WorldWideScience

Sample records for network structural features

  1. Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks

    Science.gov (United States)

    Ma, Chuang; Bao, Zhong-Kui; Zhang, Hai-Feng

    2017-10-01

    So far, many network-structure-based link prediction methods have been proposed. However, these methods only highlight one or two structural features of networks, and then use the methods to predict missing links in different networks. The performances of these existing methods are not always satisfied in all cases since each network has its unique underlying structural features. In this paper, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same network, their inner structural features are utterly different. Therefore, more structural features should be considered. However, owing to the remarkably different structural features, the contributions of different features are hard to be given in advance. Inspired by these facts, an adaptive fusion model regarding link prediction is proposed to incorporate multiple structural features. In the model, a logistic function combing multiple structural features is defined, then the weight of each feature in the logistic function is adaptively determined by exploiting the known structure information. Last, we use the "learnt" logistic function to predict the connection probabilities of missing links. According to our experimental results, we find that the performance of our adaptive fusion model is better than many similarity indices.

  2. Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

    Directory of Open Access Journals (Sweden)

    Namatēvs Ivars

    2017-12-01

    Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.

  3. Network structure classification and features of water distribution systems

    Science.gov (United States)

    Giustolisi, Orazio; Simone, Antonietta; Ridolfi, Luca

    2017-04-01

    The network connectivity structure of water distribution systems (WDSs) represents the domain where hydraulic processes occur, driving the emerging behavior of such systems, for example with respect to robustness and vulnerability. In complex network theory (CNT), a common way of classifying the network structure and connectivity is the association of the nodal degree distribution to specific probability distribution models, and during the last decades, researchers classified many real networks using the Poisson or Pareto distributions. In spite of the fact that degree-based network classification could play a crucial role to assess WDS vulnerability, this task is not easy because the network structure of WDSs is strongly constrained by spatial characteristics of the environment where they are constructed. The consequence of these spatial constraints is that the nodal degree spans very small ranges in WDSs hindering a reliable classification by the standard approach based on the nodal degree distribution. This work investigates the classification of the network structure of 22 real WDSs, built in different environments, demonstrating that the Poisson distribution generally models the degree distributions very well. In order to overcome the problem of the reliable classification based on the standard nodal degree, we define the "neighborhood" degree, equal to the sum of the nodal degrees of the nearest topological neighbors (i.e., the adjacent nodes). This definition of "neighborhood" degree is consistent with the fact that the degree of a single node is not significant for analysis of WDSs.

  4. Artificial-neural-network-based classification of mammographic microcalcifications using image structure features

    Science.gov (United States)

    Dhawan, Atam P.; Chitre, Yateen S.; Moskowitz, Myron

    1993-07-01

    Mammography associated with clinical breast examination and self-breast examination is the only effective and viable method for mass breast screening. It is however, difficult to distinguish between benign and malignant microcalcifications associated with breast cancer. Most of the techniques used in the computerized analysis of mammographic microcalcifications segment the digitized gray-level image into regions representing microcalcifications. We present a second-order gray-level histogram based feature extraction approach to extract microcalcification features. These features, called image structure features, are computed from the second-order gray-level histogram statistics, and do not require segmentation of the original image into binary regions. Several image structure features were computed for 100 cases of `difficult to diagnose' microcalcification cases with known biopsy results. These features were analyzed in a correlation study which provided a set of five best image structure features. A feedforward backpropagation neural network was used to classify mammographic microcalcifications using the image structure features. The network was trained on 10 cases of mammographic microcalcifications and tested on additional 85 `difficult-to-diagnose' microcalcifications cases using the selected image structure features. The trained network yielded good results for classification of `difficult-to- diagnose' microcalcifications into benign and malignant categories.

  5. Special Feature: Liquids and Structural Glasses Special Feature: An active biopolymer network controlled by molecular motors

    Science.gov (United States)

    Koenderink, Gijsje H.; Dogic, Zvonimir; Nakamura, Fumihiko; Bendix, Poul M.; MacKintosh, Frederick C.; Hartwig, John H.; Stossel, Thomas P.; Weitz, David A.

    2009-09-01

    We describe an active polymer network in which processive molecular motors control network elasticity. This system consists of actin filaments cross-linked by filamin A (FLNa) and contracted by bipolar filaments of muscle myosin II. The myosin motors stiffen the network by more than two orders of magnitude by pulling on actin filaments anchored in the network by FLNa cross-links, thereby generating internal stress. The stiffening response closely mimics the effects of external stress applied by mechanical shear. Both internal and external stresses can drive the network into a highly nonlinear, stiffened regime. The active stress reaches values that are equivalent to an external stress of 14 Pa, consistent with a 1-pN force per myosin head. This active network mimics many mechanical properties of cells and suggests that adherent cells exert mechanical control by operating in a nonlinear regime where cell stiffness is sensitive to changes in motor activity. This design principle may be applicable to engineering novel biologically inspired, active materials that adjust their own stiffness by internal catalytic control.

  6. Feature Selection Combined with Neural Network Structure Optimization for HIV-1 Protease Cleavage Site Prediction

    Directory of Open Access Journals (Sweden)

    Hui Liu

    2015-01-01

    Full Text Available It is crucial to understand the specificity of HIV-1 protease for designing HIV-1 protease inhibitors. In this paper, a new feature selection method combined with neural network structure optimization is proposed to analyze the specificity of HIV-1 protease and find the important positions in an octapeptide that determined its cleavability. Two kinds of newly proposed features based on Amino Acid Index database plus traditional orthogonal encoding features are used in this paper, taking both physiochemical and sequence information into consideration. Results of feature selection prove that p2, p1, p1′, and p2′ are the most important positions. Two feature fusion methods are used in this paper: combination fusion and decision fusion aiming to get comprehensive feature representation and improve prediction performance. Decision fusion of subsets that getting after feature selection obtains excellent prediction performance, which proves feature selection combined with decision fusion is an effective and useful method for the task of HIV-1 protease cleavage site prediction. The results and analysis in this paper can provide useful instruction and help designing HIV-1 protease inhibitor in the future.

  7. Feature Selection Combined with Neural Network Structure Optimization for HIV-1 Protease Cleavage Site Prediction.

    Science.gov (United States)

    Liu, Hui; Shi, Xiaomiao; Guo, Dongmei; Zhao, Zuowei; Yimin

    2015-01-01

    It is crucial to understand the specificity of HIV-1 protease for designing HIV-1 protease inhibitors. In this paper, a new feature selection method combined with neural network structure optimization is proposed to analyze the specificity of HIV-1 protease and find the important positions in an octapeptide that determined its cleavability. Two kinds of newly proposed features based on Amino Acid Index database plus traditional orthogonal encoding features are used in this paper, taking both physiochemical and sequence information into consideration. Results of feature selection prove that p2, p1, p1', and p2' are the most important positions. Two feature fusion methods are used in this paper: combination fusion and decision fusion aiming to get comprehensive feature representation and improve prediction performance. Decision fusion of subsets that getting after feature selection obtains excellent prediction performance, which proves feature selection combined with decision fusion is an effective and useful method for the task of HIV-1 protease cleavage site prediction. The results and analysis in this paper can provide useful instruction and help designing HIV-1 protease inhibitor in the future.

