring of G is called the dominator .... Theorem 1.3 shows that the complete graph Kn is the only connected graph of order n ... Conversely, if a graph G satisfies condition (i) or (ii), it is easy to see that χd(G) =.
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.
A Bond Graph Approach for the Modeling and Simulation of a Buck Converter
Directory of Open Access Journals (Sweden)
Rached Zrafi
2018-01-01
Full Text Available This paper deals with the modeling of bond graph buck converter systems. The bond graph formalism, which represents a heterogeneous formalism for physical modeling, is used to design a sub-model of a power MOSFET and PiN diode switchers. These bond graph models are based on the device’s electrical elements. The application of these models to a bond graph buck converter permit us to obtain an invariant causal structure when the switch devices change state. This paper shows the usefulness of the bond graph device’s modeling to simulate an implicit bond graph buck converter.
Energy Technology Data Exchange (ETDEWEB)
Jiang, Huaiguang; Zhang, Yingchen
2016-11-14
This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.
Connected Colourings of Complete Graphs and Hypergraphs
Leader, Imre; Tan, Ta Sheng
2014-01-01
Gallai's colouring theorem states that if the edges of a complete graph are 3-coloured, with each colour class forming a connected (spanning) subgraph, then there is a triangle that has all 3 colours. What happens for more colours: if we $k$-colour the edges of the complete graph, with each colour class connected, how many of the $\\binom{k}{3}$ triples of colours must appear as triangles? In this note we show that the `obvious' conjecture, namely that there are always at least $\\binom{k-1}{2}...
Efficient Graph Computation for Node2Vec
Zhou, Dongyan; Niu, Songjie; Chen, Shimin
2018-01-01
Node2Vec is a state-of-the-art general-purpose feature learning method for network analysis. However, current solutions cannot run Node2Vec on large-scale graphs with billions of vertices and edges, which are common in real-world applications. The existing distributed Node2Vec on Spark incurs significant space and time overhead. It runs out of memory even for mid-sized graphs with millions of vertices. Moreover, it considers at most 30 edges for every vertex in generating random walks, causin...
Graph-based semi-supervised learning
Subramanya, Amarnag
2014-01-01
While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer visi
Algorithms for Planar Graphs and Graphs in Metric Spaces
DEFF Research Database (Denmark)
Wulff-Nilsen, Christian
structural properties that can be exploited. For instance, a road network or a wire layout on a microchip is typically (near-)planar and distances in the network are often defined w.r.t. the Euclidean or the rectilinear metric. Specialized algorithms that take advantage of such properties are often orders...... of magnitude faster than the corresponding algorithms for general graphs. The first and main part of this thesis focuses on the development of efficient planar graph algorithms. The most important contributions include a faster single-source shortest path algorithm, a distance oracle with subquadratic...... for geometric graphs and graphs embedded in metric spaces. Roughly speaking, the stretch factor is a real value expressing how well a (geo-)metric graph approximates the underlying complete graph w.r.t. distances. We give improved algorithms for computing the stretch factor of a given graph and for augmenting...
Generating Realistic Labelled, Weighted Random Graphs
Directory of Open Access Journals (Sweden)
Michael Charles Davis
2015-12-01
Full Text Available Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs. Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.
Sørensen, Lauge; Nielsen, Mads
2018-05-15
The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.
Herdable Systems Over Signed, Directed Graphs
Ruf, Sebastian F.
2018-04-11
This paper considers the notion of herdability, a set-based reachability condition, which asks whether the state of a system can be controlled to be element-wise larger than a non-negative threshold. The basic theory of herdable systems is presented, including a necessary and sufficient condition for herdability. This paper then considers the impact of the underlying graph structure of a linear system on the herdability of the system, for the case where the graph is represented as signed and directed. By classifying nodes based on the length and sign of walks from an input, we find a class of completely herdable systems as well as provide a complete characterization of nodes that can be herded in systems with an underlying graph that is a directed out-branching rooted at a single input.
Herdable Systems Over Signed, Directed Graphs
Ruf, Sebastian F.; Egerstedt, Magnus; Shamma, Jeff S.
2018-01-01
This paper considers the notion of herdability, a set-based reachability condition, which asks whether the state of a system can be controlled to be element-wise larger than a non-negative threshold. The basic theory of herdable systems is presented, including a necessary and sufficient condition for herdability. This paper then considers the impact of the underlying graph structure of a linear system on the herdability of the system, for the case where the graph is represented as signed and directed. By classifying nodes based on the length and sign of walks from an input, we find a class of completely herdable systems as well as provide a complete characterization of nodes that can be herded in systems with an underlying graph that is a directed out-branching rooted at a single input.
graphkernels: R and Python packages for graph comparison.
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.
Harary, Frank
2015-01-01
Presented in 1962-63 by experts at University College, London, these lectures offer a variety of perspectives on graph theory. Although the opening chapters form a coherent body of graph theoretic concepts, this volume is not a text on the subject but rather an introduction to the extensive literature of graph theory. The seminar's topics are geared toward advanced undergraduate students of mathematics.Lectures by this volume's editor, Frank Harary, include ""Some Theorems and Concepts of Graph Theory,"" ""Topological Concepts in Graph Theory,"" ""Graphical Reconstruction,"" and other introduc
Spectral fluctuations of quantum graphs
International Nuclear Information System (INIS)
Pluhař, Z.; Weidenmüller, H. A.
2014-01-01
We prove the Bohigas-Giannoni-Schmit conjecture in its most general form for completely connected simple graphs with incommensurate bond lengths. We show that for graphs that are classically mixing (i.e., graphs for which the spectrum of the classical Perron-Frobenius operator possesses a finite gap), the generating functions for all (P,Q) correlation functions for both closed and open graphs coincide (in the limit of infinite graph size) with the corresponding expressions of random-matrix theory, both for orthogonal and for unitary symmetry
Dynamic Representations of Sparse Graphs
DEFF Research Database (Denmark)
Brodal, Gerth Stølting; Fagerberg, Rolf
1999-01-01
We present a linear space data structure for maintaining graphs with bounded arboricity—a large class of sparse graphs containing e.g. planar graphs and graphs of bounded treewidth—under edge insertions, edge deletions, and adjacency queries. The data structure supports adjacency queries in worst...... case O(c) time, and edge insertions and edge deletions in amortized O(1) and O(c+log n) time, respectively, where n is the number of nodes in the graph, and c is the bound on the arboricity....
Domination criticality in product graphs
Directory of Open Access Journals (Sweden)
M.R. Chithra
2015-07-01
Full Text Available A connected dominating set is an important notion and has many applications in routing and management of networks. Graph products have turned out to be a good model of interconnection networks. This motivated us to study the Cartesian product of graphs G with connected domination number, γc(G=2,3 and characterize such graphs. Also, we characterize the k−γ-vertex (edge critical graphs and k−γc-vertex (edge critical graphs for k=2,3 where γ denotes the domination number of G. We also discuss the vertex criticality in grids.
Graph Creation, Visualisation and Transformation
Directory of Open Access Journals (Sweden)
Maribel Fernández
2010-03-01
Full Text Available We describe a tool to create, edit, visualise and compute with interaction nets - a form of graph rewriting systems. The editor, called GraphPaper, allows users to create and edit graphs and their transformation rules using an intuitive user interface. The editor uses the functionalities of the TULIP system, which gives us access to a wealth of visualisation algorithms. Interaction nets are not only a formalism for the specification of graphs, but also a rewrite-based computation model. We discuss graph rewriting strategies and a language to express them in order to perform strategic interaction net rewriting.
Study of Chromatic parameters of Line, Total, Middle graphs and Graph operators of Bipartite graph
Nagarathinam, R.; Parvathi, N.
2018-04-01
Chromatic parameters have been explored on the basis of graph coloring process in which a couple of adjacent nodes receives different colors. But the Grundy and b-coloring executes maximum colors under certain restrictions. In this paper, Chromatic, b-chromatic and Grundy number of some graph operators of bipartite graph has been investigat
CERN. Geneva
2015-01-01
Applied statistics, aka ‘Machine Learning’, offers a wealth of techniques for answering security questions. It’s a much hyped topic in the big data world, with many companies now providing machine learning as a service. This talk will demystify these techniques, explain the math, and demonstrate their application to security problems. The presentation will include how-to’s on classifying malware, looking into encrypted tunnels, and finding botnets in DNS data. About the speaker Josiah is a security researcher with HP TippingPoint DVLabs Research Group. He has over 15 years of professional software development experience. Josiah used to do AI, with work focused on graph theory, search, and deductive inference on large knowledge bases. As rules only get you so far, he moved from AI to using machine learning techniques identifying failure modes in email traffic. There followed digressions into clustered data storage and later integrated control systems. Current ...
Angu, Rittu; Mehta, R. K.
2018-04-01
This paper presents a robust controller known as Extended State Observer (ESO) in order to improve the stability and voltage regulation of a synchronous machine connected to an infinite bus power system through a transmission line. The ESO-based control scheme is implemented with an automatic voltage regulator in conjunction with an excitation system to enhance the damping of low frequency power system oscillations, as the Power System Stabilizer (PSS) does. The implementation of PSS excitation control techniques however requires reliable information about the entire states, though they are not always directly measureable. To address this issue, the proposed ESO provides the estimate of system states as well as disturbance state together in order to improve not only the damping but also compensates system efficiently in presence of parameter uncertainties and external disturbances. The Closed-Loop Poles (CLPs) of the system have been assigned by the symmetric root locus technique, with the desired level of system damping provided by the dominant CLPs. The performance of the system is analyzed through simulating at different operating conditions. The control method is not only capable of providing zero estimation error in steady-state, but also shows robustness in tracking the reference command under parametric variations and external disturbances. Illustrative examples have been provided to demonstrate the effectiveness of the developed methodology.
Mizan: Optimizing Graph Mining in Large Parallel Systems
Kalnis, Panos
2012-03-01
Extracting information from graphs, from nding shortest paths to complex graph mining, is essential for many ap- plications. Due to the shear size of modern graphs (e.g., social networks), processing must be done on large paral- lel computing infrastructures (e.g., the cloud). Earlier ap- proaches relied on the MapReduce framework, which was proved inadequate for graph algorithms. More recently, the message passing model (e.g., Pregel) has emerged. Although the Pregel model has many advantages, it is agnostic to the graph properties and the architecture of the underlying com- puting infrastructure, leading to suboptimal performance. In this paper, we propose Mizan, a layer between the users\\' code and the computing infrastructure. Mizan considers the structure of the input graph and the architecture of the in- frastructure in order to: (i) decide whether it is bene cial to generate a near-optimal partitioning of the graph in a pre- processing step, and (ii) choose between typical point-to- point message passing and a novel approach that puts com- puting nodes in a virtual overlay ring. We deployed Mizan on a small local Linux cluster, on the cloud (256 virtual machines in Amazon EC2), and on an IBM Blue Gene/P supercomputer (1024 CPUs). We show that Mizan executes common algorithms on very large graphs 1-2 orders of mag- nitude faster than MapReduce-based implementations and up to one order of magnitude faster than implementations relying on Pregel-like hash-based graph partitioning.
Graph Sampling for Covariance Estimation
Chepuri, Sundeep Prabhakar
2017-04-25
In this paper the focus is on subsampling as well as reconstructing the second-order statistics of signals residing on nodes of arbitrary undirected graphs. Second-order stationary graph signals may be obtained by graph filtering zero-mean white noise and they admit a well-defined power spectrum whose shape is determined by the frequency response of the graph filter. Estimating the graph power spectrum forms an important component of stationary graph signal processing and related inference tasks such as Wiener prediction or inpainting on graphs. The central result of this paper is that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the second-order statistics of the graph signal from the subsampled observations, and more importantly, without any spectral priors. To this end, both a nonparametric approach as well as parametric approaches including moving average and autoregressive models for the graph power spectrum are considered. The results specialize for undirected circulant graphs in that the graph nodes leading to the best compression rates are given by the so-called minimal sparse rulers. A near-optimal greedy algorithm is developed to design the subsampling scheme for the non-parametric and the moving average models, whereas a particular subsampling scheme that allows linear estimation for the autoregressive model is proposed. Numerical experiments on synthetic as well as real datasets related to climatology and processing handwritten digits are provided to demonstrate the developed theory.
Hendrix, William; Jenkins, John; Padmanabhan, Kanchana; Chakraborty, Arpan
2014-01-01
Practical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs. Hands-On Application of Graph Data Mining Each chapter in the book focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through applications using real data sets, the book demonstrates how computational techniques can help solve real-world problems. The applications covered include network intrusion detection, tumor cell diagnostics, face recognition, predictive toxicology, mining metabolic and protein-protein interaction networks, and community detection in social networks. De...
Canonical Labelling of Site Graphs
Directory of Open Access Journals (Sweden)
Nicolas Oury
2013-06-01
Full Text Available We investigate algorithms for canonical labelling of site graphs, i.e. graphs in which edges bind vertices on sites with locally unique names. We first show that the problem of canonical labelling of site graphs reduces to the problem of canonical labelling of graphs with edge colourings. We then present two canonical labelling algorithms based on edge enumeration, and a third based on an extension of Hopcroft's partition refinement algorithm. All run in quadratic worst case time individually. However, one of the edge enumeration algorithms runs in sub-quadratic time for graphs with "many" automorphisms, and the partition refinement algorithm runs in sub-quadratic time for graphs with "few" bisimulation equivalences. This suite of algorithms was chosen based on the expectation that graphs fall in one of those two categories. If that is the case, a combined algorithm runs in sub-quadratic worst case time. Whether this expectation is reasonable remains an interesting open problem.
Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow.
Wongsuphasawat, Kanit; Smilkov, Daniel; Wexler, James; Wilson, Jimbo; Mane, Dandelion; Fritz, Doug; Krishnan, Dilip; Viegas, Fernanda B; Wattenberg, Martin
2018-01-01
We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the graph, we decouple non-critical nodes from the layout. To provide an overview, we build a clustered graph using the hierarchical structure annotated in the source code. To support exploration of nested structure on demand, we perform edge bundling to enable stable and responsive cluster expansion. Finally, we detect and highlight repeated structures to emphasize a model's modular composition. To demonstrate the utility of the visualizer, we describe example usage scenarios and report user feedback. Overall, users find the visualizer useful for understanding, debugging, and sharing the structures of their models.
Learning heat diffusion graphs
Thanou, Dorina; Dong, Xiaowen; Kressner, Daniel; Frossard, Pascal
2016-01-01
Effective information analysis generally boils down to properly identifying the structure or geometry of the data, which is often represented by a graph. In some applications, this structure may be partly determined by design constraints or pre-determined sensing arrangements, like in road transportation networks for example. In general though, the data structure is not readily available and becomes pretty difficult to define. In particular, the global smoothness assumptions, that most of the...
Syed, M. Qasim; Lovatt, Ian
2014-01-01
This paper is an addition to the series of papers on the exponential function begun by Albert Bartlett. In particular, we ask how the graph of the exponential function y = e[superscript -t/t] would appear if y were plotted versus ln t rather than the normal practice of plotting ln y versus t. In answering this question, we find a new way to…
Understanding Charts and Graphs.
1987-07-28
Farenheit degrees, which have no Onaturalo zero ); finally, ratio scales have numbers that are ordered so that the magnitudes of differences are important and...system. They have to do with the very nature of how marks serve as meaningful symbols. In the ideal case, a chart or graph will be absolutely unambiguous...and these laws comprise this principle (see Stevens, 1974). Absolute discriminability: A minimal magnitude of a mark is necessary for it to be detected
Pontier, Matthijs
2015-01-01
The essays in this book, written by researchers from both humanities and sciences, describe various theoretical and experimental approaches to adding medical ethics to a machine in medical settings. Medical machines are in close proximity with human beings, and getting closer: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. In such contexts, machines are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for e...
Energy Technology Data Exchange (ETDEWEB)
Ylen, J P; Jutila, P [Helsinki Univ. of Technology, Otaniemi (Finland)
1999-12-31
The chemical state of paper mass is considered to be a key factor to the smooth operation of the paper machine. There are simulators that have been developed either for dynamic energy and mass balances or for static chemical phenomena, but the combination of these is not a straight forward task. Control Engineering Laboratory of Helsinki University of Technology has studied the paper machine wet end phenomena with the emphasis on pH-modelling. VTT (Technical Research Centre of Finland) Process Physics has used thermodynamical modelling successfully in e.g. Bleaching processes. In this research the different approaches are combined in order to get reliable dynamical models and modelling procedures for various unit operations. A flexible pilot process will be constructed and different materials will be processed starting from simple inorganic substances (e.g. Calcium carbonate and distilled water) working towards more complex masses (thick pulp with process waters and various reagents). The pilot process is well instrumented with ion selective electrodes, total calcium analysator and all basic measurements. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Ylen, J.P.; Jutila, P. [Helsinki Univ. of Technology, Otaniemi (Finland)
1998-12-31
The chemical state of paper mass is considered to be a key factor to the smooth operation of the paper machine. There are simulators that have been developed either for dynamic energy and mass balances or for static chemical phenomena, but the combination of these is not a straight forward task. Control Engineering Laboratory of Helsinki University of Technology has studied the paper machine wet end phenomena with the emphasis on pH-modelling. VTT (Technical Research Centre of Finland) Process Physics has used thermodynamical modelling successfully in e.g. Bleaching processes. In this research the different approaches are combined in order to get reliable dynamical models and modelling procedures for various unit operations. A flexible pilot process will be constructed and different materials will be processed starting from simple inorganic substances (e.g. Calcium carbonate and distilled water) working towards more complex masses (thick pulp with process waters and various reagents). The pilot process is well instrumented with ion selective electrodes, total calcium analysator and all basic measurements. (orig.)
