Use Of Global Information Research Articles

Motif-Plus: incorporation of network motifs into top-n friendship recommendations

Although recently there have been a variety of attempts for friendship recommendations, most existing approaches overemphasise the use of global information implied in historical data while overlooking such essential transitivity rule in social networks as the 'triadic closure' principle. To overcome this limitation, we analyse triangular network motifs in social networks and design a novel principle named 'triangular power' for friendship recommendations on the basis of the 'triadic closure' rule. With this principle, we propose to quantify the willing-power that drives two people become friends by making use of triangular motifs and propose a novel strategy called Motif-Plus that utilise local information of a social network for friendship recommendations. We further incorporate this strategy into existing recommendation methods by aggregating ranks generated by individual approaches and Motif-Plus. With large-scale random sub-sampling validation experiments on three real social network datasets (Facebook, Twitter and Delicious), we compare the performance of five existing methods with Motif-Plus incorporated to their original forms. Results show that methods with Motif-Plus outperform their original forms in not only accuracy and retrieval criteria but also recommendation diversity significantly, suggesting that friendship recommendations will be benefit from the fusion of global and local information of social networks.

Evolutionary computation with spatial receding horizon control to minimize network coding resources.

The minimization of network coding resources, such as coding nodes and links, is a challenging task, not only because it is a NP-hard problem, but also because the problem scale is huge; for example, networks in real world may have thousands or even millions of nodes and links. Genetic algorithms (GAs) have a good potential of resolving NP-hard problems like the network coding problem (NCP), but as a population-based algorithm, serious scalability and applicability problems are often confronted when GAs are applied to large- or huge-scale systems. Inspired by the temporal receding horizon control in control engineering, this paper proposes a novel spatial receding horizon control (SRHC) strategy as a network partitioning technology, and then designs an efficient GA to tackle the NCP. Traditional network partitioning methods can be viewed as a special case of the proposed SRHC, that is, one-step-wide SRHC, whilst the method in this paper is a generalized N-step-wide SRHC, which can make a better use of global information of network topologies. Besides the SRHC strategy, some useful designs are also reported in this paper. The advantages of the proposed SRHC and GA for the NCP are illustrated by extensive experiments, and they have a good potential of being extended to other large-scale complex problems.

Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules

We propose a new deterministic approach to coreference resolution that combines the global information and precise features of modern machine-learning models with the transparency and modularity of deterministic, rule-based systems. Our sieve architecture applies a battery of deterministic coreference models one at a time from highest to lowest precision, where each model builds on the previous model's cluster output. The two stages of our sieve-based architecture, a mention detection stage that heavily favors recall, followed by coreference sieves that are precision-oriented, offer a powerful way to achieve both high precision and high recall. Further, our approach makes use of global information through an entity-centric model that encourages the sharing of features across all mentions that point to the same real-world entity. Despite its simplicity, our approach gives state-of-the-art performance on several corpora and genres, and has also been incorporated into hybrid state-of-the-art coreference systems for Chinese and Arabic. Our system thus offers a new paradigm for combining knowledge in rule-based systems that has implications throughout computational linguistics.

Circumventing the Price of Anarchy: Leading Dynamics to Good Behavior

Many natural games have a dramatic difference between the quality of their best and worst Nash equilibria, even in pure strategies. Yet, nearly all results to date on dynamics in games show only convergence to some equilibrium, especially within a polynomial number of steps. In this work we initiate a theory of how well-motivated multiagent dynamics can make use of global information about the game---which might be common knowledge or injected into the system by a helpful central agency---and show that in a wide range of interesting games this can allow the dynamics to quickly reach (within a polynomial number of steps) states of cost comparable to the best Nash equilibrium. We present several natural models for dynamics that can use such additional information and analyze their ability to reach low-cost states for two important and widely studied classes of potential games: network design with fair cost-sharing and party affiliation games (which include consensus and cut games). From the perspective of a...

A stability approach to convergence of curve evolution methods

This paper considers the termination criterion problem in curve evolution methods. First, we investigate the general scheme of curve evolution methods by analysing energy variation. Then, conventional termination criteria are discussed and some drawbacks are identified. Furthermore, the relationship between stability and convergence is discussed based on the Lax equivalence theorem. We then propose a new termination criterion based on a Bayesian approach and the use of global image information. The application of the proposed criterion in a given curve evolution scheme is presented. Experimental results show that the proposed termination criterion is effective and accurate compared to conventional criteria. In addition, it is successful in alleviating typical difficulties such as premature termination and weak edge leakage because of the use of global information in controlling the termination process.