  8. Structure Crack Identification Based on Surface-mounted Active Sensor Network with Time-Domain Feature Extraction and Neural Network

    Directory of Open Access Journals (Sweden)

    Chunling DU

    2012-03-01

    Full Text Available In this work the condition of metallic structures are classified based on the acquired sensor data from a surface-mounted piezoelectric sensor/actuator network. The structures are aluminum plates with riveted holes and possible crack damage at these holes. A 400 kHz sine wave burst is used as diagnostic signals. The combination of time-domain S0 waves from received sensor signals is directly used as features and preprocessing is not needed for the dam age detection. Since the time sequence of the extracted S0 has a high dimension, principal component estimation is applied to reduce its dimension before entering NN (neural network training for classification. An LVQ (learning vector quantization NN is used to classify the conditions as healthy or damaged. A number of FEM (finite element modeling results are taken as inputs to the NN for training, since the simulated S0 waves agree well with the experimental results on real plates. The performance of the classification is then validated by using these testing results.

  9. Sequence and structural features of carbohydrate binding in proteins and assessment of predictability using a neural network

    Directory of Open Access Journals (Sweden)

    Ahmad Shandar

    2007-01-01

    Full Text Available Abstract Background Protein-Carbohydrate interactions are crucial in many biological processes with implications to drug targeting and gene expression. Nature of protein-carbohydrate interactions may be studied at individual residue level by analyzing local sequence and structure environments in binding regions in comparison to non-binding regions, which provide an inherent control for such analyses. With an ultimate aim of predicting binding sites from sequence and structure, overall statistics of binding regions needs to be compiled. Sequence-based predictions of binding sites have been successfully applied to DNA-binding proteins in our earlier works. We aim to apply similar analysis to carbohydrate binding proteins. However, due to a relatively much smaller region of proteins taking part in such interactions, the methodology and results are significantly different. A comparison of protein-carbohydrate complexes has also been made with other protein-ligand complexes. Results We have compiled statistics of amino acid compositions in binding versus non-binding regions- general as well as in each different secondary structure conformation. Binding propensities of each of the 20 residue types and their structure features such as solvent accessibility, packing density and secondary structure have been calculated to assess their predisposition to carbohydrate interactions. Finally, evolutionary profiles of amino acid sequences have been used to predict binding sites using a neural network. Another set of neural networks was trained using information from single sequences and the prediction performance from the evolutionary profiles and single sequences were compared. Best of the neural network based prediction could achieve an 87% sensitivity of prediction at 23% specificity for all carbohydrate-binding sites, using evolutionary information. Single sequences gave 68% sensitivity and 55% specificity for the same data set. Sensitivity and specificity

  10. Systematic analysis of non-structural protein features for the prediction of PTM function potential by artificial neural networks.

    Science.gov (United States)

    Dewhurst, Henry M; Torres, Matthew P

    2017-01-01

    Post-translational modifications (PTMs) provide an extensible framework for regulation of protein behavior beyond the diversity represented within the genome alone. While the rate of identification of PTMs has rapidly increased in recent years, our knowledge of PTM functionality encompasses less than 5% of this data. We previously developed SAPH-ire (Structural Analysis of PTM Hotspots) for the prioritization of eukaryotic PTMs based on function potential of discrete modified alignment positions (MAPs) in a set of 8 protein families. A proteome-wide expansion of the dataset to all families of PTM-bearing, eukaryotic proteins with a representational crystal structure and the application of artificial neural network (ANN) models demonstrated the broader applicability of this approach. Although structural features of proteins have been repeatedly demonstrated to be predictive of PTM functionality, the availability of adequately resolved 3D structures in the Protein Data Bank (PDB) limits the scope of these methods. In order to bridge this gap and capture the larger set of PTM-bearing proteins without an available, homologous structure, we explored all available MAP features as ANN inputs to identify predictive models that do not rely on 3D protein structural data. This systematic, algorithmic approach explores 8 available input features in exhaustive combinations (247 models; size 2-8). To control for potential bias in random sampling for holdback in training sets, we iterated each model across 100 randomized, sample training and testing sets-yielding 24,700 individual ANNs. The size of the analyzed dataset and iterative generation of ANNs represents the largest and most thorough investigation of predictive models for PTM functionality to date. Comparison of input layer combinations allows us to quantify ANN performance with a high degree of confidence and subsequently select a top-ranked, robust fit model which highlights 3,687 MAPs, including 10,933 PTMs with a high

  11. Prediction of protein structural features by use of artificial neural networks

    DEFF Research Database (Denmark)

    Petersen, Bent

    . There is a huge over-representation of DNA sequences when comparing the amount of experimentally verified proteins with the amount of DNA sequences. The academic and industrial research community therefore has to rely on structure predictions instead of waiting for the time consuming experimentally determined...

  12. Features and Recursive Structure

    Directory of Open Access Journals (Sweden)

    Kuniya Nasukawa

    2015-01-01

    Full Text Available Based on the cross-linguistic tendency that weak vowels are realized with a central quality such as ə, ɨ, or ɯ, this paper attempts to account for this choice by proposing that the nucleus itself is one of the three monovalent vowel elements |A|, |I| and |U| which function as the building blocks of melodic structure. I claim that individual languages make a parametric choice to determine which of the three elements functions as the head of a nuclear expression. In addition, I show that elements can be freely concatenated to create melodic compounds. The resulting phonetic value of an element compound is determined by the specific elements it contains and by the head-dependency relations between those elements. This concatenation-based recursive mechanism of melodic structure can also be extended to levels above the segment, thus ultimately eliminating the need for syllabic constituents. This approach reinterprets the notion of minimalism in phonology by opposing the string-based flat structure.

  13. Geophysical Features - STRUCTURAL_FEATURES_IN: Structural Features of Indiana (Indiana Geological Survey, Line Shapefile)

    Data.gov (United States)

    NSGIC State | GIS Inventory — STRUCTURAL_FEATURES_IN is a line shapefile that shows the location of known structural features in Indiana; source data scales range from 1:12,000 to 1:500,000 (only...

  14. Core-Periphery Structure in Networks

    OpenAIRE

    Rombach, M. Puck; Porter, Mason A.; Fowler, James H.; Mucha, Peter J

    2012-01-01

    Intermediate-scale (or `meso-scale') structures in networks have received considerable attention, as the algorithmic detection of such structures makes it possible to discover network features that are not apparent either at the local scale of nodes and edges or at the global scale of summary statistics. Numerous types of meso-scale structures can occur in networks, but investigations of such features have focused predominantly on the identification and study of community structure. In this p...

  15. Schizophrenia classification using functional network features

    Science.gov (United States)

    Rish, Irina; Cecchi, Guillermo A.; Heuton, Kyle

    2012-03-01

    This paper focuses on discovering statistical biomarkers (features) that are predictive of schizophrenia, with a particular focus on topological properties of fMRI functional networks. We consider several network properties, such as node (voxel) strength, clustering coefficients, local efficiency, as well as just a subset of pairwise correlations. While all types of features demonstrate highly significant statistical differences in several brain areas, and close to 80% classification accuracy, the most remarkable results of 93% accuracy are achieved by using a small subset of only a dozen of most-informative (lowest p-value) correlation features. Our results suggest that voxel-level correlations and functional network features derived from them are highly informative about schizophrenia and can be used as statistical biomarkers for the disease.

  16. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule.