Finding the optimal Bayesian network given a constraint graph
Directory of Open Access Journals (Sweden)
Jacob M. Schreiber
2017-07-01
Full Text Available Despite recent algorithmic improvements, learning the optimal structure of a Bayesian network from data is typically infeasible past a few dozen variables. Fortunately, domain knowledge can frequently be exploited to achieve dramatic computational savings, and in many cases domain knowledge can even make structure learning tractable. Several methods have previously been described for representing this type of structural prior knowledge, including global orderings, super-structures, and constraint rules. While super-structures and constraint rules are flexible in terms of what prior knowledge they can encode, they achieve savings in memory and computational time simply by avoiding considering invalid graphs. We introduce the concept of a “constraint graph” as an intuitive method for incorporating rich prior knowledge into the structure learning task. We describe how this graph can be used to reduce the memory cost and computational time required to find the optimal graph subject to the encoded constraints, beyond merely eliminating invalid graphs. In particular, we show that a constraint graph can break the structure learning task into independent subproblems even in the presence of cyclic prior knowledge. These subproblems are well suited to being solved in parallel on a single machine or distributed across many machines without excessive communication cost.
Exclusivity structures and graph representatives of local complementation orbits
Cabello, Adán; Parker, Matthew G.; Scarpa, Giannicola; Severini, Simone
2013-07-01
We describe a construction that maps any connected graph G on three or more vertices into a larger graph, H(G), whose independence number is strictly smaller than its Lovász number which is equal to its fractional packing number. The vertices of H(G) represent all possible events consistent with the stabilizer group of the graph state associated with G, and exclusive events are adjacent. Mathematically, the graph H(G) corresponds to the orbit of G under local complementation. Physically, the construction translates into graph-theoretic terms the connection between a graph state and a Bell inequality maximally violated by quantum mechanics. In the context of zero-error information theory, the construction suggests a protocol achieving the maximum rate of entanglement-assisted capacity, a quantum mechanical analogue of the Shannon capacity, for each H(G). The violation of the Bell inequality is expressed by the one-shot version of this capacity being strictly larger than the independence number. Finally, given the correspondence between graphs and exclusivity structures, we are able to compute the independence number for certain infinite families of graphs with the use of quantum non-locality, therefore highlighting an application of quantum theory in the proof of a purely combinatorial statement.
Developing a software for tracking the memory states of the machines in the LHCb Filter Farm
Jain, Harshit
2017-01-01
The LHCb Event Filter Farm consists of more than 1500 server nodes with a total amount of roughly 65 TB operating memory .The memory is crucial for the success of the LHCb experiment, since the proton-proton collisions are temporarily stored on these memory modules. Unfortunately, the aging nodes of the server farm occasionally suffer losses of their memory modules. The lower the available memory, the lower performance we can get out of it. Inducing the users or administrators to pay attention to this matter is inefficient. One needs to upgrade it to an acceptable way. The aim of this project was to develop a software to monitor a set of test machines. The software stores the data of the memory sticks in advance in a database which will be used for future reference. Then it checks the memory sticks at a future time instant to find any failures. In the case of any such losses the software looks up in the database to find out which memory sticks have lost and displays all information of those sticks in a log fi...
Machine Protection and High Energy Density States in Matter for High Energy Hadron Accelerators
Blanco Sancho, Juan; Schmidt, R
The Large Hadron Collider (LHC) is the largest accelerator in the world. It is designed to collide two proton beams with unprecedented particle energy of 7TeV. The energy stored in each beam is 362MJ, sufficient to melt 500kg of copper. An accidental release of even a small fraction of the beam energy can result in severe damage to the equipment. Machine protection systems are essential to safely operate the accelerator and handle all possible accidents. This thesis deals with the study of different failure scenarios and its possible consequences. It addresses failure scenarios ranging from low intensity losses on high-Z materials and superconductors to high intensity losses on carbon and copper collimators. Low beam losses are sufficient to quench the superconducting magnets and the stabilized superconducting cables (bus-bars) that connects the main magnets. If this occurs and the energy from the bus-bar is not extracted fast enough it can lead to a situation similar to the accident in 2008 at LHC during pow...
State of the art and future challenges for Machine Protection Systems
Wenninger, J
2014-01-01
Current frontier accelerators explore regimes of increasing power and stored energy, with beam energies spanning more than three orders of magnitude from the GeV to theTeV scale. In many cases the high beam power has to cohabit with superconducting equipment in the form of magnets or RF cavities requiring careful control of losses and of halos to mitigate quenches. Despite their large diversity in physics goals and operation modes, all facilities depend on their Machine Protection Systems (MPS) for safe and efficient running. This presentation will aim to give an overview of current MPS and on how the MPS act on or control the beams. Lessons from the LHC and other accelerators show that ever tighter monitoring of accelerator equipment and of beam parameters is required in the future. Such new monitoring systems must not only be very accurate but also be extremely reliable to minimize false alarms. Novel MPS ideas and concepts for linear colliders, high intensity hadron accelerators and to other high power acc...
Bayesian networks modeling for thermal error of numerical control machine tools
Institute of Scientific and Technical Information of China (English)
Xin-hua YAO; Jian-zhong FU; Zi-chen CHEN
2008-01-01
The interaction between the heat source location,its intensity,thermal expansion coefficient,the machine system configuration and the running environment creates complex thermal behavior of a machine tool,and also makes thermal error prediction difficult.To address this issue,a novel prediction method for machine tool thermal error based on Bayesian networks (BNs) was presented.The method described causal relationships of factors inducing thermal deformation by graph theory and estimated the thermal error by Bayesian statistical techniques.Due to the effective combination of domain knowledge and sampled data,the BN method could adapt to the change of running state of machine,and obtain satisfactory prediction accuracy.Ex-periments on spindle thermal deformation were conducted to evaluate the modeling performance.Experimental results indicate that the BN method performs far better than the least squares(LS)analysis in terms of modeling estimation accuracy.
Operation of a quantum dot in the finite-state machine mode: Single-electron dynamic memory
Energy Technology Data Exchange (ETDEWEB)
Klymenko, M. V. [Department of Chemistry, University of Liège, B4000 Liège (Belgium); Klein, M. [The Fritz Haber Center for Molecular Dynamics and the Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904 (Israel); Levine, R. D. [The Fritz Haber Center for Molecular Dynamics and the Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904 (Israel); Crump Institute for Molecular Imaging and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine and Department of Chemistry and Biochemistry, University of California, Los Angeles, California 90095 (United States); Remacle, F., E-mail: fremacle@ulg.ac.be [Department of Chemistry, University of Liège, B4000 Liège (Belgium); The Fritz Haber Center for Molecular Dynamics and the Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904 (Israel)
2016-07-14
A single electron dynamic memory is designed based on the non-equilibrium dynamics of charge states in electrostatically defined metallic quantum dots. Using the orthodox theory for computing the transfer rates and a master equation, we model the dynamical response of devices consisting of a charge sensor coupled to either a single and or a double quantum dot subjected to a pulsed gate voltage. We show that transition rates between charge states in metallic quantum dots are characterized by an asymmetry that can be controlled by the gate voltage. This effect is more pronounced when the switching between charge states corresponds to a Markovian process involving electron transport through a chain of several quantum dots. By simulating the dynamics of electron transport we demonstrate that the quantum box operates as a finite-state machine that can be addressed by choosing suitable shapes and switching rates of the gate pulses. We further show that writing times in the ns range and retention memory times six orders of magnitude longer, in the ms range, can be achieved on the double quantum dot system using experimentally feasible parameters, thereby demonstrating that the device can operate as a dynamic single electron memory.
Operation of a quantum dot in the finite-state machine mode: Single-electron dynamic memory
International Nuclear Information System (INIS)
Klymenko, M. V.; Klein, M.; Levine, R. D.; Remacle, F.
2016-01-01
A single electron dynamic memory is designed based on the non-equilibrium dynamics of charge states in electrostatically defined metallic quantum dots. Using the orthodox theory for computing the transfer rates and a master equation, we model the dynamical response of devices consisting of a charge sensor coupled to either a single and or a double quantum dot subjected to a pulsed gate voltage. We show that transition rates between charge states in metallic quantum dots are characterized by an asymmetry that can be controlled by the gate voltage. This effect is more pronounced when the switching between charge states corresponds to a Markovian process involving electron transport through a chain of several quantum dots. By simulating the dynamics of electron transport we demonstrate that the quantum box operates as a finite-state machine that can be addressed by choosing suitable shapes and switching rates of the gate pulses. We further show that writing times in the ns range and retention memory times six orders of magnitude longer, in the ms range, can be achieved on the double quantum dot system using experimentally feasible parameters, thereby demonstrating that the device can operate as a dynamic single electron memory.
Nonexistence of a universal quantum machine to examine the precision of unknown quantum states
International Nuclear Information System (INIS)
Pang, Shengshi; Wu, Shengjun; Chen, Zeng-Bing
2011-01-01
In this work, we reveal a type of impossibility discovered in our recent research which forbids comparing the closeness of multiple unknown quantum states with any nontrivial threshold in a perfect or unambiguous way. This impossibility is distinct from the existing impossibilities in that it is a ''collective'' impossibility on multiple quantum states; most other ''no-go'' theorems are concerned with only one single state each time, i.e., it is an impossibility on a nonlocal quantum operation. This impossibility may provide new insight into the nature of quantum mechanics, and it implies more limitations on quantum information tasks than the existing no-go theorems.
Directory of Open Access Journals (Sweden)
Xianglin ZHU
2014-06-01
Full Text Available To resolve the online detection difficulty of some important state variables in fermentation process with traditional instruments, a soft sensing modeling method based on relevance vector machine (RVM with a hybrid kernel function is presented. Based on the characteristic analysis of two commonly-used kernel functions, that is, local Gaussian kernel function and global polynomial kernel function, a hybrid kernel function combing merits of Gaussian kernel function and polynomial kernel function is constructed. To design optimal parameters of this kernel function, the particle swarm optimization (PSO algorithm is applied. The proposed modeling method is used to predict the value of cell concentration in the Lysine fermentation process. Simulation results show that the presented hybrid-kernel RVM model has a better accuracy and performance than the single kernel RVM model.
Directory of Open Access Journals (Sweden)
Ali Mehmood KHAN
2016-06-01
Full Text Available Recognizing emotional states is becoming a major part of a user's context for wearable computing applications. The system should be able to acquire a user's emotional states by using physiological sensors. We want to develop a personal emotional states recognition system that is practical, reliable, and can be used for health-care related applications. We propose to use the eHealth platform 1 which is a ready-made, light weight, small and easy to use device for recognizing a few emotional states like ‘Sad’, ‘Dislike’, ‘Joy’, ‘Stress’, ‘Normal’, ‘No-Idea’, ‘Positive’ and ‘Negative’ using decision tree (J48 and k-Nearest Neighbors (IBK classifiers. In this paper, we present an approach to build a system that exhibits this property and provides evidence based on data for 8 different emotional states collected from 24 different subjects. Our results indicate that the system has an accuracy rate of approximately 98 %. In our work, we used four physiological sensors i.e. ‘Blood Volume Pulse’ (BVP, ‘Electromyogram’ (EMG, ‘Galvanic Skin Response’ (GSR, and ‘Skin Temperature’ in order to recognize emotional states (i.e. Stress, Joy/Happy, Sad, Normal/Neutral, Dislike, No-idea, Positive and Negative.
Graphs cospectral with a friendship graph or its complement
Directory of Open Access Journals (Sweden)
Alireza Abdollahi
2013-12-01
Full Text Available Let $n$ be any positive integer and let $F_n$ be the friendship (or Dutch windmill graph with $2n+1$ vertices and $3n$ edges. Here we study graphs with the same adjacency spectrum as the $F_n$. Two graphs are called cospectral if the eigenvalues multiset of their adjacency matrices are the same. Let $G$ be a graph cospectral with $F_n$. Here we prove that if $G$ has no cycle of length $4$ or $5$, then $Gcong F_n$. Moreover if $G$ is connected and planar then $Gcong F_n$.All but one of connected components of $G$ are isomorphic to $K_2$.The complement $overline{F_n}$ of the friendship graph is determined by its adjacency eigenvalues, that is, if $overline{F_n}$ is cospectral with a graph $H$, then $Hcong overline{F_n}$.
A Hybrid Approach to Processing Big Data Graphs on Memory-Restricted Systems
Harshvardhan,
2015-05-01
With the advent of big-data, processing large graphs quickly has become increasingly important. Most existing approaches either utilize in-memory processing techniques that can only process graphs that fit completely in RAM, or disk-based techniques that sacrifice performance. In this work, we propose a novel RAM-Disk hybrid approach to graph processing that can scale well from a single shared-memory node to large distributed-memory systems. It works by partitioning the graph into sub graphs that fit in RAM and uses a paging-like technique to load sub graphs. We show that without modifying the algorithms, this approach can scale from small memory-constrained systems (such as tablets) to large-scale distributed machines with 16, 000+ cores.
Decomposing Oriented Graphs into Six Locally Irregular Oriented Graphs
DEFF Research Database (Denmark)
Bensmail, Julien; Renault, Gabriel
2016-01-01
An undirected graph G is locally irregular if every two of its adjacent vertices have distinct degrees. We say that G is decomposable into k locally irregular graphs if there exists a partition E1∪E2∪⋯∪Ek of the edge set E(G) such that each Ei induces a locally irregular graph. It was recently co...
Neuro-symbolic representation learning on biological knowledge graphs.
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.
Neuro-symbolic representation learning on biological knowledge graphs
Alshahrani, Mona
2017-04-21
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.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 SemanticWeb 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.
Localization in random bipartite graphs: Numerical and empirical study
Slanina, František
2017-05-01
We investigate adjacency matrices of bipartite graphs with a power-law degree distribution. Motivation for this study is twofold: first, vibrational states in granular matter and jammed sphere packings; second, graphs encoding social interaction, especially electronic commerce. We establish the position of the mobility edge and show that it strongly depends on the power in the degree distribution and on the ratio of the sizes of the two parts of the bipartite graph. At the jamming threshold, where the two parts have the same size, localization vanishes. We found that the multifractal spectrum is nontrivial in the delocalized phase, but still near the mobility edge. We also study an empirical bipartite graph, namely, the Amazon reviewer-item network. We found that in this specific graph the mobility edge disappears, and we draw a conclusion from this fact regarding earlier empirical studies of the Amazon network.
Synthesis of state observer and nonlinear output feedback controller design of AC machines
International Nuclear Information System (INIS)
Al-Tahir, Ali Abdul Razzaq
2016-01-01
The research work developed in this thesis has been mainly devoted to the observation and sensor-less control problems of electrical systems. Three major contributions have been carried out using the high - gain concept and output feedback adaptive nonlinear control for online UPS. In this thesis, we dealt with synthesis of sampled high - gain observers for nonlinear systems application to PMSMs and DFIGs. We particularly focus on two constraints: sampling effect and tracking unmeasured mechanical and magnetic state variables. The first contribution consists in a high gain observer design that performs a relatively accurate estimation of both mechanical and magnetic state variable using the available measurements on stator currents and voltages of PMSM. We propose a global exponential observer having state predictor for a class of nonlinear globally Lipschitz system. In second contribution, we proposed a novel non - standard HGO design for non-injective feedback relation application to variable speed DFIG based WPGS. Meanwhile, a reduced system model is analyzed, provided by observability test to check is it possible synthesis state observer for sensor-less control. In last contribution, an adaptive observer for states and parameters estimation are designed for a class of state - affine systems application to output feedback adaptive nonlinear control of three-phase AC/DC boost power converter for online UPS systems. Basically, the problem focused on cascade nonlinear adaptive controller that is developed making use Lyapunov theory. The parameters uncertainties are processed by the practical control laws under back-stepping design techniques with capacity of adaptation. (author)
Gao, Lin; Cheng, Wei; Zhang, Jinhua; Wang, Jue
2016-08-01
Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.
Directory of Open Access Journals (Sweden)
Stefano ePanzeri
2016-04-01
Full Text Available Brain-machine interfaces (BMIs can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brains. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.