Analysis of global multiscale finite element methods for wave equations with continuum spatial scales

In this paper, we discuss a numerical multiscale approach for solving wave equations with heterogeneous coefficients. Our interest comes from geophysics applications and we assume that there is no scale separation with respect to spatial variables. To obtain the solution of these multiscale problems on a coarse grid, we compute global fields such that the solution smoothly depends on these fields. We present a Galerkin multiscale finite element method using the global information and provide a convergence analysis when applied to solve the wave equations. We investigate the relation between the smoothness of the global fields and convergence rates of the global Galerkin multiscale finite element method for the wave equations. Numerical examples demonstrate that the use of global information renders better accuracy for wave equations with heterogeneous coefficients than the local multiscale finite element method.

Self-organization of asymmetric associative networks

Introduction Associative networks serve as basic paradigms for memory retrieval [1-3] and have been used to describe properties of many neuronal structures [4,5]. In contrast, learning via activity dependent adaptation of real synapses is still not well understood. In particular, the Hopfield Model [2] artificially enforces synapses to represent neuronal correlations according to the Hebbian rule that unrealistically leads to symmetric couplings, catastrophic forgetting when large amounts of patterns are to be stored and explosion of weights when the Hebbian rule is applied iteratively. Various modifications of the simple Hebbian rule have been proposed, as for instance the use of global information to control Hebbian contributions to the weight matrix [6] that prohibits catastrophic forgetting and weight explosion. In contrast, we present a local learning rule that in large networks of N neurons can stabilize more than 1.6 N binary non-sparse random patterns (see Figure 1). When applied in an on-line fashion, it leads to retention of the stack of recent patterns without attrition. The synaptic algorithm turns out to be consistent with spike-timing dependent plasticity as observed in hippocampus and cortex. In fact, it resembles the perceptron rule [3] by modifying a synapse only when the postsynaptic neuron changes its activity. We also find that the mutual interaction of network dynamics with weight changes confines synaptic strength to finite values despite the formal instability of the local synaptic learning rule. Taken together our work suggests that stabilization could provide a unifying principle of weight-activity co-evolution, leading to large storage capacity, on-line ability, generalization and extraction of higher order correlations which has testable implications for synaptic dynamics and cortical function.

Adaptively Combining Local with Global Information for Natural Scenes Categorization

This paper proposes the Extended Bag-of-Visterms (EBOV) to represent semantic scenes. In previous methods, most representations are bag-of-visterms (BOV), where visterms referred to the quantized local texture information. Our new representation is built by introducing global texture information to extend standard bag-of-visterms. In particular we apply the adaptive weight to fuse the local and global information together in order to provide a better visterm representation. Given these representations, scene classification can be performed by pLSA (probabilistic Latent Semantic Analysis) model. The experiment results show that the appropriate use of global information improves the performance of scene classification, as compared with BOV representation that only takes the local information into account.

Accurate multiscale finite element methods for two-phase flow simulations

In this paper we propose a modified multiscale finite element method for two-phase flow simulations in heterogeneous porous media. The main idea of the method is to use the global fine-scale solution at initial time to determine the boundary conditions of the basis functions. This method provides a significant improvement in two-phase flow simulations in porous media where the long-range effects are important. This is typical for some recent benchmark tests, such as the SPE comparative solution project [M. Christie, M. Blunt, Tenth spe comparative solution project: a comparison of upscaling techniques, SPE Reser. Eval. Eng. 4 (2001) 308–317], where porous media have a channelized structure. The use of global information allows us to capture the long-range effects more accurately compared to the multiscale finite element methods that use only local information to construct the basis functions. We present some analysis of the proposed method to illustrate that the method can indeed capture the long-range effect in channelized media.

Integration of local and global shape analysis for logo classification

A comparison is made of global and local methods for the shape analysis of logos in an image database. The qualities of the methods are judged by using the shape signatures to define a similarity metric on the logos. As representatives for the two classes of methods, we use the negative shape method which is based on local shape information and a wavelet-based method which makes use of global information. We apply both methods to images with different kinds of degradations and examine how a particular degradation highlights the strengths and shortcomings of each method. Finally, we use these results to develop a new adaptive weighting scheme which is based on the relative performances of the two methods. This scheme gives rise to a new method that is much more robust with respect to all degradations examined and works by automatically predicting if the negative shape or wavelet method is performing better.