    Science.gov (United States)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

    2015-11-01

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

  17. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin, E-mail: xmli@cqu.edu.cn [Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044 (China); College of Automation, Chongqing University, Chongqing 400044 (China)

    2015-11-15

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

  18. Network structure of production

    Science.gov (United States)

    Atalay, Enghin; Hortaçsu, Ali; Roberts, James; Syverson, Chad

    2011-01-01

    Complex social networks have received increasing attention from researchers. Recent work has focused on mechanisms that produce scale-free networks. We theoretically and empirically characterize the buyer–supplier network of the US economy and find that purely scale-free models have trouble matching key attributes of the network. We construct an alternative model that incorporates realistic features of firms’ buyer–supplier relationships and estimate the model’s parameters using microdata on firms’ self-reported customers. This alternative framework is better able to match the attributes of the actual economic network and aids in further understanding several important economic phenomena. PMID:21402924

  19. Learning discriminative functional network features of schizophrenia

    Science.gov (United States)

    Gheiratmand, Mina; Rish, Irina; Cecchi, Guillermo; Brown, Matthew; Greiner, Russell; Bashivan, Pouya; Polosecki, Pablo; Dursun, Serdar

    2017-03-01

    Associating schizophrenia with disrupted functional connectivity is a central idea in schizophrenia research. However, identifying neuroimaging-based features that can serve as reliable "statistical biomarkers" of the disease remains a challenging open problem. We argue that generalization accuracy and stability of candidate features ("biomarkers") must be used as additional criteria on top of standard significance tests in order to discover more robust biomarkers. Generalization accuracy refers to the utility of biomarkers for making predictions about individuals, for example discriminating between patients and controls, in novel datasets. Feature stability refers to the reproducibility of the candidate features across different datasets. Here, we extracted functional connectivity network features from fMRI data at both high-resolution (voxel-level) and a spatially down-sampled lower-resolution ("supervoxel" level). At the supervoxel level, we used whole-brain network links, while at the voxel level, due to the intractably large number of features, we sampled a subset of them. We compared statistical significance, stability and discriminative utility of both feature types in a multi-site fMRI dataset, composed of schizophrenia patients and healthy controls. For both feature types, a considerable fraction of features showed significant differences between the two groups. Also, both feature types were similarly stable across multiple data subsets. However, the whole-brain supervoxel functional connectivity features showed a higher cross-validation classification accuracy of 78.7% vs. 72.4% for the voxel-level features. Cross-site variability and heterogeneity in the patient samples in the multi-site FBIRN dataset made the task more challenging compared to single-site studies. The use of the above methodology in combination with the fully data-driven approach using the whole brain information have the potential to shed light on "biomarker discovery" in schizophrenia.

  20. Patch layout generation by detecting feature networks

    KAUST Repository

    Cao, Yuanhao

    2015-02-01

    The patch layout of 3D surfaces reveals the high-level geometric and topological structures. In this paper, we study the patch layout computation by detecting and enclosing feature loops on surfaces. We present a hybrid framework which combines several key ingredients, including feature detection, feature filtering, feature curve extension, patch subdivision and boundary smoothing. Our framework is able to compute patch layouts through concave features as previous approaches, but also able to generate nice layouts through smoothing regions. We demonstrate the effectiveness of our framework by comparing with the state-of-the-art methods.

  1. On the topological structure of multinationals network

    Science.gov (United States)

    Joyez, Charlie

    2017-05-01

    This paper uses a weighted network analysis to examine the structure of multinationals' implantation countries network. Based on French firm-level dataset of multinational enterprises (MNEs) the network analysis provides information on each country position in the network and in internationalization strategies of French MNEs through connectivity preferences among the nodes. The paper also details network-wide features and their recent evolution toward a more decentralized structure. While much has been said on international trade network, this paper shows that multinational firms' studies would also benefit from network analysis, notably by investigating the sensitivity of the network construction to firm heterogeneity.

  2. Network features of the mammalian circadian clock.

    Directory of Open Access Journals (Sweden)

    Julie E Baggs

    2009-03-01

    Full Text Available The mammalian circadian clock is a cell-autonomous system that drives oscillations in behavior and physiology in anticipation of daily environmental change. To assess the robustness of a human molecular clock, we systematically depleted known clock components and observed that circadian oscillations are maintained over a wide range of disruptions. We developed a novel strategy termed Gene Dosage Network Analysis (GDNA in which small interfering RNA (siRNA-induced dose-dependent changes in gene expression were used to build gene association networks consistent with known biochemical constraints. The use of multiple doses powered the analysis to uncover several novel network features of the circadian clock, including proportional responses and signal propagation through interacting genetic modules. We also observed several examples where a gene is up-regulated following knockdown of its paralog, suggesting the clock network utilizes active compensatory mechanisms rather than simple redundancy to confer robustness and maintain function. We propose that these network features act in concert as a genetic buffering system to maintain clock function in the face of genetic and environmental perturbation.

  3. Feature network models for proximity data : statistical inference, model selection, network representations and links with related models

    NARCIS (Netherlands)

    Frank, Laurence Emmanuelle

    2006-01-01

    Feature Network Models (FNM) are graphical structures that represent proximity data in a discrete space with the use of features. A statistical inference theory is introduced, based on the additivity properties of networks and the linear regression framework. Considering features as predictor

  4. Propagation dynamics on networks featuring complex topologies

    Science.gov (United States)

    Hébert-Dufresne, Laurent; Noël, Pierre-André; Marceau, Vincent; Allard, Antoine; Dubé, Louis J.

    2010-09-01

    Analytical description of propagation phenomena on random networks has flourished in recent years, yet more complex systems have mainly been studied through numerical means. In this paper, a mean-field description is used to coherently couple the dynamics of the network elements (such as nodes, vertices, individuals, etc.) on the one hand and their recurrent topological patterns (such as subgraphs, groups, etc.) on the other hand. In a susceptible-infectious-susceptible (SIS) model of epidemic spread on social networks with community structure, this approach yields a set of ordinary differential equations for the time evolution of the system, as well as analytical solutions for the epidemic threshold and equilibria. The results obtained are in good agreement with numerical simulations and reproduce the behavior of random networks in the appropriate limits which highlights the influence of topology on the processes. Finally, it is demonstrated that our model predicts higher epidemic thresholds for clustered structures than for equivalent random topologies in the case of networks with zero degree correlation.

  5. Feature to prototype transition in neural networks

    Science.gov (United States)

    Krotov, Dmitry; Hopfield, John

    Models of associative memory with higher order (higher than quadratic) interactions, and their relationship to neural networks used in deep learning are discussed. Associative memory is conventionally described by recurrent neural networks with dynamical convergence to stable points. Deep learning typically uses feedforward neural nets without dynamics. However, a simple duality relates these two different views when applied to problems of pattern classification. From the perspective of associative memory such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. In the dual description, these models correspond to feedforward neural networks with one hidden layer and unusual activation functions transmitting the activities of the visible neurons to the hidden layer. These activation functions are rectified polynomials of a higher degree rather than the rectified linear functions used in deep learning. The network learns representations of the data in terms of features for rectified linear functions, but as the power in the activation function is increased there is a gradual shift to a prototype-based representation, the two extreme regimes of pattern recognition known in cognitive psychology. Simons Center for Systems Biology.

  6. Construction of Individual Morphological Brain Networks with Multiple Morphometric Features

    Directory of Open Access Journals (Sweden)

    Chunlan Yang

    2017-04-01

    Full Text Available In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28 each scanned twice, and reproducibility was evaluated through test–retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20–40%, and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78 indicates

  7. Evolving production network structures

    DEFF Research Database (Denmark)

    Grunow, Martin; Gunther, H.O.; Burdenik, H.