Efficient Extraction of High Centrality Vertices in Distributed Graphs
Energy Technology Data Exchange (ETDEWEB)
Kumbhare, Alok [Univ. of Southern California, Los Angeles, CA (United States); Frincu, Marc [Univ. of Southern California, Los Angeles, CA (United States); Raghavendra, Cauligi S. [Univ. of Southern California, Los Angeles, CA (United States); Prasanna, Viktor K. [Univ. of Southern California, Los Angeles, CA (United States)
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 contribute 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.
Endomorphisms of graph algebras
DEFF Research Database (Denmark)
Conti, Roberto; Hong, Jeong Hee; Szymanski, Wojciech
2012-01-01
We initiate a systematic investigation of endomorphisms of graph C*-algebras C*(E), extending several known results on endomorphisms of the Cuntz algebras O_n. Most but not all of this study is focused on endomorphisms which permute the vertex projections and globally preserve the diagonal MASA D...... that the restriction to the diagonal MASA of an automorphism which globally preserves both D_E and the core AF-subalgebra eventually commutes with the corresponding one-sided shift. Secondly, we exhibit several properties of proper endomorphisms, investigate invertibility of localized endomorphisms both on C...
Yap, Hian-Poh
1996-01-01
This book provides an up-to-date and rapid introduction to an important and currently active topic in graph theory. The author leads the reader to the forefront of research in this area. Complete and easily readable proofs of all the main theorems, together with numerous examples, exercises and open problems are given. The book is suitable for use as a textbook or as seminar material for advanced undergraduate and graduate students. The references are comprehensive and so it will also be useful for researchers as a handbook.
Palittapongarnpim, Pantita; Sanders, Barry C.
2018-05-01
Quantum tomography infers quantum states from measurement data, but it becomes infeasible for large systems. Machine learning enables tomography of highly entangled many-body states and suggests a new powerful approach to this problem.
Graph Algorithm Animation with Grrr
Rodgers, Peter; Vidal, Natalia
2000-01-01
We discuss geometric positioning, highlighting of visited nodes and user defined highlighting that form the algorithm animation facilities in the Grrr graph rewriting programming language. The main purpose of animation was initially for the debugging and profiling of Grrr code, but recently it has been extended for the purpose of teaching algorithms to undergraduate students. The animation is restricted to graph based algorithms such as graph drawing, list manipulation or more traditional gra...
Generating a Tolerogenic Cell Therapy Knowledge Graph from Literature
Directory of Open Access Journals (Sweden)
Andre Lamurias
2017-11-01
Full Text Available Tolerogenic cell therapies provide an alternative to conventional immunosuppressive treatments of autoimmune disease and address, among other goals, the rejection of organ or stem cell transplants. Since various methodologies can be followed to develop tolerogenic therapies, it is important to be aware and up to date on all available studies that may be relevant to their improvement. Recently, knowledge graphs have been proposed to link various sources of information, using text mining techniques. Knowledge graphs facilitate the automatic retrieval of information about the topics represented in the graph. The objective of this work was to automatically generate a knowledge graph for tolerogenic cell therapy from biomedical literature. We developed a system, ICRel, based on machine learning to extract relations between cells and cytokines from abstracts. Our system retrieves related documents from PubMed, annotates each abstract with cell and cytokine named entities, generates the possible combinations of cell–cytokine pairs cooccurring in the same sentence, and identifies meaningful relations between cells and cytokines. The extracted relations were used to generate a knowledge graph, where each edge was supported by one or more documents. We obtained a graph containing 647 cell–cytokine relations, based on 3,264 abstracts. The modules of ICRel were evaluated with cross-validation and manual evaluation of the relations extracted. The relation extraction module obtained an F-measure of 0.789 in a reference database, while the manual evaluation obtained an accuracy of 0.615. Even though the knowledge graph is based on information that was already published in other articles about immunology, the system we present is more efficient than the laborious task of manually reading all the literature to find indirect or implicit relations. The ICRel graph will help experts identify implicit relations that may not be evident in published studies.
Optimization Problems on Threshold Graphs
Directory of Open Access Journals (Sweden)
Elena Nechita
2010-06-01
Full Text Available During the last three decades, different types of decompositions have been processed in the field of graph theory. Among these we mention: decompositions based on the additivity of some characteristics of the graph, decompositions where the adjacency law between the subsets of the partition is known, decompositions where the subgraph induced by every subset of the partition must have predeterminate properties, as well as combinations of such decompositions. In this paper we characterize threshold graphs using the weakly decomposition, determine: density and stability number, Wiener index and Wiener polynomial for threshold graphs.
Eulerian Graphs and Related Topics
Fleischner, Herbert
1990-01-01
The two volumes comprising Part 1 of this work embrace the theme of Eulerian trails and covering walks. They should appeal both to researchers and students, as they contain enough material for an undergraduate or graduate graph theory course which emphasizes Eulerian graphs, and thus can be read by any mathematician not yet familiar with graph theory. But they are also of interest to researchers in graph theory because they contain many recent results, some of which are only partial solutions to more general problems. A number of conjectures have been included as well. Various problems (such a
Energy Technology Data Exchange (ETDEWEB)
Maunz, Peter Lukas Wilhelm [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sterk, Jonathan David [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Lobser, Daniel [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Parekh, Ojas D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ryan-Anderson, Ciaran [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2016-01-01
In recent years, advanced network analytics have become increasingly important to na- tional security with applications ranging from cyber security to detection and disruption of ter- rorist networks. While classical computing solutions have received considerable investment, the development of quantum algorithms to address problems, such as data mining of attributed relational graphs, is a largely unexplored space. Recent theoretical work has shown that quan- tum algorithms for graph analysis can be more efficient than their classical counterparts. Here, we have implemented a trapped-ion-based two-qubit quantum information proces- sor to address these goals. Building on Sandia's microfabricated silicon surface ion traps, we have designed, realized and characterized a quantum information processor using the hyperfine qubits encoded in two 171 Yb + ions. We have implemented single qubit gates using resonant microwave radiation and have employed Gate set tomography (GST) to characterize the quan- tum process. For the first time, we were able to prove that the quantum process surpasses the fault tolerance thresholds of some quantum codes by demonstrating a diamond norm distance of less than 1 . 9 x 10 [?] 4 . We used Raman transitions in order to manipulate the trapped ions' motion and realize two-qubit gates. We characterized the implemented motion sensitive and insensitive single qubit processes and achieved a maximal process infidelity of 6 . 5 x 10 [?] 5 . We implemented the two-qubit gate proposed by Molmer and Sorensen and achieved a fidelity of more than 97 . 7%.
Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems
Hearn, Tristan A.
2015-01-01
This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.
Machine Shop Grinding Machines.
Dunn, James
This curriculum manual is one in a series of machine shop curriculum manuals intended for use in full-time secondary and postsecondary classes, as well as part-time adult classes. The curriculum can also be adapted to open-entry, open-exit programs. Its purpose is to equip students with basic knowledge and skills that will enable them to enter the…
Asymptote Misconception on Graphing Functions: Does Graphing Software Resolve It?
Directory of Open Access Journals (Sweden)
Mehmet Fatih Öçal
2017-01-01
Full Text Available Graphing function is an important issue in mathematics education due to its use in various areas of mathematics and its potential roles for students to enhance learning mathematics. The use of some graphing software assists students’ learning during graphing functions. However, the display of graphs of functions that students sketched by hand may be relatively different when compared to the correct forms sketched using graphing software. The possible misleading effects of this situation brought a discussion of a misconception (asymptote misconception on graphing functions. The purpose of this study is two- fold. First of all, this study investigated whether using graphing software (GeoGebra in this case helps students to determine and resolve this misconception in calculus classrooms. Second, the reasons for this misconception are sought. The multiple case study was utilized in this study. University students in two calculus classrooms who received instructions with (35 students or without GeoGebra assisted instructions (32 students were compared according to whether they fell into this misconception on graphing basic functions (1/x, lnx, ex. In addition, students were interviewed to reveal the reasons behind this misconception. Data were analyzed by means of descriptive and content analysis methods. The findings indicated that those who received GeoGebra assisted instruction were better in resolving it. In addition, the reasons behind this misconception were found to be teacher-based, exam-based and some other factors.
Medeiros, Gustavo Costa; Leppink, Eric W.; Yaemi, Ana; Mariani, Mirella; Tavares, Hermano; Grant, Jon E.
2015-01-01
Aims The objective of this paper is to perform a cross-cultural comparison of gambling disorder (GD) due to electronic gaming machines (EGM), a form of gambling that may have a high addictive potential. Our goal is to investigate two treatment-seeking samples of adults collected in Brazil and the United States, countries with different socio-cultural backgrounds. This comparison may lead to a better understanding of cultural influences on GD. Methods The total studied sample involved 733 treatment-seeking subjects: 353 men and 380 women (average age = 45.80, standard deviation ±10.9). The Brazilian sample had 517 individuals and the American sample 216. Subjects were recruited by analogous strategies. Results We found that the Brazilian sample was younger, predominantly male, less likely to be Caucasian, more likely to be partnered, had a faster progression from recreational gambling to GD, and were more likely to endorse chasing losses. Conclusion This study demonstrated that there are significant differences between treatment-seeking samples of adults presenting GD due to EGM in Brazil and in the United States. These findings suggest that cultural aspects may have a relevant role in GD due to EGM. PMID:26474662
ON BIPOLAR SINGLE VALUED NEUTROSOPHIC GRAPHS
Said Broumi; Mohamed Talea; Assia Bakali; Florentin Smarandache
2016-01-01
In this article, we combine the concept of bipolar neutrosophic set and graph theory. We introduce the notions of bipolar single valued neutrosophic graphs, strong bipolar single valued neutrosophic graphs, complete bipolar single valued neutrosophic graphs, regular bipolar single valued neutrosophic graphs and investigate some of their related properties.
Graph Theory. 2. Vertex Descriptors and Graph Coloring
Directory of Open Access Journals (Sweden)
Lorentz JÄNTSCHI
2002-12-01
Full Text Available This original work presents the construction of a set of ten sequence matrices and their applications for ordering vertices in graphs. For every sequence matrix three ordering criteria are applied: lexicographic ordering, based on strings of numbers, corresponding to every vertex, extracted as rows from sequence matrices; ordering by the sum of path lengths from a given vertex; and ordering by the sum of paths, starting from a given vertex. We also examine a graph that has different orderings for the above criteria. We then proceed to demonstrate that every criterion induced its own partition of graph vertex. We propose the following theoretical result: both LAVS and LVDS criteria generate identical partitioning of vertices in any graph. Finally, a coloring of graph vertices according to introduced ordering criteria was proposed.
A Practical Approach to Constructing a Knowledge Graph for Cybersecurity
Directory of Open Access Journals (Sweden)
Yan Jia
2018-02-01
Full Text Available Cyberattack forms are complex and varied, and the detection and prediction of dynamic types of attack are always challenging tasks. Research on knowledge graphs is becoming increasingly mature in many fields. At present, it is very significant that certain scholars have combined the concept of the knowledge graph with cybersecurity in order to construct a cybersecurity knowledge base. This paper presents a cybersecurity knowledge base and deduction rules based on a quintuple model. Using machine learning, we extract entities and build ontology to obtain a cybersecurity knowledge base. New rules are then deduced by calculating formulas and using the path-ranking algorithm. The Stanford named entity recognizer (NER is also used to train an extractor to extract useful information. Experimental results show that the Stanford NER provides many features and the useGazettes parameter may be used to train a recognizer in the cybersecurity domain in preparation for future work. Keywords: Cybersecurity, Knowledge graph, Knowledge deduction
On an edge partition and root graphs of some classes of line graphs
Directory of Open Access Journals (Sweden)
K Pravas
2017-04-01
Full Text Available The Gallai and the anti-Gallai graphs of a graph $G$ are complementary pairs of spanning subgraphs of the line graph of $G$. In this paper we find some structural relations between these graph classes by finding a partition of the edge set of the line graph of a graph $G$ into the edge sets of the Gallai and anti-Gallai graphs of $G$. Based on this, an optimal algorithm to find the root graph of a line graph is obtained. Moreover, root graphs of diameter-maximal, distance-hereditary, Ptolemaic and chordal graphs are also discussed.
The One Universal Graph — a free and open graph database
International Nuclear Information System (INIS)
Ng, Liang S.; Champion, Corbin
2016-01-01
Recent developments in graph database mostly are huge projects involving big organizations, big operations and big capital, as the name Big Data attests. We proposed the concept of One Universal Graph (OUG) which states that all observable and known objects and concepts (physical, conceptual or digitally represented) can be connected with only one single graph; furthermore the OUG can be implemented with a very simple text file format with free software, capable of being executed on Android or smaller devices. As such the One Universal Graph Data Exchange (GOUDEX) modules can potentially be installed on hundreds of millions of Android devices and Intel compatible computers shipped annually. Coupled with its open nature and ability to connect to existing leading search engines and databases currently in operation, GOUDEX has the potential to become the largest and a better interface for users and programmers to interact with the data on the Internet. With a Web User Interface for users to use and program in native Linux environment, Free Crowdware implemented in GOUDEX can help inexperienced users learn programming with better organized documentation for free software, and is able to manage programmer's contribution down to a single line of code or a single variable in software projects. It can become the first practically realizable “Internet brain” on which a global artificial intelligence system can be implemented. Being practically free and open, One Universal Graph can have significant applications in robotics, artificial intelligence as well as social networks. (paper)
The One Universal Graph — a free and open graph database
Ng, Liang S.; Champion, Corbin
2016-02-01
Recent developments in graph database mostly are huge projects involving big organizations, big operations and big capital, as the name Big Data attests. We proposed the concept of One Universal Graph (OUG) which states that all observable and known objects and concepts (physical, conceptual or digitally represented) can be connected with only one single graph; furthermore the OUG can be implemented with a very simple text file format with free software, capable of being executed on Android or smaller devices. As such the One Universal Graph Data Exchange (GOUDEX) modules can potentially be installed on hundreds of millions of Android devices and Intel compatible computers shipped annually. Coupled with its open nature and ability to connect to existing leading search engines and databases currently in operation, GOUDEX has the potential to become the largest and a better interface for users and programmers to interact with the data on the Internet. With a Web User Interface for users to use and program in native Linux environment, Free Crowdware implemented in GOUDEX can help inexperienced users learn programming with better organized documentation for free software, and is able to manage programmer's contribution down to a single line of code or a single variable in software projects. It can become the first practically realizable “Internet brain” on which a global artificial intelligence system can be implemented. Being practically free and open, One Universal Graph can have significant applications in robotics, artificial intelligence as well as social networks.
Taber, Daniel R; Chriqui, Jamie F; Vuillaume, Renee; Chaloupka, Frank J
2014-01-01
Sodas are widely sold in vending machines and other school venues in the United States, particularly in high school. Research suggests that policy changes have reduced soda access, but the impact of reduced access on consumption is unclear. This study was designed to identify student, environmental, or policy characteristics that modify the associations between school vending machines and student dietary behaviors. Data on school vending machine access and student diet were obtained as part of the National Youth Physical Activity and Nutrition Study (NYPANS) and linked to state-level data on soda taxes, restaurant taxes, and state laws governing the sale of soda in schools. Regression models were used to: 1) estimate associations between vending machine access and soda consumption, fast food consumption, and lunch source, and 2) determine if associations were modified by state soda taxes, restaurant taxes, laws banning in-school soda sales, or student characteristics (race/ethnicity, sex, home food access, weight loss behaviors.). Contrary to the hypothesis, students tended to consume 0.53 fewer servings of soda/week (95% CI: -1.17, 0.11) and consume fast food on 0.24 fewer days/week (95% CI: -0.44, -0.05) if they had in-school access to vending machines. They were also less likely to consume soda daily (23.9% vs. 27.9%, average difference = -4.02, 95% CI: -7.28, -0.76). However, these inverse associations were observed primarily among states with lower soda and restaurant tax rates (relative to general food tax rates) and states that did not ban in-school soda sales. Associations did not vary by any student characteristics except for weight loss behaviors. Isolated changes to the school food environment may have unintended consequences unless policymakers incorporate other initiatives designed to discourage overall soda consumption.
Directory of Open Access Journals (Sweden)
Daniel R Taber
Full Text Available Sodas are widely sold in vending machines and other school venues in the United States, particularly in high school. Research suggests that policy changes have reduced soda access, but the impact of reduced access on consumption is unclear. This study was designed to identify student, environmental, or policy characteristics that modify the associations between school vending machines and student dietary behaviors.Data on school vending machine access and student diet were obtained as part of the National Youth Physical Activity and Nutrition Study (NYPANS and linked to state-level data on soda taxes, restaurant taxes, and state laws governing the sale of soda in schools. Regression models were used to: 1 estimate associations between vending machine access and soda consumption, fast food consumption, and lunch source, and 2 determine if associations were modified by state soda taxes, restaurant taxes, laws banning in-school soda sales, or student characteristics (race/ethnicity, sex, home food access, weight loss behaviors..Contrary to the hypothesis, students tended to consume 0.53 fewer servings of soda/week (95% CI: -1.17, 0.11 and consume fast food on 0.24 fewer days/week (95% CI: -0.44, -0.05 if they had in-school access to vending machines. They were also less likely to consume soda daily (23.9% vs. 27.9%, average difference = -4.02, 95% CI: -7.28, -0.76. However, these inverse associations were observed primarily among states with lower soda and restaurant tax rates (relative to general food tax rates and states that did not ban in-school soda sales. Associations did not vary by any student characteristics except for weight loss behaviors.Isolated changes to the school food environment may have unintended consequences unless policymakers incorporate other initiatives designed to discourage overall soda consumption.