Node and element resequencing using the Laplacian of a finite element graph: Part II—Implementation and numerical results

AbstractIn Part I of this work, Paulino et al.1 have presented an algorithm for profile and wavefront reduction of large sparse matrices of symmetric configuration. This algorithm is based on spectral properties of a Finite Element Graph (FEG), An FEG has been defined as a nodal graph G, a dual graph G* or a communication graph G˙ associated with a generic finite element mesh. The novel algorithm has been called Spectral FEG Resequencing (SFR). This algorithm has specific features that distinguish it from previous algorithms. These features include (1) use of global information in the graph, (2) no need of a pseudoperipheral vertex or the endpoints of a pseudodiameter, and (3) no need of any type of level structure of the FEG. To validate this algorithm in a numerical sense, extensive computational testing on a variety of problems is presented here. This includes algorithmic performance evaluation using a library of benchmark test problems which contains both connected and non‐connected graphs, study of the algebraic connectivity (λ2) of an FEG, eigensolver convergence verification, running time performance evaluation and assessment of the algorithm on a set of practical finite element examples. It is shown that the SFR algorithm is effective in reordering nodes and/or elements of generic finite element meshes. Moreover, it computes orderings which compare favourably with the ones obtained by some previous algorithms that have been published in the technical literature.

Computer-aided analysis of fluidized bed to external wall local heat transfer

Fluidized bed to external wall heat transfer phenomena play an important role in the design and rating of a fluidized bed. In fluidized beds where the heat exchanging surfaces are located along the external walls, maximum heat transfer rates are desirable with a rather uniform distribution over the wall height. Although available models rely upon local hydrodynamic information such as particle velocity and trajectory, gas velocity and void fraction, they cannot provide specific information about these parameters. Instead, the use of global information, derived through certain assumptions or limited analytical expressions, makes the predictions of these models far from realistic. The many empirical equations can provide only average heat transfer rates along the heat exchanging surface and usually yield widely different results. In this study, a computer program was developed to calculate the local bed-to-wall heat transfer, when coupled to the CHEMFLUB computer program which predicts the local hydrodynamics of a fluidized bed [1]. The new program enables the user to adjust various design parameters until a more satisfactory design is reached or to evaluate the heat transfer in the existing beds. This paper is a brief summary of the various heat transfer models and gives sample results for two design cases: a shallow and a narrow bed. Comparisons with available experimental data and correlations are also provided to evaluate the proposed computer model.

Spatial-spectral clustering using recursive spanning trees

The inherent contextual property of spanning trees is exploited in a nonparametric contextual clustering algorithm for multispectral satellite data. The linkage problem associated with shortest spanning trees is avoided by making extensive use of global information, and a two-stage algorithm (segmentation then clustering) is described, each stage being based upon recursive spanning trees and minimax variance techniques. A conditional entropy or ‘segmentation loss’ derived from mutual information is shown to provide a useful indication of the number of segments needed before clustering. The performance of the algorithm is compared with a single-pixel clustering algorithm and shows significant reduction in classification noise, both at class boundaries and within classes, while the spatial resolution of the single-pixel classifier is retained.

Detection of Topology Errors by State Estimation

In modern energy management systems (EMS), there are two types of measurement data collected by the SCADA (supervisory control and data acquisition) system, namely, status data of breakers and switches, and analog data of real and reactive power flows, injections, and bus voltages. The status data are used to determine real-time topology of the network. The analog data are used to determine line and transformer loading and voltage profile. These data are noisy due to measurement errors, communication noise, missing data, etc. In addition to simple checking of the analog data locally, in most modern EMS, state estimation is used to process these data globally to correct errors in the raw analog measurement data. In this paper, the use of global information in state estimation to correct errors in the direct measurements of the status data is proposed and conditions for detectability of errors are analyzed. The telemetered data of breaker and switch status are processed in the EMS computer to determine the present network topology of the system, and this function is called network topology processor. Errors in status data will show up as errors in the network topology. Sasson et al and Dy Liacco et al used a tree search algorithm for the network topology processor. The method is widely used in practice. Bonanomi et al proposed a sequential search method through the network graph. Recently Lugtu et al suggested the approach of using state estimation results for topology error detection.

Image Smoothing by Local Use of Global Information

Image Smoothing by Local Use of Global Information