    2007-01-01

    When deciding about future production network configurations, the current structures have to be taken into account. Further, core issues such as the maturity of the products and the capacity requirements for test runs and ramp-ups must be incorporated. Our approach is based on optimization...... modelling and assigns products and capacity expansions to production sites under the above constraints. It also considers the production complexity at the individual sites and the flexibility of the network. Our implementation results for a large manufacturing network reveal substantial possible cost...... reductions compared to the traditional manual planning results of our industrial partner....

  8. Linking network topology to function. Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O.C. Martin, A. Krzywicki and M. Zagorski

    Science.gov (United States)

    di Bernardo, Diego

    2016-07-01

    The review by Martin et al. deals with a long standing problem at the interface of complex systems and molecular biology, that is the relationship between the topology of a complex network and its function. In biological terms the problem translates to relating the topology of gene regulatory networks (GRNs) to specific cellular functions. GRNs control the spatial and temporal activity of the genes encoded in the cell's genome by means of specialised proteins called Transcription Factors (TFs). A TF is able to recognise and bind specifically to a sequence (TF biding site) of variable length (order of magnitude of 10) found upstream of the sequence encoding one or more genes (at least in prokaryotes) and thus activating or repressing their transcription. TFs can thus be distinguished in activator and repressor. The picture can become more complex since some classes of TFs can form hetero-dimers consisting of a protein complex whose subunits are the individual TFs. Heterodimers can have completely different binding sites and activity compared to their individual parts. In this review the authors limit their attention to prokaryotes where the complexity of GRNs is somewhat reduced. Moreover they exploit a unique feature of living systems, i.e. evolution, to understand whether function can shape network topology. Indeed, prokaryotes such as bacteria are among the oldest living systems that have become perfectly adapted to their environment over geological scales and thus have reached an evolutionary steady-state where the fitness of the population has reached a plateau. By integrating in silico analysis and comparative evolution, the authors show that indeed function does tend to shape the structure of a GRN, however this trend is not always present and depends on the properties of the network being examined. Interestingly, the trend is more apparent for sparse networks, i.e. where the density of edges is very low. Sparsity is indeed one of the most prominent features

  9. Feature detection in satellite images using neural network technology

    Science.gov (United States)

    Augusteijn, Marijke F.; Dimalanta, Arturo S.

    1992-01-01

    A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.

  10. Feature Fusion Based on Convolutional Neural Network for SAR ATR

    Directory of Open Access Journals (Sweden)

    Chen Shi-Qi

    2017-01-01

    Full Text Available Recent breakthroughs in algorithms related to deep convolutional neural networks (DCNN have stimulated the development of various of signal processing approaches, where the specific application of Automatic Target Recognition (ATR using Synthetic Aperture Radar (SAR data has spurred widely attention. Inspired by the more efficient distributed training such as inception architecture and residual network, a new feature fusion structure which jointly exploits all the merits of each version is proposed to reduce the data dimensions and the complexity of computation. The detailed procedure presented in this paper consists of the fused features, which make the representation of SAR images more distinguishable after the extraction of a set of features from DCNN, followed by a trainable classifier. In particular, the obtained results on the 10-class benchmark data set demonstrate that the presented architecture can achieve remarkable classification performance to the current state-of-the-art methods.

  11. Bayesian network feature finder (BANFF): an R package for gene network feature selection.

    Science.gov (United States)

    Lan, Zhou; Zhao, Yize; Kang, Jian; Yu, Tianwei

    2016-12-01

    Network marker selection on genome-scale networks plays an important role in the understanding of biological mechanisms and disease pathologies. Recently, a Bayesian nonparametric mixture model has been developed and successfully applied for selecting genes and gene sub-networks. Hence, extending this method to a unified approach for network-based feature selection on general large-scale networks and creating an easy-to-use software package is on demand. We extended the method and developed an R package, the Bayesian network feature finder (BANFF), providing a package of posterior inference, model comparison and graphical illustration of model fitting. The model was extended to a more general form, and a parallel computing algorithm for the Markov chain Monte Carlo -based posterior inference and an expectation maximization-based algorithm for posterior approximation were added. Based on simulation studies, we demonstrate the use of BANFF on analyzing gene expression on a protein-protein interaction network. https://cran.r-project.org/web/packages/BANFF/index.html CONTACT: jiankang@umich.edu, tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  12. Network topology and correlation features affiliated with European airline companies

    Science.gov (United States)

    Han, Ding-Ding; Qian, Jiang-Hai; Liu, Jin-Gao

    2009-01-01

    The physics information of four specific airline flight networks in European Continent, namely the Austrian airline, the British airline, the France-Holland airline and the Lufthhansa airline, was quantitatively analyzed by the concepts of a complex network. It displays some features of small-world networks, namely a large clustering coefficient and small average shortest-path length for these specific airline networks. The degree distributions for the small degree branch reveal power law behavior with an exponent value of 2-3 for the Austrian and the British flight networks, and that of 1-2 for the France-Holland and the Lufthhansa airline flight networks. So the studied four airlines are sorted into two classes according to the topology structure. Similarly, the flight weight distributions show two kinds of different decay behavior with the flight weight: one for the Austrian and the British airlines and another for the France-Holland airline and the Lufthhansa airlines. In addition, the degree-degree correlation analysis shows that the network has disassortative behavior for all the value of degree k, and this phenomenon is different from the international airline network and US airline network. Analysis of the clustering coefficient ( C(k)) versus k, indicates that the flight networks of the Austrian Airline and the British Airline reveal a hierarchical organization for all airports, however, the France-Holland Airline and the Lufthhansa Airline show a hierarchical organization mostly for larger airports. The correlation of node strength ( S(k)) and degree is also analyzed, and a power-law fit S(k)∼k1.1 can roughly fit all data of these four airline companies. Furthermore, we mention seasonal changes and holidays may cause the flight network to form a different topology. An example of the Austrian Airline during Christmas was studied and analyzed.

  13. Extraction of texture features with a multiresolution neural network

    Science.gov (United States)

    Lepage, Richard; Laurendeau, Denis; Gagnon, Roger A.

    1992-09-01

    Texture is an important surface characteristic. Many industrial materials such as wood, textile, or paper are best characterized by their texture. Detection of defaults occurring on such materials or classification for quality control anD matching can be carried out through careful texture analysis. A system for the classification of pieces of wood used in the furniture industry is proposed. This paper is concerned with a neural network implementation of the features extraction and classification components of the proposed system. Texture appears differently depending at which spatial scale it is observed. A complete description of a texture thus implies an analysis at several spatial scales. We propose a compact pyramidal representation of the input image for multiresolution analysis. The feature extraction system is implemented on a multilayer artificial neural network. Each level of the pyramid, which is a representation of the input image at a given spatial resolution scale, is mapped into a layer of the neural network. A full resolution texture image is input at the base of the pyramid and a representation of the texture image at multiple resolutions is generated by the feedforward pyramid structure of the neural network. The receptive field of each neuron at a given pyramid level is preprogrammed as a discrete Gaussian low-pass filter. Meaningful characteristics of the textured image must be extracted if a good resolving power of the classifier must be achieved. Local dominant orientation is the principal feature which is extracted from the textured image. Local edge orientation is computed with a Sobel mask at four orientation angles (multiple of (pi) /4). The resulting intrinsic image, that is, the local dominant orientation image, is fed to the texture classification neural network. The classification network is a three-layer feedforward back-propagation neural network.