Taber, Daniel R.; Chriqui, Jamie F.; Vuillaume, Renee; Chaloupka, Frank J.
2014-01-01
Background Sodas are widely sold in vending machines and other school venues in the United States, particularly in high school. Research suggests that policy changes have reduced soda access, but the impact of reduced access on consumption is unclear. This study was designed to identify student, environmental, or policy characteristics that modify the associations between school vending machines and student dietary behaviors. Methods Data on school vending machine access and student diet were obtained as part of the National Youth Physical Activity and Nutrition Study (NYPANS) and linked to state-level data on soda taxes, restaurant taxes, and state laws governing the sale of soda in schools. Regression models were used to: 1) estimate associations between vending machine access and soda consumption, fast food consumption, and lunch source, and 2) determine if associations were modified by state soda taxes, restaurant taxes, laws banning in-school soda sales, or student characteristics (race/ethnicity, sex, home food access, weight loss behaviors.) Results Contrary to the hypothesis, students tended to consume 0.53 fewer servings of soda/week (95% CI: -1.17, 0.11) and consume fast food on 0.24 fewer days/week (95% CI: -0.44, -0.05) if they had in-school access to vending machines. They were also less likely to consume soda daily (23.9% vs. 27.9%, average difference = -4.02, 95% CI: -7.28, -0.76). However, these inverse associations were observed primarily among states with lower soda and restaurant tax rates (relative to general food tax rates) and states that did not ban in-school soda sales. Associations did not vary by any student characteristics except for weight loss behaviors. Conclusion Isolated changes to the school food environment may have unintended consequences unless policymakers incorporate other initiatives designed to discourage overall soda consumption. PMID:25083906
Sachs, Nicholas A.; Ruiz-Torres, Ricardo; Perreault, Eric J.; Miller, Lee E.
2016-02-01
Objective. It is quite remarkable that brain machine interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed. Approach. We have developed a system that uses two independent Wiener filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The decoder combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state. Main results. We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor’s proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder. Significance. We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the
Automation of a universal machine; Automatizacion de una maquina universal
Energy Technology Data Exchange (ETDEWEB)
Rodriguez S, J
1997-09-01
The development of the hardware and software of a control system for a servo-hydraulic machine is presented. The universal machine is an Instron, model 1331, used to make mechanical tests. The software includes the acquisition of data from the measurements, processing and graphic presentation of the results in the assay of the `tension` type. The control is based on a PPI (Programmable Peripheral Interface) 8255, in which the different states of the machine are set. The control functions of the machine are: (a) Start of an assay, (b) Pause in the assay, (c) End of the assay, (d) Choice of the control mode of the machine, that they could be in load, stroke or strain modes. For the data acquisition, a commercial card, National Products, model DAS-16, plugged in a slot of a Pc was used. Three transducers provide the analog signals, a cell of load, a LVDT and a extensometer. All the data are digitalized and handled in order to get the results in the appropriate working units. A stress-strain graph is obtained in the screen of the Pc for a tension test for a specific material. The points of maximum stress, rupture stress and the yield stress of the material under test are shown. (Author).
Multigraph: Interactive Data Graphs on the Web
Phillips, M. B.
2010-12-01
Many aspects of geophysical science involve time dependent data that is often presented in the form of a graph. Considering that the web has become a primary means of communication, there are surprisingly few good tools and techniques available for presenting time-series data on the web. The most common solution is to use a desktop tool such as Excel or Matlab to create a graph which is saved as an image and then included in a web page like any other image. This technique is straightforward, but it limits the user to one particular view of the data, and disconnects the graph from the data in a way that makes updating a graph with new data an often cumbersome manual process. This situation is somewhat analogous to the state of mapping before the advent of GIS. Maps existed only in printed form, and creating a map was a laborious process. In the last several years, however, the world of mapping has experienced a revolution in the form of web-based and other interactive computer technologies, so that it is now commonplace for anyone to easily browse through gigabytes of geographic data. Multigraph seeks to bring a similar ease of access to time series data. Multigraph is a program for displaying interactive time-series data graphs in web pages that includes a simple way of configuring the appearance of the graph and the data to be included. It allows multiple data sources to be combined into a single graph, and allows the user to explore the data interactively. Multigraph lets users explore and visualize "data space" in the same way that interactive mapping applications such as Google Maps facilitate exploring and visualizing geography. Viewing a Multigraph graph is extremely simple and intuitive, and requires no instructions. Creating a new graph for inclusion in a web page involves writing a simple XML configuration file and requires no programming. Multigraph can read data in a variety of formats, and can display data from a web service, allowing users to "surf
The planar cubic Cayley graphs
Georgakopoulos, Agelos
2018-01-01
The author obtains a complete description of the planar cubic Cayley graphs, providing an explicit presentation and embedding for each of them. This turns out to be a rich class, comprising several infinite families. He obtains counterexamples to conjectures of Mohar, Bonnington and Watkins. The author's analysis makes the involved graphs accessible to computation, corroborating a conjecture of Droms.
Groupies in random bipartite graphs
Yilun Shang
2010-01-01
A vertex $v$ of a graph $G$ is called a groupie if its degree is notless than the average of the degrees of its neighbors. In thispaper we study the influence of bipartition $(B_1,B_2)$ on groupiesin random bipartite graphs $G(B_1,B_2,p)$ with both fixed $p$ and$p$ tending to zero.
Nested Dynamic Condition Response Graphs
DEFF Research Database (Denmark)
Hildebrandt, Thomas; Mukkamala, Raghava Rao; Slaats, Tijs
2012-01-01
We present an extension of the recently introduced declarative process model Dynamic Condition Response Graphs ( DCR Graphs) to allow nested subgraphs and a new milestone relation between events. The extension was developed during a case study carried out jointly with our industrial partner...
Graph Sampling for Covariance Estimation
Chepuri, Sundeep Prabhakar; Leus, Geert
2017-01-01
specialize for undirected circulant graphs in that the graph nodes leading to the best compression rates are given by the so-called minimal sparse rulers. A near-optimal greedy algorithm is developed to design the subsampling scheme for the non
Network reconstruction via graph blending
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.
A cluster algorithm for graphs
S. van Dongen
2000-01-01
textabstractA cluster algorithm for graphs called the emph{Markov Cluster algorithm (MCL~algorithm) is introduced. The algorithm provides basically an interface to an algebraic process defined on stochastic matrices, called the MCL~process. The graphs may be both weighted (with nonnegative weight)
Evaluation of quality assurance of some diagnostic x-ray machines in Khartoum state
International Nuclear Information System (INIS)
Mohammed, Sirelkatim Khogali
2013-04-01
Availability and the use of x-ray equipment in both private and government hospitals are on the increase today in Khartoum state. Quality control of such equipment is of particular importance to prevent avoidable high doses, radiation leakages and to ensure dose optimization. The results of quality control in this study indicated that: all centers were within the k Vp reproducibility level (5%). At k Vp 50 and 60 there were 93% of centers within the limit and 7% were out. For 70, 81 and 90 k Vp all centers were within the limit. 73% of centers were within the level of HVL test, but 27% of them were out of the limit 80% of centers had a linear relationship between m As and dose, but three centers had no linear relationship. For time reproducibility 80% of centers were within the time reproducibility and 13% were out of limit. The beam on control and indicator were available and functional for all centers. The warning light was present in one center. But 93% of centers, but 20% of centers had no window lead glass. Lead aprons were available and functional in all centers. The gloves were available and functional in 33% of centers. But in 67% of centers they were not present. Gonads shields were present in 33% of centers, but not available for 67% of centers.(Author)
Jafarzadegan, K.; Merwade, V.; Saksena, S.
2017-12-01
Using conventional hydrodynamic methods for floodplain mapping in large-scale and data-scarce regions is problematic due to the high cost of these methods, lack of reliable data and uncertainty propagation. In this study a new framework is proposed to generate 100-year floodplains for any gauged or ungauged watershed across the United States (U.S.). This framework uses Flood Insurance Rate Maps (FIRMs), topographic, climatic and land use data which are freely available for entire U.S. for floodplain mapping. The framework consists of three components, including a Random Forest classifier for watershed classification, a Probabilistic Threshold Binary Classifier (PTBC) for generating the floodplains, and a lookup table for linking the Random Forest classifier to the PTBC. The effectiveness and reliability of the proposed framework is tested on 145 watersheds from various geographical locations in the U.S. The validation results show that around 80 percent of total watersheds are predicted well, 14 percent have acceptable fit and less than five percent are predicted poorly compared to FIRMs. Another advantage of this framework is its ability in generating floodplains for all small rivers and tributaries. Due to the high accuracy and efficiency of this framework, it can be used as a preliminary decision making tool to generate 100-year floodplain maps for data-scarce regions and all tributaries where hydrodynamic methods are difficult to use.
Applicability of the Directed Graph Methodology
Energy Technology Data Exchange (ETDEWEB)
Huszti, Jozsef [Institute of Isotope of the Hungarian Academy of Sciences, Budapest (Hungary); Nemeth, Andras [ESRI Hungary, Budapest (Hungary); Vincze, Arpad [Hungarian Atomic Energy Authority, Budapest (Hungary)
2012-06-15
Possible methods to construct, visualize and analyse the 'map' of the State's nuclear infrastructure based on different directed graph approaches are proposed. The transportation and the flow network models are described in detail. The use of the possible evaluation methodologies and the use of available software tools to construct and maintain the nuclear 'map' using pre-defined standard building blocks (nuclear facilities) are introduced and discussed.
International Nuclear Information System (INIS)
Uren, Kenneth Richard; Schoor, George van
2013-01-01
This second paper in a two part series presents the application of a developed state space model extraction methodology applied to a Brayton cycle-based PCU (power conversion unit) of a PBMR (pebble bed modular reactor). The goal is to investigate if the state space extraction methodology can cope with larger and more complex thermohydraulic systems. In Part I the state space model extraction methodology for the purpose of control was described in detail and a state space representation was extracted for a U-tube system to illustrate the concept. In this paper a 25th order nonlinear state space representation in terms of the different energy domains is extracted. This state space representation is solved and the responses of a number of important states are compared with results obtained from a PBMR PCU Flownex ® model. Flownex ® is a validated thermo fluid simulation software package. The results show that the state space model closely resembles the dynamics of the PBMR PCU. This kind of model may be used for nonlinear MIMO (multi-input, multi-output) type of control strategies. However, there is still a need for linear state space models since many control system design and analysis techniques require a linear state space model. This issue is also addressed in this paper by showing how a linear state space model can be derived from the extracted nonlinear state space model. The linearised state space model is also validated by comparing the state space model to an existing linear Simulink ® model of the PBMR PCU system. - Highlights: • State space model extraction of a pebble bed modular reactor PCU (power conversion unit). • A 25th order nonlinear time varying state space model is obtained. • Linearisation of a nonlinear state space model for use in power output control. • Non-minimum phase characteristic that is challenging in terms of control. • Models derived are useful for MIMO control strategies
Planar graphs theory and algorithms
Nishizeki, T
1988-01-01
Collected in this volume are most of the important theorems and algorithms currently known for planar graphs, together with constructive proofs for the theorems. Many of the algorithms are written in Pidgin PASCAL, and are the best-known ones; the complexities are linear or 0(nlogn). The first two chapters provide the foundations of graph theoretic notions and algorithmic techniques. The remaining chapters discuss the topics of planarity testing, embedding, drawing, vertex- or edge-coloring, maximum independence set, subgraph listing, planar separator theorem, Hamiltonian cycles, and single- or multicommodity flows. Suitable for a course on algorithms, graph theory, or planar graphs, the volume will also be useful for computer scientists and graph theorists at the research level. An extensive reference section is included.
Quantum chaos on discrete graphs
International Nuclear Information System (INIS)
Smilansky, Uzy
2007-01-01
Adapting a method developed for the study of quantum chaos on quantum (metric) graphs (Kottos and Smilansky 1997 Phys. Rev. Lett. 79 4794, Kottos and Smilansky 1999 Ann. Phys., NY 274 76), spectral ζ functions and trace formulae for discrete Laplacians on graphs are derived. This is achieved by expressing the spectral secular equation in terms of the periodic orbits of the graph and obtaining functions which belong to the class of ζ functions proposed originally by Ihara (1966 J. Mat. Soc. Japan 18 219) and expanded by subsequent authors (Stark and Terras 1996 Adv. Math. 121 124, Kotani and Sunada 2000 J. Math. Sci. Univ. Tokyo 7 7). Finally, a model of 'classical dynamics' on the discrete graph is proposed. It is analogous to the corresponding classical dynamics derived for quantum graphs (Kottos and Smilansky 1997 Phys. Rev. Lett. 79 4794, Kottos and Smilansky 1999 Ann. Phys., NY 274 76). (fast track communication)
RJSplot: Interactive Graphs with R.
Barrios, David; Prieto, Carlos
2018-03-01
Data visualization techniques provide new methods for the generation of interactive graphs. These graphs allow a better exploration and interpretation of data but their creation requires advanced knowledge of graphical libraries. Recent packages have enabled the integration of interactive graphs in R. However, R provides limited graphical packages that allow the generation of interactive graphs for computational biology applications. The present project has joined the analytical power of R with the interactive graphical features of JavaScript in a new R package (RJSplot). It enables the easy generation of interactive graphs in R, provides new visualization capabilities, and contributes to the advance of computational biology analytical methods. At present, 16 interactive graphics are available in RJSplot, such as the genome viewer, Manhattan plots, 3D plots, heatmaps, dendrograms, networks, and so on. The RJSplot package is freely available online at http://rjsplot.net. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Graph-based structural change detection for rotating machinery monitoring
Lu, Guoliang; Liu, Jie; Yan, Peng
2018-01-01
Detection of structural changes is critically important in operational monitoring of a rotating machine. This paper presents a novel framework for this purpose, where a graph model for data modeling is adopted to represent/capture statistical dynamics in machine operations. Meanwhile we develop a numerical method for computing temporal anomalies in the constructed graphs. The martingale-test method is employed for the change detection when making decisions on possible structural changes, where excellent performance is demonstrated outperforming exciting results such as the autoregressive-integrated-moving average (ARIMA) model. Comprehensive experimental results indicate good potentials of the proposed algorithm in various engineering applications. This work is an extension of a recent result (Lu et al., 2017).
Faucet: streaming de novo assembly graph construction.
Rozov, Roye; Goldshlager, Gil; Halperin, Eran; Shamir, Ron
2018-01-01
We present Faucet, a two-pass streaming algorithm for assembly graph construction. Faucet builds an assembly graph incrementally as each read is processed. Thus, reads need not be stored locally, as they can be processed while downloading data and then discarded. We demonstrate this functionality by performing streaming graph assembly of publicly available data, and observe that the ratio of disk use to raw data size decreases as coverage is increased. Faucet pairs the de Bruijn graph obtained from the reads with additional meta-data derived from them. We show these metadata-coverage counts collected at junction k-mers and connections bridging between junction pairs-contain most salient information needed for assembly, and demonstrate they enable cleaning of metagenome assembly graphs, greatly improving contiguity while maintaining accuracy. We compared Fauceted resource use and assembly quality to state of the art metagenome assemblers, as well as leading resource-efficient genome assemblers. Faucet used orders of magnitude less time and disk space than the specialized metagenome assemblers MetaSPAdes and Megahit, while also improving on their memory use; this broadly matched performance of other assemblers optimizing resource efficiency-namely, Minia and LightAssembler. However, on metagenomes tested, Faucet,o outputs had 14-110% higher mean NGA50 lengths compared with Minia, and 2- to 11-fold higher mean NGA50 lengths compared with LightAssembler, the only other streaming assembler available. Faucet is available at https://github.com/Shamir-Lab/Faucet. rshamir@tau.ac.il or eranhalperin@gmail.com. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.
Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.
Pound, Michael P; Atkinson, Jonathan A; Townsend, Alexandra J; Wilson, Michael H; Griffiths, Marcus; Jackson, Aaron S; Bulat, Adrian; Tzimiropoulos, Georgios; Wells, Darren M; Murchie, Erik H; Pridmore, Tony P; French, Andrew P
2017-10-01
In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets. © The Authors 2017. Published by Oxford University Press.