  14. Processing of Feature Selectivity in Cortical Networks with Specific Connectivity.

    Directory of Open Access Journals (Sweden)

    Sadra Sadeh

    Full Text Available Although non-specific at the onset of eye opening, networks in rodent visual cortex attain a non-random structure after eye opening, with a specific bias for connections between neurons of similar preferred orientations. As orientation selectivity is already present at eye opening, it remains unclear how this specificity in network wiring contributes to feature selectivity. Using large-scale inhibition-dominated spiking networks as a model, we show that feature-specific connectivity leads to a linear amplification of feedforward tuning, consistent with recent electrophysiological single-neuron recordings in rodent neocortex. Our results show that optimal amplification is achieved at an intermediate regime of specific connectivity. In this configuration a moderate increase of pairwise correlations is observed, consistent with recent experimental findings. Furthermore, we observed that feature-specific connectivity leads to the emergence of orientation-selective reverberating activity, and entails pattern completion in network responses. Our theoretical analysis provides a mechanistic understanding of subnetworks' responses to visual stimuli, and casts light on the regime of operation of sensory cortices in the presence of specific connectivity.

  15. Social Networks and Network Structures

    Science.gov (United States)

    2006-11-01

    Research in Command & Control • Latent Semantic Analysis – Team communication – Emergent team dynamics – Shared situation awareness • Dynamic Network...requirements – Information technology requirements 28 LSA Essentials of Latent Semantic Analysis 29 Communication Analysis • Goal: Automatically monitor and

  16. Structural features of lignohumic acids

    Czech Academy of Sciences Publication Activity Database

    Novák, František; Šestauberová, Martina; Hrabal, R.

    2015-01-01

    Roč. 1093, August (2015), s. 179-185 ISSN 0022-2860 Institutional support: RVO:60077344 Keywords : C-13 NMR * FTIR * humic acids * lignohumate * lignosulfonate * structure Subject RIV: DF - Soil Science Impact factor: 1.780, year: 2015

  17. Deterministic bound for avionics switched networks according to networking features using network calculus

    Directory of Open Access Journals (Sweden)

    Feng HE

    2017-12-01

    Full Text Available The state of the art avionics system adopts switched networks for airborne communications. A major concern in the design of the networks is the end-to-end guarantee ability. Analytic methods have been developed to compute the worst-case delays according to the detailed configurations of flows and networks within avionics context, such as network calculus and trajectory approach. It still lacks a relevant method to make a rapid performance estimation according to some typically switched networking features, such as networking scale, bandwidth utilization and average flow rate. The goal of this paper is to establish a deterministic upper bound analysis method by using these networking features instead of the complete network configurations. Two deterministic upper bounds are proposed from network calculus perspective: one is for a basic estimation, and another just shows the benefits from grouping strategy. Besides, a mathematic expression for grouping ability is established based on the concept of network connecting degree, which illustrates the possibly minimal grouping benefit. For a fully connected network with 4 switches and 12 end systems, the grouping ability coming from grouping strategy is 15–20%, which just coincides with the statistical data (18–22% from the actual grouping advantage. Compared with the complete network calculus analysis method for individual flows, the effectiveness of the two deterministic upper bounds is no less than 38% even with remarkably varied packet lengths. Finally, the paper illustrates the design process for an industrial Avionics Full DupleX switched Ethernet (AFDX networking case according to the two deterministic upper bounds and shows that a better control for network connecting, when designing a switched network, can improve the worst-case delays dramatically. Keywords: Deterministic bound, Grouping ability, Network calculus, Networking features, Switched networks

  18. [Network structures in biological systems].

    Science.gov (United States)

    Oleskin, A V

    2013-01-01

    Network structures (networks) that have been extensively studied in the humanities are characterized by cohesion, a lack of a central control unit, and predominantly fractal properties. They are contrasted with structures that contain a single centre (hierarchies) as well as with those whose elements predominantly compete with one another (market-type structures). As far as biological systems are concerned, their network structures can be subdivided into a number of types involving different organizational mechanisms. Network organization is characteristic of various structural levels of biological systems ranging from single cells to integrated societies. These networks can be classified into two main subgroups: (i) flat (leaderless) network structures typical of systems that are composed of uniform elements and represent modular organisms or at least possess manifest integral properties and (ii) three-dimensional, partly hierarchical structures characterized by significant individual and/or intergroup (intercaste) differences between their elements. All network structures include an element that performs structural, protective, and communication-promoting functions. By analogy to cell structures, this element is denoted as the matrix of a network structure. The matrix includes a material and an immaterial component. The material component comprises various structures that belong to the whole structure and not to any of its elements per se. The immaterial (ideal) component of the matrix includes social norms and rules regulating network elements' behavior. These behavioral rules can be described in terms of algorithms. Algorithmization enables modeling the behavior of various network structures, particularly of neuron networks and their artificial analogs.

  19. Optimized null model for protein structure networks.

    Science.gov (United States)

    Milenković, Tijana; Filippis, Ioannis; Lappe, Michael; Przulj, Natasa

    2009-06-26

    Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by

  20. Optimized null model for protein structure networks.

    Directory of Open Access Journals (Sweden)

    Tijana Milenković

    Full Text Available Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model

  1. Network quotients: structural skeletons of complex systems.

    Science.gov (United States)

    Xiao, Yanghua; MacArthur, Ben D; Wang, Hui; Xiong, Momiao; Wang, Wei

    2008-10-01

    A defining feature of many large empirical networks is their intrinsic complexity. However, many networks also contain a large degree of structural repetition. An immediate question then arises: can we characterize essential network complexity while excluding structural redundancy? In this article we utilize inherent network symmetry to collapse all redundant information from a network, resulting in a coarse graining which we show to carry the essential structural information of the "parent" network. In the context of algebraic combinatorics, this coarse-graining is known as the "quotient." We systematically explore the theoretical properties of network quotients and summarize key statistics of a variety of "real-world" quotients with respect to those of their parent networks. In particular, we find that quotients can be substantially smaller than their parent networks yet typically preserve various key functional properties such as complexity (heterogeneity and hub vertices) and communication (diameter and mean geodesic distance), suggesting that quotients constitute the essential structural skeletons of their parent networks. We summarize with a discussion of potential uses of quotients in analysis of biological regulatory networks and ways in which using quotients can reduce the computational complexity of network algorithms.

  2. Determining Vision Graphs for Distributed Camera Networks Using Feature Digests

    Directory of Open Access Journals (Sweden)

    Richard J. Radke

    2007-01-01

    Full Text Available We propose a decentralized method for obtaining the vision graph for a distributed, ad-hoc camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. Each camera encodes a spatially well-distributed set of distinctive, approximately viewpoint-invariant feature points into a fixed-length “feature digest” that is broadcast throughout the network. Each receiver camera robustly matches its own features with the decompressed digest and decides whether sufficient evidence exists to form a vision graph edge. We also show how a camera calibration algorithm that passes messages only along vision graph edges can recover accurate 3D structure and camera positions in a distributed manner. We analyze the performance of different message formation schemes, and show that high detection rates (>0.8 can be achieved while maintaining low false alarm rates (<0.05 using a simulated 60-node outdoor camera network.