Walia, Rasna R; Caragea, Cornelia; Lewis, Benjamin A; Towfic, Fadi; Terribilini, Michael; El-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant
2012-05-10
RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition 'code' that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction. We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naïve Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM
On characterizing terrain visibility graphs
Directory of Open Access Journals (Sweden)
William Evans
2015-06-01
Full Text Available A terrain is an $x$-monotone polygonal line in the $xy$-plane. Two vertices of a terrain are mutually visible if and only if there is no terrain vertex on or above the open line segment connecting them. A graph whose vertices represent terrain vertices and whose edges represent mutually visible pairs of terrain vertices is called a terrain visibility graph. We would like to find properties that are both necessary and sufficient for a graph to be a terrain visibility graph; that is, we would like to characterize terrain visibility graphs.Abello et al. [Discrete and Computational Geometry, 14(3:331--358, 1995] showed that all terrain visibility graphs are “persistent”. They showed that the visibility information of a terrain point set implies some ordering requirements on the slopes of the lines connecting pairs of points in any realization, and as a step towards showing sufficiency, they proved that for any persistent graph $M$ there is a total order on the slopes of the (pseudo lines in a generalized configuration of points whose visibility graph is $M$.We give a much simpler proof of this result by establishing an orientation to every triple of vertices, reflecting some slope ordering requirements that are consistent with $M$ being the visibility graph, and prove that these requirements form a partial order. We give a faster algorithm to construct a total order on the slopes. Our approach attempts to clarify the implications of the graph theoretic properties on the ordering of the slopes, and may be interpreted as defining properties on an underlying oriented matroid that we show is a restricted type of $3$-signotope.
Graph embedding with rich information through heterogeneous graph
Sun, Guolei
2017-11-12
Graph embedding, aiming to learn low-dimensional representations for nodes in graphs, has attracted increasing attention due to its critical application including node classification, link prediction and clustering in social network analysis. Most existing algorithms for graph embedding only rely on the topology information and fail to use the copious information in nodes as well as edges. As a result, their performance for many tasks may not be satisfactory. In this thesis, we proposed a novel and general framework for graph embedding with rich text information (GERI) through constructing a heterogeneous network, in which we integrate node and edge content information with graph topology. Specially, we designed a novel biased random walk to explore the constructed heterogeneous network with the notion of flexible neighborhood. Our sampling strategy can compromise between BFS and DFS local search on heterogeneous graph. To further improve our algorithm, we proposed semi-supervised GERI (SGERI), which learns graph embedding in an discriminative manner through heterogeneous network with label information. The efficacy of our method is demonstrated by extensive comparison experiments with 9 baselines over multi-label and multi-class classification on various datasets including Citeseer, Cora, DBLP and Wiki. It shows that GERI improves the Micro-F1 and Macro-F1 of node classification up to 10%, and SGERI improves GERI by 5% in Wiki.
CORECLUSTER: A Degeneracy Based Graph Clustering Framework
Giatsidis , Christos; Malliaros , Fragkiskos; Thilikos , Dimitrios M. ,; Vazirgiannis , Michalis
2014-01-01
International audience; Graph clustering or community detection constitutes an important task forinvestigating the internal structure of graphs, with a plethora of applications in several domains. Traditional tools for graph clustering, such asspectral methods, typically suffer from high time and space complexity. In thisarticle, we present \\textsc{CoreCluster}, an efficient graph clusteringframework based on the concept of graph degeneracy, that can be used along withany known graph clusteri...
Akcora, Cuneyt Gurcan; Gel, Yulia R.; Kantarcioglu, Murat
2017-01-01
Bitcoin and its underlying technology Blockchain have become popular in recent years. Designed to facilitate a secure distributed platform without central authorities, Blockchain is heralded as a paradigm that will be as powerful as Big Data, Cloud Computing and Machine learning. Blockchain incorporates novel ideas from various fields such as public key encryption and distributed systems. As such, a reader often comes across resources that explain the Blockchain technology from a certain pers...
Human Machine Learning Symbiosis
Walsh, Kenneth R.; Hoque, Md Tamjidul; Williams, Kim H.
2017-01-01
Human Machine Learning Symbiosis is a cooperative system where both the human learner and the machine learner learn from each other to create an effective and efficient learning environment adapted to the needs of the human learner. Such a system can be used in online learning modules so that the modules adapt to each learner's learning state both…
Quantum walks of two interacting particles on percolation graphs
Siloi, Ilaria; Benedetti, Claudia; Piccinini, Enrico; Paris, Matteo G. A.; Bordone, Paolo
2017-10-01
We address the dynamics of two indistinguishable interacting particles moving on a dynamical percolation graph, i.e., a graph where the edges are independent random telegraph processes whose values jump between 0 and 1, thus mimicking percolation. The interplay between the particle interaction strength, initial state and the percolation rate determine different dynamical regimes for the walkers. We show that, whenever the walkers are initially localised within the interaction range, fast noise enhances the particle spread compared to the noiseless case.
Le Doeuff, René
2013-01-01
In this book a general matrix-based approach to modeling electrical machines is promulgated. The model uses instantaneous quantities for key variables and enables the user to easily take into account associations between rotating machines and static converters (such as in variable speed drives). General equations of electromechanical energy conversion are established early in the treatment of the topic and then applied to synchronous, induction and DC machines. The primary characteristics of these machines are established for steady state behavior as well as for variable speed scenarios. I
SNAP: A General Purpose Network Analysis and Graph Mining Library.
Leskovec, Jure; Sosič, Rok
2016-10-01
Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task. Here, we describe Stanford Network Analysis Platform (SNAP), a general-purpose, high-performance system that provides easy to use, high-level operations for analysis and manipulation of large networks. We present SNAP functionality, describe its implementational details, and give performance benchmarks. SNAP has been developed for single big-memory machines and it balances the trade-off between maximum performance, compact in-memory graph representation, and the ability to handle dynamic graphs where nodes and edges are being added or removed over time. SNAP can process massive networks with hundreds of millions of nodes and billions of edges. SNAP offers over 140 different graph algorithms that can efficiently manipulate large graphs, calculate structural properties, generate regular and random graphs, and handle attributes and meta-data on nodes and edges. Besides being able to handle large graphs, an additional strength of SNAP is that networks and their attributes are fully dynamic, they can be modified during the computation at low cost. SNAP is provided as an open source library in C++ as well as a module in Python. We also describe the Stanford Large Network Dataset, a set of social and information real-world networks and datasets, which we make publicly available. The collection is a complementary resource to our SNAP software and is widely used for development and benchmarking of graph analytics algorithms.
Label Information Guided Graph Construction for Semi-Supervised Learning.
Zhuang, Liansheng; Zhou, Zihan; Gao, Shenghua; Yin, Jingwen; Lin, Zhouchen; Ma, Yi
2017-09-01
In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.
GSMNet: A Hierarchical Graph Model for Moving Objects in Networks
Directory of Open Access Journals (Sweden)
Hengcai Zhang
2017-03-01
Full Text Available Existing data models for moving objects in networks are often limited by flexibly controlling the granularity of representing networks and the cost of location updates and do not encompass semantic information, such as traffic states, traffic restrictions and social relationships. In this paper, we aim to fill the gap of traditional network-constrained models and propose a hierarchical graph model called the Geo-Social-Moving model for moving objects in Networks (GSMNet that adopts four graph structures, RouteGraph, SegmentGraph, ObjectGraph and MoveGraph, to represent the underlying networks, trajectories and semantic information in an integrated manner. The bulk of user-defined data types and corresponding operators is proposed to handle moving objects and answer a new class of queries supporting three kinds of conditions: spatial, temporal and semantic information. Then, we develop a prototype system with the native graph database system Neo4Jto implement the proposed GSMNet model. In the experiment, we conduct the performance evaluation using simulated trajectories generated from the BerlinMOD (Berlin Moving Objects Database benchmark and compare with the mature MOD system Secondo. The results of 17 benchmark queries demonstrate that our proposed GSMNet model has strong potential to reduce time-consuming table join operations an d shows remarkable advantages with regard to representing semantic information and controlling the cost of location updates.
International Nuclear Information System (INIS)
Montmain, J.
2005-01-01
Today's complexity of systems where man is involved has led to the development of more and more sophisticated information processing systems where decision making has become more and more difficult. The operator task has moved from operation to supervision and the production tool has become indissociable from its numerical instrumentation and control system. The integration of more and more numerous and sophisticated control indicators in the control room does not necessary fulfill the expectations of the operation team. It is preferable to develop cooperative information systems which are real situation understanding aids. The stake is not the automation of operators' cognitive tasks but the supply of a reasoning help. One of the challenges of interactive information systems is the selection, organisation and dynamical display of information. The efficiency of the whole man-machine system depends on the communication interface efficiency. This article presents the principles and specificities of man-machine supervision systems: 1 - principle: operator's role in control room, operator and automation, monitoring and diagnosis, characteristics of useful models for supervision; 2 - qualitative reasoning: origin, trends, evolutions; 3 - causal reasoning: causality, causal graph representation, causal and diagnostic graph; 4 - multi-points of view reasoning: multi flow modeling method, Sagace method; 5 - approximate reasoning: the symbolic numerical interface, the multi-criteria decision; 6 - example of application: supervision in a spent-fuel reprocessing facility. (J.S.)
Directory of Open Access Journals (Sweden)
Alireza eGharabaghi
2014-03-01
Full Text Available Motor recovery after stroke is an unsolved challenge despite intensive rehabilitation training programs. Brain stimulation techniques have been explored in addition to traditional rehabilitation training to increase the excitability of the stimulated motor cortex. This modulation of cortical excitability augments the response to afferent input during motor exercises, thereby enhancing skilled motor learning by long-term potentiation-like plasticity. Recent approaches examined brain stimulation applied concurrently with voluntary movements to induce more specific use-dependent neural plasticity during motor training for neurorehabilitation. Unfortunately, such approaches are not applicable for the many severely affected stroke patients lacking residual hand function. These patients require novel activity-dependent stimulation paradigms based on intrinsic brain activity. Here, we report on such brain state-dependent stimulation (BSDS combined with haptic feedback provided by a robotic hand orthosis. Transcranial magnetic stimulation of the motor cortex and haptic feedback to the hand were controlled by sensorimotor desynchronization during motor-imagery and applied within a brain-machine interface environment in one healthy subject and one patient with severe hand paresis in the chronic phase after stroke. BSDS significantly increased the excitability of the stimulated motor cortex in both healthy and post-stroke conditions, an effect not observed in non-BSDS protocols. This feasibility study suggests that closing the loop between intrinsic brain state, cortical stimulation and haptic feedback provides a novel neurorehabilitation strategy for stroke patients lacking residual hand function, a proposal that warrants further investigation in a larger cohort of stroke patients.
System dynamics and control with bond graph modeling
Kypuros, Javier
2013-01-01
Part I Dynamic System ModelingIntroduction to System DynamicsIntroductionSystem Decomposition and Model ComplexityMathematical Modeling of Dynamic SystemsAnalysis and Design of Dynamic SystemsControl of Dynamic SystemsDiagrams of Dynamic SystemsA Graph-Centered Approach to ModelingSummaryPracticeExercisesBasic Bond Graph ElementsIntroductionPower and Energy VariablesBasic 1-Port ElementsBasic 2-Ports ElementsJunction ElementsSimple Bond Graph ExamplesSummaryPracticeExercisesBond Graph Synthesis and Equation DerivationIntroductionGeneral GuidelinesMechanical TranslationMechanical RotationElectrical CircuitsHydraulic CircuitsMixed SystemsState Equation DerivationState-Space RepresentationsAlgebraic Loops and Derivative CausalitySummaryPracticeExercisesImpedance Bond GraphsIntroductionLaplace Transform of the State-Space EquationBasic 1-Port ImpedancesImpedance Bond Graph SynthesisJunctions, Transformers, and GyratorsEffort and Flow DividersSign ChangesTransfer Function DerivationAlternative Derivation of Transf...
Abramov, V.
2013-12-01
This innovation on www.repowermachine.com is finalist at Clean-tech and Energy of 2012 Minnesota's TEKNE AWARDS. Vehicles are pushed by force of friction between their wheels and land, propellers and water or air according to Third Newton's law of physics of moving. Force of friction is dependent to vehicle weight as highest torque of wheel or propeller for vehicle moving from stop. Friction force DOES NOT dependent to motor power. Why existing SUV of 2,000 lb uses 550 hp motor when first vehicle has 0.75 hp motor (Carl Benz';s patent #37435, January 29, 1886 in Germany)? Gas or magnet field reaches needed torque of wheels too slowly because requires huge motor power for acceleration SUV from 0 to 100 mph for 5 second. The acceleration system by gas or magnet field uses additional energy for increasing motor shaft idle speed and reduces its highest torque of physical volume because necessary to increase motor power that equal/exceed motor power according to vehicle weight. Therefore, any transmission torque DOES NOT NEED and it is use as second brake. Ship, locomotives, helicopters, CNC machine tools, etc motor(s) directly turn wheels, propellers, spindles or ignore to use gear -transmission designs. How do you follow to Creator's physics law of LEVER for saving energy? Existing machine propulsion is transformed by one comprising least numbers of gears and maybe shafts from above state-of-the-art 1,000 gearbox apparatus designs. It is installed or replaced transmission in existing propulsion that is transformed to non-accelerated propulsion. It cuts about 80% mechanical energy that acceleration system wastes in motor heat form, cuts time of movement by reaching each speed for 1-2 seconds. It produces all needed speeds and uses only idle speed of cheapest motor with reduced power and cost that have replaced existing motor too. There is opportunity to eliminate vehicle/machine roads traffics in cities that creates additional unknown GHG emissions Revolutionary
Hierarchy of modular graph identities
International Nuclear Information System (INIS)
D’Hoker, Eric; Kaidi, Justin
2016-01-01
The low energy expansion of Type II superstring amplitudes at genus one is organized in terms of modular graph functions associated with Feynman graphs of a conformal scalar field on the torus. In earlier work, surprising identities between two-loop graphs at all weights, and between higher-loop graphs of weights four and five were constructed. In the present paper, these results are generalized in two complementary directions. First, all identities at weight six and all dihedral identities at weight seven are obtained and proven. Whenever the Laurent polynomial at the cusp is available, the form of these identities confirms the pattern by which the vanishing of the Laurent polynomial governs the full modular identity. Second, the family of modular graph functions is extended to include all graphs with derivative couplings and worldsheet fermions. These extended families of modular graph functions are shown to obey a hierarchy of inhomogeneous Laplace eigenvalue equations. The eigenvalues are calculated analytically for the simplest infinite sub-families and obtained by Maple for successively more complicated sub-families. The spectrum is shown to consist solely of eigenvalues s(s−1) for positive integers s bounded by the weight, with multiplicities which exhibit rich representation-theoretic patterns.
Semantic graphs and associative memories
Pomi, Andrés; Mizraji, Eduardo
2004-12-01
Graphs have been increasingly utilized in the characterization of complex networks from diverse origins, including different kinds of semantic networks. Human memories are associative and are known to support complex semantic nets; these nets are represented by graphs. However, it is not known how the brain can sustain these semantic graphs. The vision of cognitive brain activities, shown by modern functional imaging techniques, assigns renewed value to classical distributed associative memory models. Here we show that these neural network models, also known as correlation matrix memories, naturally support a graph representation of the stored semantic structure. We demonstrate that the adjacency matrix of this graph of associations is just the memory coded with the standard basis of the concept vector space, and that the spectrum of the graph is a code invariant of the memory. As long as the assumptions of the model remain valid this result provides a practical method to predict and modify the evolution of the cognitive dynamics. Also, it could provide us with a way to comprehend how individual brains that map the external reality, almost surely with different particular vector representations, are nevertheless able to communicate and share a common knowledge of the world. We finish presenting adaptive association graphs, an extension of the model that makes use of the tensor product, which provides a solution to the known problem of branching in semantic nets.
Hierarchy of modular graph identities
Energy Technology Data Exchange (ETDEWEB)
D’Hoker, Eric; Kaidi, Justin [Mani L. Bhaumik Institute for Theoretical Physics, Department of Physics and Astronomy,University of California,Los Angeles, CA 90095 (United States)
2016-11-09
The low energy expansion of Type II superstring amplitudes at genus one is organized in terms of modular graph functions associated with Feynman graphs of a conformal scalar field on the torus. In earlier work, surprising identities between two-loop graphs at all weights, and between higher-loop graphs of weights four and five were constructed. In the present paper, these results are generalized in two complementary directions. First, all identities at weight six and all dihedral identities at weight seven are obtained and proven. Whenever the Laurent polynomial at the cusp is available, the form of these identities confirms the pattern by which the vanishing of the Laurent polynomial governs the full modular identity. Second, the family of modular graph functions is extended to include all graphs with derivative couplings and worldsheet fermions. These extended families of modular graph functions are shown to obey a hierarchy of inhomogeneous Laplace eigenvalue equations. The eigenvalues are calculated analytically for the simplest infinite sub-families and obtained by Maple for successively more complicated sub-families. The spectrum is shown to consist solely of eigenvalues s(s−1) for positive integers s bounded by the weight, with multiplicities which exhibit rich representation-theoretic patterns.
XML Graphs in Program Analysis
DEFF Research Database (Denmark)
Møller, Anders; Schwartzbach, Michael I.
2011-01-01
of XML graphs against different XML schema languages, and provide a software package that enables others to make use of these ideas. We also survey the use of XML graphs for program analysis with four very different languages: XACT (XML in Java), Java Servlets (Web application programming), XSugar......XML graphs have shown to be a simple and effective formalism for representing sets of XML documents in program analysis. It has evolved through a six year period with variants tailored for a range of applications. We present a unified definition, outline the key properties including validation...
Rabern, Landon
2007-01-01
We improve upper bounds on the chromatic number proven independently in \\cite{reedNote} and \\cite{ingo}. Our main lemma gives a sufficient condition for two paths in graph to be completely joined. Using this, we prove that if a graph has an optimal coloring with more than $\\frac{\\omega}{2}$ singleton color classes, then it satisfies $\\chi \\leq \\frac{\\omega + \\Delta + 1}{2}$. It follows that a graph satisfying $n - \\Delta < \\alpha + \\frac{\\omega - 1}{2}$ must also satisfy $\\chi \\leq \\frac{\\ome...
Graphs with Eulerian unit spheres
Knill, Oliver
2015-01-01
d-spheres in graph theory are inductively defined as graphs for which all unit spheres S(x) are (d-1)-spheres and that the removal of one vertex renders the graph contractible. Eulerian d-spheres are geometric d-spheres which are d+1 colorable. We prove here that G is an Eulerian sphere if and only if the degrees of all the (d-2)-dimensional sub-simplices in G are even. This generalizes a Kempe-Heawood result for d=2 and is work related to the conjecture that all d-spheres have chromatic numb...
CSIR Research Space (South Africa)
Ngxande, Mkhuseli
2017-11-01
Full Text Available This paper presents a literature review of driver drowsiness detection based on behavioral measures using machine learning techniques. Faces contain information that can be used to interpret levels of drowsiness. There are many facial features...
Szabó, György; Fáth, Gábor
2007-07-01
Game theory is one of the key paradigms behind many scientific disciplines from biology to behavioral sciences to economics. In its evolutionary form and especially when the interacting agents are linked in a specific social network the underlying solution concepts and methods are very similar to those applied in non-equilibrium statistical physics. This review gives a tutorial-type overview of the field for physicists. The first four sections introduce the necessary background in classical and evolutionary game theory from the basic definitions to the most important results. The fifth section surveys the topological complications implied by non-mean-field-type social network structures in general. The next three sections discuss in detail the dynamic behavior of three prominent classes of models: the Prisoner's Dilemma, the Rock-Scissors-Paper game, and Competing Associations. The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games.
Bond graph model-based fault diagnosis of hybrid systems
Borutzky, Wolfgang
2015-01-01
This book presents a bond graph model-based approach to fault diagnosis in mechatronic systems appropriately represented by a hybrid model. The book begins by giving a survey of the fundamentals of fault diagnosis and failure prognosis, then recalls state-of-art developments referring to latest publications, and goes on to discuss various bond graph representations of hybrid system models, equations formulation for switched systems, and simulation of their dynamic behavior. The structured text: • focuses on bond graph model-based fault detection and isolation in hybrid systems; • addresses isolation of multiple parametric faults in hybrid systems; • considers system mode identification; • provides a number of elaborated case studies that consider fault scenarios for switched power electronic systems commonly used in a variety of applications; and • indicates that bond graph modelling can also be used for failure prognosis. In order to facilitate the understanding of fault diagnosis and the presented...
Properly colored connectivity of graphs
Li, Xueliang; Qin, Zhongmei
2018-01-01
A comprehensive survey of proper connection of graphs is discussed in this book with real world applications in computer science and network security. Beginning with a brief introduction, comprising relevant definitions and preliminary results, this book moves on to consider a variety of properties of graphs that imply bounds on the proper connection number. Detailed proofs of significant advancements toward open problems and conjectures are presented with complete references. Researchers and graduate students with an interest in graph connectivity and colorings will find this book useful as it builds upon fundamental definitions towards modern innovations, strategies, and techniques. The detailed presentation lends to use as an introduction to proper connection of graphs for new and advanced researchers, a solid book for a graduate level topics course, or as a reference for those interested in expanding and further developing research in the area.
Graph anomalies in cyber communications
Energy Technology Data Exchange (ETDEWEB)
Vander Wiel, Scott A [Los Alamos National Laboratory; Storlie, Curtis B [Los Alamos National Laboratory; Sandine, Gary [Los Alamos National Laboratory; Hagberg, Aric A [Los Alamos National Laboratory; Fisk, Michael [Los Alamos National Laboratory
2011-01-11
Enterprises monitor cyber traffic for viruses, intruders and stolen information. Detection methods look for known signatures of malicious traffic or search for anomalies with respect to a nominal reference model. Traditional anomaly detection focuses on aggregate traffic at central nodes or on user-level monitoring. More recently, however, traffic is being viewed more holistically as a dynamic communication graph. Attention to the graph nature of the traffic has expanded the types of anomalies that are being sought. We give an overview of several cyber data streams collected at Los Alamos National Laboratory and discuss current work in modeling the graph dynamics of traffic over the network. We consider global properties and local properties within the communication graph. A method for monitoring relative entropy on multiple correlated properties is discussed in detail.
Open Graphs and Computational Reasoning
Directory of Open Access Journals (Sweden)
Lucas Dixon
2010-06-01
Full Text Available We present a form of algebraic reasoning for computational objects which are expressed as graphs. Edges describe the flow of data between primitive operations which are represented by vertices. These graphs have an interface made of half-edges (edges which are drawn with an unconnected end and enjoy rich compositional principles by connecting graphs along these half-edges. In particular, this allows equations and rewrite rules to be specified between graphs. Particular computational models can then be encoded as an axiomatic set of such rules. Further rules can be derived graphically and rewriting can be used to simulate the dynamics of a computational system, e.g. evaluating a program on an input. Examples of models which can be formalised in this way include traditional electronic circuits as well as recent categorical accounts of quantum information.
Woeginger, G.J.
1998-01-01
In this short note we argue that the toughness of split graphs can be computed in polynomial time. This solves an open problem from a recent paper by Kratsch et al. (Discrete Math. 150 (1996) 231–245).
Generating random networks and graphs
Coolen, Ton; Roberts, Ekaterina
2017-01-01
This book supports researchers who need to generate random networks, or who are interested in the theoretical study of random graphs. The coverage includes exponential random graphs (where the targeted probability of each network appearing in the ensemble is specified), growth algorithms (i.e. preferential attachment and the stub-joining configuration model), special constructions (e.g. geometric graphs and Watts Strogatz models) and graphs on structured spaces (e.g. multiplex networks). The presentation aims to be a complete starting point, including details of both theory and implementation, as well as discussions of the main strengths and weaknesses of each approach. It includes extensive references for readers wishing to go further. The material is carefully structured to be accessible to researchers from all disciplines while also containing rigorous mathematical analysis (largely based on the techniques of statistical mechanics) to support those wishing to further develop or implement the theory of rand...
KUBIK, MARTHA Y.; WALL, MELANIE; SHEN, LIJUAN; NANNEY, MARILYN S.; NELSON, TOBEN F.; LASKA, MELISSA N.; STORY, MARY
2012-01-01
Background Policy that targets the school food environment has been advanced as one way to increase the availability of healthy food at schools and healthy food choice by students. Although both state- and district-level policy initiatives have focused on school nutrition standards, it remains to be seen whether these policies translate into healthy food practices at the school level, where student behavior will be impacted. Objective To examine whether state- and district-level nutrition policies addressing junk food in school vending machines and school stores were associated with less junk food in school vending machines and school stores. Junk food was defined as foods and beverages with low nutrient density that provide calories primarily through fats and added sugars. Design A cross-sectional study design was used to assess self-report data collected by computer-assisted telephone interviews or self-administered mail questionnaires from state-, district-, and school-level respondents participating in the School Health Policies and Programs Study 2006. The School Health Policies and Programs Study, administered every 6 years since 1994 by the Centers for Disease Control and Prevention, is considered the largest, most comprehensive assessment of school health policies and programs in the United States. Subjects/setting A nationally representative sample (n = 563) of public elementary, middle, and high schools was studied. Statistical analysis Logistic regression adjusted for school characteristics, sampling weights, and clustering was used to analyze data. Policies were assessed for strength (required, recommended, neither required nor recommended prohibiting junk food) and whether strength was similar for school vending machines and school stores. Results School vending machines and school stores were more prevalent in high schools (93%) than middle (84%) and elementary (30%) schools. For state policies, elementary schools that required prohibiting junk food
Kubik, Martha Y; Wall, Melanie; Shen, Lijuan; Nanney, Marilyn S; Nelson, Toben F; Laska, Melissa N; Story, Mary
2010-07-01
Policy that targets the school food environment has been advanced as one way to increase the availability of healthy food at schools and healthy food choice by students. Although both state- and district-level policy initiatives have focused on school nutrition standards, it remains to be seen whether these policies translate into healthy food practices at the school level, where student behavior will be impacted. To examine whether state- and district-level nutrition policies addressing junk food in school vending machines and school stores were associated with less junk food in school vending machines and school stores. Junk food was defined as foods and beverages with low nutrient density that provide calories primarily through fats and added sugars. A cross-sectional study design was used to assess self-report data collected by computer-assisted telephone interviews or self-administered mail questionnaires from state-, district-, and school-level respondents participating in the School Health Policies and Programs Study 2006. The School Health Policies and Programs Study, administered every 6 years since 1994 by the Centers for Disease Control and Prevention, is considered the largest, most comprehensive assessment of school health policies and programs in the United States. A nationally representative sample (n=563) of public elementary, middle, and high schools was studied. Logistic regression adjusted for school characteristics, sampling weights, and clustering was used to analyze data. Policies were assessed for strength (required, recommended, neither required nor recommended prohibiting junk food) and whether strength was similar for school vending machines and school stores. School vending machines and school stores were more prevalent in high schools (93%) than middle (84%) and elementary (30%) schools. For state policies, elementary schools that required prohibiting junk food in school vending machines and school stores offered less junk food than
Graph theory and its applications
Gross, Jonathan L
2006-01-01
Gross and Yellen take a comprehensive approach to graph theory that integrates careful exposition of classical developments with emerging methods, models, and practical needs. Their unparalleled treatment provides a text ideal for a two-semester course and a variety of one-semester classes, from an introductory one-semester course to courses slanted toward classical graph theory, operations research, data structures and algorithms, or algebra and topology.
2017-01-01
This book provides an overview on current sustainable machining. Its chapters cover the concept in economic, social and environmental dimensions. It provides the reader with proper ways to handle several pollutants produced during the machining process. The book is useful on both undergraduate and postgraduate levels and it is of interest to all those working with manufacturing and machining technology.
A faithful functor among algebras and graphs
Falcón Ganfornina, Óscar Jesús; Falcón Ganfornina, Raúl Manuel; Núñez Valdés, Juan; Pacheco Martínez, Ana María; Villar Liñán, María Trinidad; Vigo Aguiar, Jesús (Coordinador)
2016-01-01
The problem of identifying a functor between the categories of algebras and graphs is currently open. Based on a known algorithm that identifies isomorphisms of Latin squares with isomorphism of vertex-colored graphs, we describe here a pair of graphs that enable us to find a faithful functor between finite-dimensional algebras over finite fields and these graphs.
Graphs with branchwidth at most three
Bodlaender, H.L.; Thilikos, D.M.
1997-01-01
In this paper we investigate both the structure of graphs with branchwidth at most three, as well as algorithms to recognise such graphs. We show that a graph has branchwidth at most three, if and only if it has treewidth at most three and does not contain the three-dimensional binary cube graph
Graphs whose complement and square are isomorphic
DEFF Research Database (Denmark)
Pedersen, Anders Sune
2014-01-01
We study square-complementary graphs, that is, graphs whose complement and square are isomorphic. We prove several necessary conditions for a graph to be square-complementary, describe ways of building new square-complementary graphs from existing ones, construct infinite families of square-compl...
Acyclicity in edge-colored graphs
DEFF Research Database (Denmark)
Gutin, Gregory; Jones, Mark; Sheng, Bin
2017-01-01
A walk W in edge-colored graphs is called properly colored (PC) if every pair of consecutive edges in W is of different color. We introduce and study five types of PC acyclicity in edge-colored graphs such that graphs of PC acyclicity of type i is a proper superset of graphs of acyclicity of type...
Building Scalable Knowledge Graphs for Earth Science
Ramachandran, Rahul; Maskey, Manil; Gatlin, Patrick; Zhang, Jia; Duan, Xiaoyi; Miller, J. J.; Bugbee, Kaylin; Christopher, Sundar; Freitag, Brian
2017-01-01
Knowledge Graphs link key entities in a specific domain with other entities via relationships. From these relationships, researchers can query knowledge graphs for probabilistic recommendations to infer new knowledge. Scientific papers are an untapped resource which knowledge graphs could leverage to accelerate research discovery. Goal: Develop an end-to-end (semi) automated methodology for constructing Knowledge Graphs for Earth Science.
Port-Hamiltonian Systems on Open Graphs
Schaft, A.J. van der; Maschke, B.M.
2010-01-01
In this talk we discuss how to define in an intrinsic manner port-Hamiltonian dynamics on open graphs. Open graphs are graphs where some of the vertices are boundary vertices (terminals), which allow interconnection with other systems. We show that a directed graph carries two natural Dirac
Constructing Dense Graphs with Unique Hamiltonian Cycles
Lynch, Mark A. M.
2012-01-01
It is not difficult to construct dense graphs containing Hamiltonian cycles, but it is difficult to generate dense graphs that are guaranteed to contain a unique Hamiltonian cycle. This article presents an algorithm for generating arbitrarily large simple graphs containing "unique" Hamiltonian cycles. These graphs can be turned into dense graphs…
Skew-adjacency matrices of graphs
Cavers, M.; Cioaba, S.M.; Fallat, S.; Gregory, D.A.; Haemers, W.H.; Kirkland, S.J.; McDonald, J.J.; Tsatsomeros, M.
2012-01-01
The spectra of the skew-adjacency matrices of a graph are considered as a possible way to distinguish adjacency cospectral graphs. This leads to the following topics: graphs whose skew-adjacency matrices are all cospectral; relations between the matchings polynomial of a graph and the characteristic
Commuting graphs of matrix algebras
International Nuclear Information System (INIS)
Akbari, S.; Bidkhori, H.; Mohammadian, A.
2006-08-01
The commuting graph of a ring R, denoted by Γ(R), is a graph whose vertices are all non- central elements of R and two distinct vertices x and y are adjacent if and only if xy = yx. The commuting graph of a group G, denoted by Γ(G), is similarly defined. In this paper we investigate some graph theoretic properties of Γ(M n (F)), where F is a field and n ≥ 2. Also we study the commuting graphs of some classical groups such as GL n (F) and SL n (F). We show that Γ(M n (F)) is a connected graph if and only if every field extension of F of degree n contains a proper intermediate field. We prove that apart from finitely many fields, a similar result is true for Γ(GL n (F)) and Γ(SL n (F)). Also we show that for two fields E and F and integers m, n ≥> 2, if Γ(M m (E)) ≅ Γ(M n (F)), then m = n and vertical bar E vertical bar = vertical bar F vertical bar. (author)
Graph Quasicontinuous Functions and Densely Continuous Forms
Directory of Open Access Journals (Sweden)
Lubica Hola
2017-07-01
Full Text Available Let $X, Y$ be topological spaces. A function $f: X \\to Y$ is said to be graph quasicontinuous if there is a quasicontinuous function $g: X \\to Y$ with the graph of $g$ contained in the closure of the graph of $f$. There is a close relation between the notions of graph quasicontinuous functions and minimal usco maps as well as the notions of graph quasicontinuous functions and densely continuous forms. Every function with values in a compact Hausdorff space is graph quasicontinuous; more generally every locally compact function is graph quasicontinuous.
The forwarding indices of graphs - a survey
Directory of Open Access Journals (Sweden)
Jun-Ming Xu
2013-01-01
Full Text Available A routing \\(R\\ of a connected graph \\(G\\ of order \\(n\\ is a collection of \\(n(n-1\\ simple paths connecting every ordered pair of vertices of \\(G\\. The vertex-forwarding index \\(\\xi(G,R\\ of \\(G\\ with respect to a routing \\(R\\ is defined as the maximum number of paths in \\(R\\ passing through any vertex of \\(G\\. The vertex-forwarding index \\(\\xi(G\\ of \\(G\\ is defined as the minimum \\(\\xi(G,R\\ over all routings \\(R\\ of \\(G\\. Similarly, the edge-forwarding index \\(\\pi(G,R\\ of \\(G\\ with respect to a routing \\(R\\ is the maximum number of paths in \\(R\\ passing through any edge of \\(G\\. The edge-forwarding index \\(\\pi(G\\ of \\(G\\ is the minimum \\(\\pi(G,R\\ over all routings \\(R\\ of \\(G\\. The vertex-forwarding index or the edge-forwarding index corresponds to the maximum load of the graph. Therefore, it is important to find routings minimizing these indices and thus has received much research attention for over twenty years. This paper surveys some known results on these forwarding indices, further research problems and several conjectures, also states some difficulty and relations to other topics in graph theory.