  3. Metastable Features of Economic Networks and Responses to Exogenous Shocks.

    Directory of Open Access Journals (Sweden)

    Ali Hosseiny

    Full Text Available It is well known that a network structure plays an important role in addressing a collective behavior. In this paper we study a network of firms and corporations for addressing metastable features in an Ising based model. In our model we observe that if in a recession the government imposes a demand shock to stimulate the network, metastable features shape its response. Actually we find that there exists a minimum bound where any demand shock with a size below it is unable to trigger the market out of recession. We then investigate the impact of network characteristics on this minimum bound. We surprisingly observe that in a Watts-Strogatz network, although the minimum bound depends on the average of the degrees, when translated into the language of economics, such a bound is independent of the average degrees. This bound is about 0.44ΔGDP, where ΔGDP is the gap of GDP between recession and expansion. We examine our suggestions for the cases of the United States and the European Union in the recent recession, and compare them with the imposed stimulations. While the stimulation in the US has been above our threshold, in the EU it has been far below our threshold. Beside providing a minimum bound for a successful stimulation, our study on the metastable features suggests that in the time of crisis there is a "golden time passage" in which the minimum bound for successful stimulation can be much lower. Hence, our study strongly suggests stimulations to arise within this time passage.

  4. Disease gene prioritization using network and feature.

    Science.gov (United States)

    Xie, Bingqing; Agam, Gady; Balasubramanian, Sandhya; Xu, Jinbo; Gilliam, T Conrad; Maltsev, Natalia; Börnigen, Daniela

    2015-04-01

    Identifying high-confidence candidate genes that are causative for disease phenotypes, from the large lists of variations produced by high-throughput genomics, can be both time-consuming and costly. The development of novel computational approaches, utilizing existing biological knowledge for the prioritization of such candidate genes, can improve the efficiency and accuracy of the biomedical data analysis. It can also reduce the cost of such studies by avoiding experimental validations of irrelevant candidates. In this study, we address this challenge by proposing a novel gene prioritization approach that ranks promising candidate genes that are likely to be involved in a disease or phenotype under study. This algorithm is based on the modified conditional random field (CRF) model that simultaneously makes use of both gene annotations and gene interactions, while preserving their original representation. We validated our approach on two independent disease benchmark studies by ranking candidate genes using network and feature information. Our results showed both high area under the curve (AUC) value (0.86), and more importantly high partial AUC (pAUC) value (0.1296), and revealed higher accuracy and precision at the top predictions as compared with other well-performed gene prioritization tools, such as Endeavour (AUC-0.82, pAUC-0.083) and PINTA (AUC-0.76, pAUC-0.066). We were able to detect more target genes (9/18/19/27) on top positions (1/5/10/20) compared to Endeavour (3/11/14/23) and PINTA (6/10/13/18). To demonstrate its usability, we applied our method to a case study for the prediction of molecular mechanisms contributing to intellectual disability and autism. Our approach was able to correctly recover genes related to both disorders and provide suggestions for possible additional candidates based on their rankings and functional annotations.

  5. Investigation of efficient features for image recognition by neural networks.

    Science.gov (United States)

    Goltsev, Alexander; Gritsenko, Vladimir

    2012-04-01

    In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better. Copyright © 2011 Elsevier Ltd. All rights reserved.

  6. Exploring biological network structure with clustered random networks

    Directory of Open Access Journals (Sweden)

    Bansal Shweta

    2009-12-01

    Full Text Available Abstract Background Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions and the extent of clustering (the tendency for a set of three nodes to be interconnected are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Results Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. Conclusion ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in

  7. Exploring biological network structure with clustered random networks.

    Science.gov (United States)

    Bansal, Shweta; Khandelwal, Shashank; Meyers, Lauren Ancel

    2009-12-09

    Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks) provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics.Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in unraveling the functional consequences of the structural

  8. Neural Network-Based Segmentation of Textures Using Gabor Features

    OpenAIRE

    Ramakrishnan, AG; Raja, Kumar S; Ram, Ragu HV

    2002-01-01

    The effectiveness of Gabor filters for texture segmentation is well known. In this paper, we propose a texture identification scheme, based on a neural network (NN) using Gabor features. The features are derived from both the Gabor cosine and sine filters. Through experiments, we demonstrate the effectiveness of a NN based classifier using Gabor features for identifying textures in a controlled environment. The neural network used for texture identification is based on the multilayer perceptr...

  9. Breast image feature learning with adaptive deconvolutional networks

    Science.gov (United States)

    Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.

    2012-03-01

    Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

  10. Regular Network Class Features Enhancement Using an Evolutionary Synthesis Algorithm

    Directory of Open Access Journals (Sweden)

    O. G. Monahov

    2014-01-01

    Full Text Available This paper investigates a solution of the optimization problem concerning the construction of diameter-optimal regular networks (graphs. Regular networks are of practical interest as the graph-theoretical models of reliable communication networks of parallel supercomputer systems, as a basis of the structure in a model of small world in optical and neural networks. It presents a new class of parametrically described regular networks - hypercirculant networks (graphs. An approach that uses evolutionary algorithms for the automatic generation of parametric descriptions of optimal hypercirculant networks is developed. Synthesis of optimal hypercirculant networks is based on the optimal circulant networks with smaller degree of nodes. To construct optimal hypercirculant networks is used a template of circulant network from the known optimal families of circulant networks with desired number of nodes and with smaller degree of nodes. Thus, a generating set of the circulant network is used as a generating subset of the hypercirculant network, and the missing generators are synthesized by means of the evolutionary algorithm, which is carrying out minimization of diameter (average diameter of networks. A comparative analysis of the structural characteristics of hypercirculant, toroidal, and circulant networks is conducted. The advantage hypercirculant networks under such structural characteristics, as diameter, average diameter, and the width of bisection, with comparable costs of the number of nodes and the number of connections is demonstrated. It should be noted the advantage of hypercirculant networks of dimension three over four higher-dimensional tori. Thus, the optimization of hypercirculant networks of dimension three is more efficient than the introduction of an additional dimension for the corresponding toroidal structures. The paper also notes the best structural parameters of hypercirculant networks in comparison with iBT-networks previously

  11. A framework for online social networking features

    Directory of Open Access Journals (Sweden)

    Mohsen Shafiei Nikabadi

    2014-06-01

    Full Text Available Social networks form a basis for maintaining social contacts, finding users with common interests, creating local content and sharing information. Recently networks have created a fundamental framework for analyzing and modeling the complex systems. Users' behavior studies and evaluates the system performance and leads to better planning and implementation of advertising policies on the web sites. Therefore, this study offers a framework for online social networks' characteristics. In terms of objective, this survey is practical descriptive. Sampling has been done among 384 of graduate students who have good experiences of membership in online social network. Confirmatory factor analysis is used to evaluate the validity of variables in research model. Characteristics of online social networks are defined based on six components and framework's indexes are analyzed through factor analysis. The reliability is calculated separately for each dimension and since they are all above 0.7, the reliability of the study can be confirmed. According to our research results, in terms of size, the number of people who apply for membership in various online social networking is an important index. In terms of individual preference to connect with, people who are relative play essential role in social network development. In terms of homogeneity variable, the number of people who visit their friends’ pages is important for measuring frequency variable. In terms of frequency, the use of entertainment and recreation services is more important index. In terms of proximity, being in the same city is a more important index and index of creating a sense of belonging and confidence is more important for measuring reciprocity variable.