Graph balancing: a special case of scheduling unrelated parallel machines
Czech Academy of Sciences Publication Activity Database
Ebenlendr, Tomáš; Krčál, M.; Sgall, J.
2014-01-01
Roč. 68, č. 1 (2014), s. 62-80 ISSN 0178-4617 R&D Projects: GA ČR GBP202/12/G061; GA MŠk(CZ) 1M0545; GA AV ČR IAA100190902 Institutional support: RVO:67985840 Keywords : approximation algorithms * weighted outdegree * orientation Subject RIV: BA - General Mathematics Impact factor: 0.791, year: 2014 http://link.springer.com/article/10.1007%2Fs00453-012-9668-9
Graph Learning in Knowledge Bases
Energy Technology Data Exchange (ETDEWEB)
Goldberg, Sean [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wang, Daisy Zhe [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-09-01
The amount of text data has been growing exponentially in recent years, giving rise to automatic information extraction methods that store text annotations in a database. The current state-of-theart structured prediction methods, however, are likely to contain errors and it’s important to be able to manage the overall uncertainty of the database. On the other hand, the advent of crowdsourcing has enabled humans to aid machine algorithms at scale. As part of this project we introduced pi-CASTLE , a system that optimizes and integrates human and machine computing as applied to a complex structured prediction problem involving conditional random fields (CRFs). We proposed strategies grounded in information theory to select a token subset, formulate questions for the crowd to label, and integrate these labelings back into the database using a method of constrained inference. On both a text segmentation task over academic citations and a named entity recognition task over tweets we showed an order of magnitude improvement in accuracy gain over baseline methods.
Interactive Graph Layout of a Million Nodes
Peng Mi; Maoyuan Sun; Moeti Masiane; Yong Cao; Chris North
2016-01-01
Sensemaking of large graphs, specifically those with millions of nodes, is a crucial task in many fields. Automatic graph layout algorithms, augmented with real-time human-in-the-loop interaction, can potentially support sensemaking of large graphs. However, designing interactive algorithms to achieve this is challenging. In this paper, we tackle the scalability problem of interactive layout of large graphs, and contribute a new GPU-based force-directed layout algorithm that exploits graph to...
Khovanov homology of graph-links
Energy Technology Data Exchange (ETDEWEB)
Nikonov, Igor M [M. V. Lomonosov Moscow State University, Faculty of Mechanics and Mathematics, Moscow (Russian Federation)
2012-08-31
Graph-links arise as the intersection graphs of turning chord diagrams of links. Speaking informally, graph-links provide a combinatorial description of links up to mutations. Many link invariants can be reformulated in the language of graph-links. Khovanov homology, a well-known and useful knot invariant, is defined for graph-links in this paper (in the case of the ground field of characteristic two). Bibliography: 14 titles.
PRIVATE GRAPHS – ACCESS RIGHTS ON GRAPHS FOR SEAMLESS NAVIGATION
Directory of Open Access Journals (Sweden)
W. Dorner
2016-06-01
Full Text Available After the success of GNSS (Global Navigational Satellite Systems and navigation services for public streets, indoor seems to be the next big development in navigational services, relying on RTLS – Real Time Locating Services (e.g. WIFI and allowing seamless navigation. In contrast to navigation and routing services on public streets, seamless navigation will cause an additional challenge: how to make routing data accessible to defined users or restrict access rights for defined areas or only to parts of the graph to a defined user group? The paper will present case studies and data from literature, where seamless and especially indoor navigation solutions are presented (hospitals, industrial complexes, building sites, but the problem of restricted access rights was only touched from a real world, but not a technical perspective. The analysis of case studies will show, that the objective of navigation and the different target groups for navigation solutions will demand well defined access rights and require solutions, how to make only parts of a graph to a user or application available to solve a navigational task. The paper will therefore introduce the concept of private graphs, which is defined as a graph for navigational purposes covering the street, road or floor network of an area behind a public street and suggest different approaches how to make graph data for navigational purposes available considering access rights and data protection, privacy and security issues as well.
Linked Data is People: Building a Knowledge Graph to Reshape the Library Staff Directory
Directory of Open Access Journals (Sweden)
Jason A. Clark
2017-04-01
Full Text Available One of our greatest library resources is people. Most libraries have staff directory information published on the web, yet most of this data is trapped in local silos, PDFs, or unstructured HTML markup. With this in mind, the library informatics team at Montana State University (MSU Library set a goal of remaking our people pages by connecting the local staff database to the Linked Open Data (LOD cloud. In pursuing linked data integration for library staff profiles, we have realized two primary use cases: improving the search engine optimization (SEO for people pages and creating network graph visualizations. In this article, we will focus on the code to build this library graph model as well as the linked data workflows and ontology expressions developed to support it. Existing linked data work has largely centered around machine-actionable data and improvements for bots or intelligent software agents. Our work demonstrates that connecting your staff directory to the LOD cloud can reveal relationships among people in dynamic ways, thereby raising staff visibility and bringing an increased level of understanding and collaboration potential for one of our primary assets: the people that make the library happen.
Overlapping community detection based on link graph using distance dynamics
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.
Benchmarking Measures of Network Controllability on Canonical Graph Models
Wu-Yan, Elena; Betzel, Richard F.; Tang, Evelyn; Gu, Shi; Pasqualetti, Fabio; Bassett, Danielle S.
2018-03-01
The control of networked dynamical systems opens the possibility for new discoveries and therapies in systems biology and neuroscience. Recent theoretical advances provide candidate mechanisms by which a system can be driven from one pre-specified state to another, and computational approaches provide tools to test those mechanisms in real-world systems. Despite already having been applied to study network systems in biology and neuroscience, the practical performance of these tools and associated measures on simple networks with pre-specified structure has yet to be assessed. Here, we study the behavior of four control metrics (global, average, modal, and boundary controllability) on eight canonical graphs (including Erdős-Rényi, regular, small-world, random geometric, Barábasi-Albert preferential attachment, and several modular networks) with different edge weighting schemes (Gaussian, power-law, and two nonparametric distributions from brain networks, as examples of real-world systems). We observe that differences in global controllability across graph models are more salient when edge weight distributions are heavy-tailed as opposed to normal. In contrast, differences in average, modal, and boundary controllability across graph models (as well as across nodes in the graph) are more salient when edge weight distributions are less heavy-tailed. Across graph models and edge weighting schemes, average and modal controllability are negatively correlated with one another across nodes; yet, across graph instances, the relation between average and modal controllability can be positive, negative, or nonsignificant. Collectively, these findings demonstrate that controllability statistics (and their relations) differ across graphs with different topologies and that these differences can be muted or accentuated by differences in the edge weight distributions. More generally, our numerical studies motivate future analytical efforts to better understand the mathematical
Vector grammars and PN machines
Institute of Scientific and Technical Information of China (English)
蒋昌俊
1996-01-01
The concept of vector grammars under the string semantic is introduced.The dass of vector grammars is given,which is similar to the dass of Chomsky grammars.The regular vector grammar is divided further.The strong and weak relation between the vector grammar and scalar grammar is discussed,so the spectrum system graph of scalar and vector grammars is made.The equivalent relation between the regular vector grammar and Petri nets (also called PN machine) is pointed.The hybrid PN machine is introduced,and its language is proved equivalent to the language of the context-free vector grammar.So the perfect relation structure between vector grammars and PN machines is formed.
Eigenfunction statistics on quantum graphs
International Nuclear Information System (INIS)
Gnutzmann, S.; Keating, J.P.; Piotet, F.
2010-01-01
We investigate the spatial statistics of the energy eigenfunctions on large quantum graphs. It has previously been conjectured that these should be described by a Gaussian Random Wave Model, by analogy with quantum chaotic systems, for which such a model was proposed by Berry in 1977. The autocorrelation functions we calculate for an individual quantum graph exhibit a universal component, which completely determines a Gaussian Random Wave Model, and a system-dependent deviation. This deviation depends on the graph only through its underlying classical dynamics. Classical criteria for quantum universality to be met asymptotically in the large graph limit (i.e. for the non-universal deviation to vanish) are then extracted. We use an exact field theoretic expression in terms of a variant of a supersymmetric σ model. A saddle-point analysis of this expression leads to the estimates. In particular, intensity correlations are used to discuss the possible equidistribution of the energy eigenfunctions in the large graph limit. When equidistribution is asymptotically realized, our theory predicts a rate of convergence that is a significant refinement of previous estimates. The universal and system-dependent components of intensity correlation functions are recovered by means of an exact trace formula which we analyse in the diagonal approximation, drawing in this way a parallel between the field theory and semiclassics. Our results provide the first instance where an asymptotic Gaussian Random Wave Model has been established microscopically for eigenfunctions in a system with no disorder.
Directory of Open Access Journals (Sweden)
Andreas P. Braun
2016-04-01
Full Text Available Box graphs succinctly and comprehensively characterize singular fibers of elliptic fibrations in codimension two and three, as well as flop transitions connecting these, in terms of representation theoretic data. We develop a framework that provides a systematic map between a box graph and a crepant algebraic resolution of the singular elliptic fibration, thus allowing an explicit construction of the fibers from a singular Weierstrass or Tate model. The key tool is what we call a fiber face diagram, which shows the relevant information of a (partial toric triangulation and allows the inclusion of more general algebraic blowups. We shown that each such diagram defines a sequence of weighted algebraic blowups, thus providing a realization of the fiber defined by the box graph in terms of an explicit resolution. We show this correspondence explicitly for the case of SU(5 by providing a map between box graphs and fiber faces, and thereby a sequence of algebraic resolutions of the Tate model, which realizes each of the box graphs.
Degree-based graph construction
International Nuclear Information System (INIS)
Kim, Hyunju; Toroczkai, Zoltan; Erdos, Peter L; Miklos, Istvan; Szekely, Laszlo A
2009-01-01
Degree-based graph construction is a ubiquitous problem in network modelling (Newman et al 2006 The Structure and Dynamics of Networks (Princeton Studies in Complexity) (Princeton, NJ: Princeton University Press), Boccaletti et al 2006 Phys. Rep. 424 175), ranging from social sciences to chemical compounds and biochemical reaction networks in the cell. This problem includes existence, enumeration, exhaustive construction and sampling questions with aspects that are still open today. Here we give necessary and sufficient conditions for a sequence of nonnegative integers to be realized as a simple graph's degree sequence, such that a given (but otherwise arbitrary) set of connections from an arbitrarily given node is avoided. We then use this result to present a swap-free algorithm that builds all simple graphs realizing a given degree sequence. In a wider context, we show that our result provides a greedy construction method to build all the f-factor subgraphs (Tutte 1952 Can. J. Math. 4 314) embedded within K n setmn S k , where K n is the complete graph and S k is a star graph centred on one of the nodes. (fast track communication)
Hierarchical organisation of causal graphs
International Nuclear Information System (INIS)
Dziopa, P.
1993-01-01
This paper deals with the design of a supervision system using a hierarchy of models formed by graphs, in which the variables are the nodes and the causal relations between the variables of the arcs. To obtain a representation of the variables evolutions which contains only the relevant features of their real evolutions, the causal relations are completed with qualitative transfer functions (QTFs) which produce roughly the behaviour of the classical transfer functions. Major improvements have been made in the building of the hierarchical organization. First, the basic variables of the uppermost level and the causal relations between them are chosen. The next graph is built by adding intermediary variables to the upper graph. When the undermost graph has been built, the transfer functions parameters corresponding to its causal relations are identified. The second task consists in the upwelling of the information from the undermost graph to the uppermost one. A fusion procedure of the causal relations has been designed to compute the QFTs relevant for each level. This procedure aims to reduce the number of parameters needed to represent an evolution at a high level of abstraction. These techniques have been applied to the hierarchical modelling of nuclear process. (authors). 8 refs., 12 figs
Experimental quantum annealing: case study involving the graph isomorphism problem.
Zick, Kenneth M; Shehab, Omar; French, Matthew
2015-06-08
Quantum annealing is a proposed combinatorial optimization technique meant to exploit quantum mechanical effects such as tunneling and entanglement. Real-world quantum annealing-based solvers require a combination of annealing and classical pre- and post-processing; at this early stage, little is known about how to partition and optimize the processing. This article presents an experimental case study of quantum annealing and some of the factors involved in real-world solvers, using a 504-qubit D-Wave Two machine and the graph isomorphism problem. To illustrate the role of classical pre-processing, a compact Hamiltonian is presented that enables a reduced Ising model for each problem instance. On random N-vertex graphs, the median number of variables is reduced from N(2) to fewer than N log2 N and solvable graph sizes increase from N = 5 to N = 13. Additionally, error correction via classical post-processing majority voting is evaluated. While the solution times are not competitive with classical approaches to graph isomorphism, the enhanced solver ultimately classified correctly every problem that was mapped to the processor and demonstrated clear advantages over the baseline approach. The results shed some light on the nature of real-world quantum annealing and the associated hybrid classical-quantum solvers.
Directory of Open Access Journals (Sweden)
Štefánia Salokyová
2016-06-01
Full Text Available The article observes the amount of vibration on the bearing house of a turning lathe selected in advance through the change of the revolutions per minute and the thickness of the removed material in frontal type of lathe processing. Increase in mechanical vibration values depending on the value of nominal thickness of splinter was observed during changing technological parameters of the drilling process as a consequence of rotation speed of the motor. The vibration acceleration amplitude course changes depending on the frequencies are evaluated together for 400, 800 and 1200 motor r/min. A piezoelectric sensor of the type 4507B-004 from the Brüel & Kjaer Company was used for monitoring the frequency analysis of the vibration, which was attached to the bearing house of the lathe TOS SV 18RB. The vibration signal measured during the processing and during the time period is transformed through the means of a quick Fourier transformation to the frequency spectrum in the range of 3.0–10.0 kHz. Measured values of vibration acceleration amplitude were processed and evaluated by the SignalExpress software. Graphical abstract Unwanted vibration in machine tools like lathe is one of the main problems as it affects the quality of the machined parts and tool life and creates noise during machining operation. Bearings are of paramount importance to almost all forms of rotating machinery and are the most common among machine elements. The article describes in more detail the issue of vibrations created when machining the material by lathe turning. It also includes execution, experiment evaluation in this field, and comparison of measured vibrations’ acceleration amplitude values according to the standards.
Graph based techniques for tag cloud generation
DEFF Research Database (Denmark)
Leginus, Martin; Dolog, Peter; Lage, Ricardo Gomes
2013-01-01
Tag cloud is one of the navigation aids for exploring documents. Tag cloud also link documents through the user defined terms. We explore various graph based techniques to improve the tag cloud generation. Moreover, we introduce relevance measures based on underlying data such as ratings...... or citation counts for improved measurement of relevance of tag clouds. We show, that on the given data sets, our approach outperforms the state of the art baseline methods with respect to such relevance by 41 % on Movielens dataset and by 11 % on Bibsonomy data set....
Brueggemann, T.; Hurink, Johann L.; Vredeveld, T.; Woeginger, Gerhard
2006-01-01
We study the problem of minimizing the makespan on m parallel machines. We introduce a very large-scale neighborhood of exponential size (in the number of machines) that is based on a matching in a complete graph. The idea is to partition the jobs assigned to the same machine into two sets. This
Graph modeling systems and methods
Neergaard, Mike
2015-10-13
An apparatus and a method for vulnerability and reliability modeling are provided. The method generally includes constructing a graph model of a physical network using a computer, the graph model including a plurality of terminating vertices to represent nodes in the physical network, a plurality of edges to represent transmission paths in the physical network, and a non-terminating vertex to represent a non-nodal vulnerability along a transmission path in the physical network. The method additionally includes evaluating the vulnerability and reliability of the physical network using the constructed graph model, wherein the vulnerability and reliability evaluation includes a determination of whether each terminating and non-terminating vertex represents a critical point of failure. The method can be utilized to evaluate wide variety of networks, including power grid infrastructures, communication network topologies, and fluid distribution systems.
Feder, Tomá s; Motwani, Rajeev
2009-01-01
Results on graph turnpike problem without distinctness, including its NP-completeness, and an O(m+n log n) algorithm, is presented. The usual turnpike problem has all pairwise distances given, but does not specify which pair of vertices w e corresponds to. There are two other problems that can be viewed as special cases of the graph turnpike problem, including the bandwidth problem and the low-distortion graph embedding problem. The aim for the turnpike problem in the NP-complete is to orient the edges with weights w i in either direction so that when the whole cycle is transversed in the real line, it returns to a chosen starting point for the cycle. An instance of the turnpike problem with or without distinctness is uniquely mappable if there exists at most one solution up to translation and choice of orientation.
Feder, Tomás
2009-06-01
Results on graph turnpike problem without distinctness, including its NP-completeness, and an O(m+n log n) algorithm, is presented. The usual turnpike problem has all pairwise distances given, but does not specify which pair of vertices w e corresponds to. There are two other problems that can be viewed as special cases of the graph turnpike problem, including the bandwidth problem and the low-distortion graph embedding problem. The aim for the turnpike problem in the NP-complete is to orient the edges with weights w i in either direction so that when the whole cycle is transversed in the real line, it returns to a chosen starting point for the cycle. An instance of the turnpike problem with or without distinctness is uniquely mappable if there exists at most one solution up to translation and choice of orientation.
Negation switching invariant signed graphs
Directory of Open Access Journals (Sweden)
Deepa Sinha
2014-04-01
Full Text Available A signed graph (or, $sigraph$ in short is a graph G in which each edge x carries a value $\\sigma(x \\in \\{-, +\\}$ called its sign. Given a sigraph S, the negation $\\eta(S$ of the sigraph S is a sigraph obtained from S by reversing the sign of every edge of S. Two sigraphs $S_{1}$ and $S_{2}$ on the same underlying graph are switching equivalent if it is possible to assign signs `+' (`plus' or `-' (`minus' to vertices of $S_{1}$ such that by reversing the sign of each of its edges that has received opposite signs at its ends, one obtains $S_{2}$. In this paper, we characterize sigraphs which are negation switching invariant and also see for what sigraphs, S and $\\eta (S$ are signed isomorphic.
Graybill, George
2007-01-01
Just how simple are simple machines? With our ready-to-use resource, they are simple to teach and easy to learn! Chocked full of information and activities, we begin with a look at force, motion and work, and examples of simple machines in daily life are given. With this background, we move on to different kinds of simple machines including: Levers, Inclined Planes, Wedges, Screws, Pulleys, and Wheels and Axles. An exploration of some compound machines follows, such as the can opener. Our resource is a real time-saver as all the reading passages, student activities are provided. Presented in s
Machine assisted histogram classification
Benyó, B.; Gaspar, C.; Somogyi, P.
2010-04-01
LHCb is one of the four major experiments under completion at the Large Hadron Collider (LHC). Monitoring the quality of the acquired data is important, because it allows the verification of the detector performance. Anomalies, such as missing values or unexpected distributions can be indicators of a malfunctioning detector, resulting in poor data quality. Spotting faulty or ageing components can be either done visually using instruments, such as the LHCb Histogram Presenter, or with the help of automated tools. In order to assist detector experts in handling the vast monitoring information resulting from the sheer size of the detector, we propose a graph based clustering tool combined with machine learning algorithm and demonstrate its use by processing histograms representing 2D hitmaps events. We prove the concept by detecting ion feedback events in the LHCb experiment's RICH subdetector.
Machine assisted histogram classification
Energy Technology Data Exchange (ETDEWEB)
Benyo, B; Somogyi, P [BME-IIT, H-1117 Budapest, Magyar tudosok koerutja 2. (Hungary); Gaspar, C, E-mail: Peter.Somogyi@cern.c [CERN-PH, CH-1211 Geneve 23 (Switzerland)
2010-04-01
LHCb is one of the four major experiments under completion at the Large Hadron Collider (LHC). Monitoring the quality of the acquired data is important, because it allows the verification of the detector performance. Anomalies, such as missing values or unexpected distributions can be indicators of a malfunctioning detector, resulting in poor data quality. Spotting faulty or ageing components can be either done visually using instruments, such as the LHCb Histogram Presenter, or with the help of automated tools. In order to assist detector experts in handling the vast monitoring information resulting from the sheer size of the detector, we propose a graph based clustering tool combined with machine learning algorithm and demonstrate its use by processing histograms representing 2D hitmaps events. We prove the concept by detecting ion feedback events in the LHCb experiment's RICH subdetector.
The fascinating world of graph theory
Benjamin, Arthur; Zhang, Ping
2015-01-01
Graph theory goes back several centuries and revolves around the study of graphs-mathematical structures showing relations between objects. With applications in biology, computer science, transportation science, and other areas, graph theory encompasses some of the most beautiful formulas in mathematics-and some of its most famous problems. The Fascinating World of Graph Theory explores the questions and puzzles that have been studied, and often solved, through graph theory. This book looks at graph theory's development and the vibrant individuals responsible for the field's growth. Introducin
XML Graphs in Program Analysis
DEFF Research Database (Denmark)
Møller, Anders; Schwartzbach, Michael Ignatieff
2007-01-01
XML graphs have shown to be a simple and effective formalism for representing sets of XML documents in program analysis. It has evolved through a six year period with variants tailored for a range of applications. We present a unified definition, outline the key properties including validation...... of XML graphs against different XML schema languages, and provide a software package that enables others to make use of these ideas. We also survey four very different applications: XML in Java, Java Servlets and JSP, transformations between XML and non-XML data, and XSLT....
Cyclic graphs and Apery's theorem
International Nuclear Information System (INIS)
Sorokin, V N
2002-01-01
This is a survey of results about the behaviour of Hermite-Pade approximants for graphs of Markov functions, and a survey of interpolation problems leading to Apery's result about the irrationality of the value ζ(3) of the Riemann zeta function. The first example is given of a cyclic graph for which the Hermite-Pade problem leads to Apery's theorem. Explicit formulae for solutions are obtained, namely, Rodrigues' formulae and integral representations. The asymptotic behaviour of the approximants is studied, and recurrence formulae are found
Interacting particle systems on graphs
Sood, Vishal
In this dissertation, the dynamics of socially or biologically interacting populations are investigated. The individual members of the population are treated as particles that interact via links on a social or biological network represented as a graph. The effect of the structure of the graph on the properties of the interacting particle system is studied using statistical physics techniques. In the first chapter, the central concepts of graph theory and social and biological networks are presented. Next, interacting particle systems that are drawn from physics, mathematics and biology are discussed in the second chapter. In the third chapter, the random walk on a graph is studied. The mean time for a random walk to traverse between two arbitrary sites of a random graph is evaluated. Using an effective medium approximation it is found that the mean first-passage time between pairs of sites, as well as all moments of this first-passage time, are insensitive to the density of links in the graph. The inverse of the mean-first passage time varies non-monotonically with the density of links near the percolation transition of the random graph. Much of the behavior can be understood by simple heuristic arguments. Evolutionary dynamics, by which mutants overspread an otherwise uniform population on heterogeneous graphs, are studied in the fourth chapter. Such a process underlies' epidemic propagation, emergence of fads, social cooperation or invasion of an ecological niche by a new species. The first part of this chapter is devoted to neutral dynamics, in which the mutant genotype does not have a selective advantage over the resident genotype. The time to extinction of one of the two genotypes is derived. In the second part of this chapter, selective advantage or fitness is introduced such that the mutant genotype has a higher birth rate or a lower death rate. This selective advantage leads to a dynamical competition in which selection dominates for large populations
International Nuclear Information System (INIS)
Carlini, A.; Sasaki, M.
2003-01-01
We address the problem of finding optimal CPTP (completely positive trace-preserving) maps between a set of binary pure states and another set of binary generic mixed state in a two-dimensional space. The necessary and sufficient conditions for the existence of such CPTP maps can be discussed within a simple geometrical picture. We exploit this analysis to show the existence of an optimal quantum repeater which is superior to the known repeating strategies for a set of coherent states sent through a lossy quantum channel. We also show that the geometrical formulation of the CPTP mapping conditions can be a simpler method to derive a state-dependent quantum (anti) cloning machine than the study so far based on the explicit solution of several constraints imposed by unitarity in an extended Hilbert space
Maximilien Brice
2008-01-01
Dr Mauro Dell’Ambrogio, State Secretary for Education and Research of the Swiss Confederation visit the ATLAS Cavern and the LHC Machine with with Collaboration Spokesperson P. Jenni and Technical Coordinator M. Nessi.
Maximization of regional probabilities using Optimal Surface Graphs
DEFF Research Database (Denmark)
Arias Lorza, Andres M.; Van Engelen, Arna; Petersen, Jens
2018-01-01
Purpose: We present a segmentation method that maximizes regional probabilities enclosed by coupled surfaces using an Optimal Surface Graph (OSG) cut approach. This OSG cut determines the globally optimal solution given a graph constructed around an initial surface. While most methods for vessel...... wall segmentation only use edge information, we show that maximizing regional probabilities using an OSG improves the segmentation results. We applied this to automatically segment the vessel wall of the carotid artery in magnetic resonance images. Methods: First, voxel-wise regional probability maps...... were obtained using a Support Vector Machine classifier trained on local image features. Then, the OSG segments the regions which maximizes the regional probabilities considering smoothness and topological constraints. Results: The method was evaluated on 49 carotid arteries from 30 subjects...
Integrated color face graphs for plant accident display
International Nuclear Information System (INIS)
Hara, Fumio
1987-01-01
This paper presents an integrated man-machine interface that uses cartoon-like colored graphs in the form of faces, that, through different facial expressions, display a plant condition. This is done by drawing the face on a CRT by nonlinearly transforming 31 variables and coloring the face. This integrated color graphics technique is applied to display the progess of events in the Three Mile Island nuclear power plant accident. Human visual perceptive characteristics are investigated in relation to the perception of the plant accident process, the naturality in face color change, and the consistency between facial expressions and colors. This paper concludes that colors used in an integrated color face graphs must be completely consistent with emotional feelings perceived from the colors. (author)
Koshka, Yaroslav; Perera, Dilina; Hall, Spencer; Novotny, M A
2017-07-01
The possibility of using a quantum computer D-Wave 2X with more than 1000 qubits to determine the global minimum of the energy landscape of trained restricted Boltzmann machines is investigated. In order to overcome the problem of limited interconnectivity in the D-Wave architecture, the proposed RBM embedding combines multiple qubits to represent a particular RBM unit. The results for the lowest-energy (the ground state) and some of the higher-energy states found by the D-Wave 2X were compared with those of the classical simulated annealing (SA) algorithm. In many cases, the D-Wave machine successfully found the same RBM lowest-energy state as that found by SA. In some examples, the D-Wave machine returned a state corresponding to one of the higher-energy local minima found by SA. The inherently nonperfect embedding of the RBM into the Chimera lattice explored in this work (i.e., multiple qubits combined into a single RBM unit were found not to be guaranteed to be all aligned) and the existence of small, persistent biases in the D-Wave hardware may cause a discrepancy between the D-Wave and the SA results. In some of the investigated cases, introduction of a small bias field into the energy function or optimization of the chain-strength parameter in the D-Wave embedding successfully addressed difficulties of the particular RBM embedding. With further development of the D-Wave hardware, the approach will be suitable for much larger numbers of RBM units.
Critical Behavior of the Annealed Ising Model on Random Regular Graphs
Can, Van Hao
2017-11-01
In Giardinà et al. (ALEA Lat Am J Probab Math Stat 13(1):121-161, 2016), the authors have defined an annealed Ising model on random graphs and proved limit theorems for the magnetization of this model on some random graphs including random 2-regular graphs. Then in Can (Annealed limit theorems for the Ising model on random regular graphs, arXiv:1701.08639, 2017), we generalized their results to the class of all random regular graphs. In this paper, we study the critical behavior of this model. In particular, we determine the critical exponents and prove a non standard limit theorem stating that the magnetization scaled by n^{3/4} converges to a specific random variable, with n the number of vertices of random regular graphs.
Xu, Zhiqiang
2017-02-16
Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.
Xu, Zhiqiang; Cheng, James; Xiao, Xiaokui; Fujimaki, Ryohei; Muraoka, Yusuke
2017-01-01
Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.
AstroML: "better, faster, cheaper" towards state-of-the-art data mining and machine learning
Ivezic, Zeljko; Connolly, Andrew J.; Vanderplas, Jacob
2015-01-01
We present AstroML, a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, matplotlib, and astropy, and distributed under an open license. AstroML contains a growing library of statistical and machine learning routines for analyzing astronomical data in Python, loaders for several open astronomical datasets (such as SDSS and other recent major surveys), and a large suite of examples of analyzing and visualizing astronomical datasets. AstroML is especially suitable for introducing undergraduate students to numerical research projects and for graduate students to rapidly undertake cutting-edge research. The long-term goal of astroML is to provide a community repository for fast Python implementations of common tools and routines used for statistical data analysis in astronomy and astrophysics (see http://www.astroml.org).
Machining dynamics fundamentals, applications and practices
Cheng, Kai
2008-01-01
Machining dynamics are vital to the performance of machine tools and machining processes in manufacturing. This book discusses the state-of-the-art applications, practices and research in machining dynamics. It presents basic theory, analysis and control methodology. It is useful for manufacturing engineers, supervisors, engineers and designers.
Multiple graph regularized protein domain ranking
Wang, Jim Jing-Yan
2012-11-19
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. 2012 Wang et al; licensee BioMed Central Ltd.
Multiple graph regularized protein domain ranking
Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin
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. 2012 Wang et al; licensee BioMed Central Ltd.
Multiple graph regularized protein domain ranking.
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
Directory of Open Access Journals (Sweden)
Wang Jim
2012-11-01
Full Text Available Abstract 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.
Energy Technology Data Exchange (ETDEWEB)
Hindle, D.
1999-06-01
The article surveys latest equipment available from the world`s manufacturers of a range of machines for tunnelling. These are grouped under headings: excavators; impact hammers; road headers; and shields and tunnel boring machines. Products of thirty manufacturers are referred to. Addresses and fax numbers of companies are supplied. 5 tabs., 13 photos.
El-Refaie, Ayman Mohamed Fawzi [Niskayuna, NY; Reddy, Patel Bhageerath [Madison, WI
2012-07-17
An interior permanent magnet electric machine is disclosed. The interior permanent magnet electric machine comprises a rotor comprising a plurality of radially placed magnets each having a proximal end and a distal end, wherein each magnet comprises a plurality of magnetic segments and at least one magnetic segment towards the distal end comprises a high resistivity magnetic material.
Kirrane, Diane E.
1990-01-01
As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)
International Nuclear Information System (INIS)
Ritson, D.
1989-05-01
This talk examines methods available to minimize, but never entirely eliminate, degradation of machine performance caused by terrain following. Breaking of planar machine symmetry for engineering convenience and/or monetary savings must be balanced against small performance degradation, and can only be decided on a case-by-case basis. 5 refs
Prieto, L. P.; Sharma, K.; Kidzinski, L.; Rodríguez-Triana, M. J.; Dillenbourg, P.
2018-01-01
The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration graphs (teaching activities and their social plane over time)…
A Graph-Based Approach to Action Scheduling in a Parallel Database System
Grefen, P.W.P.J.; Apers, Peter M.G.
Parallel database machines are meant to obtain high performance in transaction processing, both in terms of response time adn throughput. To obtain high performance, a good scheduling of the execution of the various actions in transactions is crucial. This paper describes a graph-based technique for
Bond graphs for modelling, control and fault diagnosis of engineering systems
2017-01-01
This book presents theory and latest application work in Bond Graph methodology with a focus on: • Hybrid dynamical system models, • Model-based fault diagnosis, model-based fault tolerant control, fault prognosis • and also addresses • Open thermodynamic systems with compressible fluid flow, • Distributed parameter models of mechanical subsystems. In addition, the book covers various applications of current interest ranging from motorised wheelchairs, in-vivo surgery robots, walking machines to wind-turbines.The up-to-date presentation has been made possible by experts who are active members of the worldwide bond graph modelling community. This book is the completely revised 2nd edition of the 2011 Springer compilation text titled Bond Graph Modelling of Engineering Systems – Theory, Applications and Software Support. It extends the presentation of theory and applications of graph methodology by new developments and latest research results. Like the first edition, this book addresses readers in a...
A Machine Learning Concept for DTN Routing
Dudukovich, Rachel; Hylton, Alan; Papachristou, Christos
2017-01-01
This paper discusses the concept and architecture of a machine learning based router for delay tolerant space networks. The techniques of reinforcement learning and Bayesian learning are used to supplement the routing decisions of the popular Contact Graph Routing algorithm. An introduction to the concepts of Contact Graph Routing, Q-routing and Naive Bayes classification are given. The development of an architecture for a cross-layer feedback framework for DTN (Delay-Tolerant Networking) protocols is discussed. Finally, initial simulation setup and results are given.
Constructing Knowledge Graphs of Depression
Huang, Zhisheng; Yang, Jie; van Harmelen, Frank; Hu, Qing
2017-01-01
Knowledge Graphs have been shown to be useful tools for integrating multiple medical knowledge sources, and to support such tasks as medical decision making, literature retrieval, determining healthcare quality indicators, co-morbodity analysis and many others. A large number of medical knowledge