  12. Hadoop neural network for parallel and distributed feature selection.

    Science.gov (United States)

    Hodge, Victoria J; O'Keefe, Simon; Austin, Jim

    2016-06-01

    In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of five feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop YARN. Hadoop allows parallel and distributed processing. Each feature selector can be divided into subtasks and the subtasks can then be processed in parallel. Multiple feature selectors can also be processed simultaneously (in parallel) allowing multiple feature selectors to be compared. We identify commonalities among the five features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all five feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  13. Advanced Polymer Network Structures

    Science.gov (United States)

    2016-02-01

    characteristic time 02 /UmaLJ =τ . Topologically bound monomers interact through the sum of the purely repulsive LJ potential ( arc 6/12= ) or so-called Weeks...3 Content of the simulated polymer double network. Self- attraction coefficient between particles within a network (first or second) is fixed at 1...technique to the study the microscopic topology and dynamics of a wide variety of polymer networks and gels.5–8 The pair interaction between

  14. Features of Random Metal Nanowire Networks with

    KAUST Repository

    Maloth, Thirupathi

    2017-05-01

    Among the alternatives to conventional Indium Tin Oxide (ITO) used in making transparent conducting electrodes, the random metal nanowire (NW) networks are considered to be superior offering performance at par with ITO. The performance is measured in terms of sheet resistance and optical transmittance. However, as the electrical properties of such random networks are achieved thanks to a percolation network, a minimum size of the electrodes is needed so it actually exceeds the representative volume element (RVE) of the material and the macroscopic electrical properties are achieved. There is not much information about the compatibility of this minimum RVE size with the resolution actually needed in electronic devices. Furthermore, the efficiency of NWs in terms of electrical conduction is overlooked. In this work, we address the above industrially relevant questions - 1) The minimum size of electrodes that can be made based on the dimensions of NWs and the material coverage. For this, we propose a morphology based classification in defining the RVE size and we also compare the same with that is based on macroscopic electrical properties stabilization. 2) The amount of NWs that do not participate in electrical conduction, hence of no practical use. The results presented in this thesis are a design guide to experimentalists to design transparent electrodes with more optimal usage of the material.

  15. Some interesting structural features and metallated hypervalent ...

    Indian Academy of Sciences (India)

    Administrator

    The metallation of hypervalent species/electron deficient molecules 1 of main group elements with transition metals through nucleophilic substitution reactions has resulted in the isolation of some novel products 2. These synthetic species have some interesting structural features. An overview of our experience in this field is ...

  16. Structural features of bionanocomposite derived from novel ...

    Indian Academy of Sciences (India)

    Home; Journals; Bulletin of Materials Science; Volume 36; Issue 2. Structural features of bionanocomposite derived from novel designed poly(ester-imide) based on natural amino acids with hydroxyl segments tailored for better dispersion of TiO2 nanofiller. Shadpour Mallakpour Parvin Asadi. Volume 36 Issue 2 April 2013 ...

  17. A Topological Perspective of Neural Network Structure

    Science.gov (United States)

    Sizemore, Ann; Giusti, Chad; Cieslak, Matthew; Grafton, Scott; Bassett, Danielle

    The wiring patterns of white matter tracts between brain regions inform functional capabilities of the neural network. Indeed, densely connected and cyclically arranged cognitive systems may communicate and thus perform distinctly. However, previously employed graph theoretical statistics are local in nature and thus insensitive to such global structure. Here we present an investigation of the structural neural network in eight healthy individuals using persistent homology. An extension of homology to weighted networks, persistent homology records both circuits and cliques (all-to-all connected subgraphs) through a repetitive thresholding process, thus perceiving structural motifs. We report structural features found across patients and discuss brain regions responsible for these patterns, finally considering the implications of such motifs in relation to cognitive function.

  18. Internet of Things: Structure, Features and Management

    Directory of Open Access Journals (Sweden)

    Aleksandrovičs Vladislavs

    2016-12-01

    Full Text Available Internet of Things (IoT - a rapidly developing technology today and most likely everyday thing in the future. Numerous devices, computing machines and build-in sensors connected in a single dynamic network continuously receive and exchange information from the outer environment. Huge data clusters are collected and put to use in handmade applications that scrupulously manage and control given objectives. In this way, an interactive technical infrastructure is created, which can oversee and infiltrate any person’s vital processes. Though separately every device and technological solution in the IoT can be known for many years, each architecture is unique and provides new challenges for the network owner. This research aims to investigate IoT general structure and management aspects with the knowledge of which the authors will try to answer a trivial question whether it is possible to comprehensively control such a tremendous structure with the current level of technology.

  19. Detecting Hierarchical Structure in Networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard

    2012-01-01

    a generative Bayesian model that is able to infer whether hierarchies are present or not from a hypothesis space encompassing all types of hierarchical tree structures. For efficient inference we propose a collapsed Gibbs sampling procedure that jointly infers a partition and its hierarchical structure......Many real-world networks exhibit hierarchical organization. Previous models of hierarchies within relational data has focused on binary trees; however, for many networks it is unknown whether there is hierarchical structure, and if there is, a binary tree might not account well for it. We propose....... On synthetic and real data we demonstrate that our model can detect hierarchical structure leading to better link-prediction than competing models. Our model can be used to detect if a network exhibits hierarchical structure, thereby leading to a better comprehension and statistical account the network....

  20. Discovering network structure beyond communities.

    Science.gov (United States)

    Nishikawa, Takashi; Motter, Adilson E

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.

  1. Structural Features of Multiple Jets in Crossflow

    Science.gov (United States)

    Hale, C. A.; Roberts, D. R.; Plesniak, M. W.; Ramadhayani, S.

    1997-11-01

    (Supported by Allison Engine Company) Multiple jets in crossflow are commonly used in gas turbine film cooling applications. The various structural features such as the pair of counter-rotating ``kidney'' vortices influence the coolant jet trajectory and consequently its film cooling effectiveness. Although the canonical isolated round jet in crossflow has been studied for the past 60 years, the origin and dynamics of the complex flow structures are not yet completely understood. The problem is compounded when multiple jets interact. This study utilized a single row of five jets separated in the spanwise direction by three jet diameters, which were injected into a crossflow at 35 degree inclination. Among the parameters studied were velocity ratio (0.5 to 1.5), hole length-to-diameter ratio (1 to 3) and plenum flow characteristics. Flow visualization employing high-speed cinematography and surface streak topology were combined with hot-wire velocity measurements and computational results to investigate the various structural flow features. The flow physics of the structural features of the jet are explained and their implication for the technological problem of film cooling are discussed.

  2. Discovering Network Structure Beyond Communities

    OpenAIRE

    Nishikawa, Takashi; Motter, Adilson E.

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes chara...

  3. Feature extraction for deep neural networks based on decision boundaries

    Science.gov (United States)

    Woo, Seongyoun; Lee, Chulhee

    2017-05-01

    Feature extraction is a process used to reduce data dimensions using various transforms while preserving the discriminant characteristics of the original data. Feature extraction has been an important issue in pattern recognition since it can reduce the computational complexity and provide a simplified classifier. In particular, linear feature extraction has been widely used. This method applies a linear transform to the original data to reduce the data dimensions. The decision boundary feature extraction method (DBFE) retains only informative directions for discriminating among the classes. DBFE has been applied to various parametric and non-parametric classifiers, which include the Gaussian maximum likelihood classifier (GML), the k-nearest neighbor classifier, support vector machines (SVM) and neural networks. In this paper, we apply DBFE to deep neural networks. This algorithm is based on the nonparametric version of DBFE, which was developed for neural networks. Experimental results with the UCI database show improved classification accuracy with reduced dimensionality.

  4. Supervised feature learning for curvilinear structure segmentation.

    Science.gov (United States)

    Becker, Carlos; Rigamonti, Roberto; Lepetit, Vincent; Fua, Pascal

    2013-01-01

    We present a novel, fully-discriminative method for curvilinear structure segmentation that simultaneously learns a classifier and the features it relies on. Our approach requires almost no parameter tuning and, in the case of 2D images, removes the requirement for hand-designed features, thus freeing the practitioner from the time-consuming tasks of parameter and feature selection. Our approach relies on the Gradient Boosting framework to learn discriminative convolutional filters in closed form at each stage, and can operate on raw image pixels as well as additional data sources, such as the output of other methods like the Optimally Oriented Flux. We will show that it outperforms state-of-the-art curvilinear segmentation methods on both 2D images and 3D image stacks.

  5. Collective network for computer structures

    Energy Technology Data Exchange (ETDEWEB)

    Blumrich, Matthias A [Ridgefield, CT; Coteus, Paul W [Yorktown Heights, NY; Chen, Dong [Croton On Hudson, NY; Gara, Alan [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Hoenicke, Dirk [Ossining, NY; Takken, Todd E [Brewster, NY; Steinmacher-Burow, Burkhard D [Wernau, DE; Vranas, Pavlos M [Bedford Hills, NY

    2011-08-16

    A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices ate included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network and class structures. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to needs of a processing algorithm.

  6. Distance metric learning for complex networks: Towards size-independent comparison of network structures

    Science.gov (United States)

    Aliakbary, Sadegh; Motallebi, Sadegh; Rashidian, Sina; Habibi, Jafar; Movaghar, Ali

    2015-02-01

    Real networks show nontrivial topological properties such as community structure and long-tail degree distribution. Moreover, many network analysis applications are based on topological comparison of complex networks. Classification and clustering of networks, model selection, and anomaly detection are just some applications of network comparison. In these applications, an effective similarity metric is needed which, given two complex networks of possibly different sizes, evaluates the amount of similarity between the structural features of the two networks. Traditional graph comparison approaches, such as isomorphism-based methods, are not only too time consuming but also inappropriate to compare networks with different sizes. In this paper, we propose an intelligent method based on the genetic algorithms for integrating, selecting, and weighting the network features in order to develop an effective similarity measure for complex networks. The proposed similarity metric outperforms state of the art methods with respect to different evaluation criteria.

  7. Feature Extraction for Structural Dynamics Model Validation

    Energy Technology Data Exchange (ETDEWEB)

    Farrar, Charles [Los Alamos National Laboratory; Nishio, Mayuko [Yokohama University; Hemez, Francois [Los Alamos National Laboratory; Stull, Chris [Los Alamos National Laboratory; Park, Gyuhae [Chonnam Univesity; Cornwell, Phil [Rose-Hulman Institute of Technology; Figueiredo, Eloi [Universidade Lusófona; Luscher, D. J. [Los Alamos National Laboratory; Worden, Keith [University of Sheffield

    2016-01-13

    As structural dynamics becomes increasingly non-modal, stochastic and nonlinear, finite element model-updating technology must adopt the broader notions of model validation and uncertainty quantification. For example, particular re-sampling procedures must be implemented to propagate uncertainty through a forward calculation, and non-modal features must be defined to analyze nonlinear data sets. The latter topic is the focus of this report, but first, some more general comments regarding the concept of model validation will be discussed.

  8. Phase-shifting structures for isolated features

    Science.gov (United States)

    Garofalo, Joseph G.; Kostelak, Robert L.; Yang, Tungsheng

    1991-07-01

    The technique for improving optical projection-system resolution by phase-shifting alternate apertures of a periodic grating was introduced in 1982. This halves the frequency content of the image passing through the optics and should therefore double the effective resolution of such patterns. Unfortunately, as feature separation increases, the efficacy of this method diminishes. Previous work applying a similar approach to isolated features involves introducing minute, non-printable, phase-shifted assist slots around the desired feature. The diffraction side-lobes of these slots constructively interfere with the center lobe of the primary aperture. The resolution enhancement afforded be this technique is limited by the printability of the assist slots. This restraint also dictates 1X-size reticle feature dimensions and the employment of high contrast imaging resists. A new approach entails significantly oversizing the desired feature and introducing a phase-shifting region around the periphery. This type of structure affords substantial focus-exposure improvements and may either be fabricated in a single-level, self-aligned scheme or by a two-level exposure with conventional e-beam tools since the phase-shifting regions are on the order of 1 micrometers (reticle dimensions). Extensive modeling of this structure for isolated contact holes and spaces explores the myriad of trade- offs involved in an optimum design. Mask-fabrication tolerances, such as phase-shift uniformity, are also investigated. It is shown that the focus-exposure window enlarges as the overall structure dimensions increase. The degree of enhancement must therefore by weighed against packing density restrictions. Also, the structure suffers, to some degree, from the effect of side-lobes. However, for a given side-lobe intensity, this technique yields enhancements superior to the assist-slot approach. As is typical of phase-shifted systems, performance is improved as the partial coherence ((sigma

  9. Inferring network structure from cascades

    Science.gov (United States)

    Ghonge, Sushrut; Vural, Dervis Can

    2017-07-01

    Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.

  10. Supramolecular Multiblock Copolymers Featuring Complex Secondary Structures.

    Science.gov (United States)

    Elacqua, Elizabeth; Manning, Kylie B; Lye, Diane S; Pomarico, Scott K; Morgia, Federica; Weck, Marcus

    2017-09-06

    This contribution introduces main-chain supramolecular ABC and ABB'A block copolymers sustained by orthogonal metal coordination and hydrogen bonding between telechelic polymers that feature distinct secondary structure motifs. Controlled polymerization techniques in combination with supramolecular assembly are used to engineer heterotelechelic π-sheets that undergo high-fidelity association with both helical and coil-forming synthetic polymers. Our design features multiple advances to achieve our targeted structures, in particular, those emulating sheet-like structural aspects using poly(p-phenylenevinylene)s (PPVs). To engineer heterotelechelic PPVs in a sheet-like design, we engineer an iterative one-pot cross metathesis-ring-opening metathesis polymerization (CM-ROMP) strategy that affords functionalized Grubbs-II initiators that subsequently polymerize a paracyclophanediene. Supramolecular assembly of two heterotelechelic PPVs is used to realize a parallel π-sheet, wherein further orthogonal assembly with helical motifs is possible. We also construct an antiparallel π-sheet, wherein terminal PPV blocks are adjacent to a flexible coil-like poly(norbornene) (PNB). The PNB is designed, through supramolecular chain collapse, to expose benzene and perfluorobenzene motifs that promote a hairpin turn via charge-transfer-aided folding. We demonstrate that targeted helix-(π-sheet)-helix and helix-(π-sheet)-coil assemblies occur without compromising intrinsic helicity, while both parallel and antiparallel β-sheet-like structures are realized. Our main-chain orthogonal assembly approach allows the engineering of multiblock copolymer scaffolds featuring diverse secondary structures via the directional assembly of telechelic building blocks. The targeted assemblies, a mix of sequence-defined helix-sheet-coil and helix-sheet-helix architectures, are Nature-inspired synthetic mimics that expose α/β and α+β protein classes via de novo design and cooperative assembly

  11. How structure determines correlations in neuronal networks.

    Directory of Open Access Journals (Sweden)

    Volker Pernice

    2011-05-01

    Full Text Available Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.

  12. Confidence sets for network structure

    CERN Document Server

    Airoldi, Edoardo M; Wolfe, Patrick J

    2011-01-01

    Latent variable models are frequently used to identify structure in dichotomous network data, in part because they give rise to a Bernoulli product likelihood that is both well understood and consistent with the notion of exchangeable random graphs. In this article we propose conservative confidence sets that hold with respect to these underlying Bernoulli parameters as a function of any given partition of network nodes, enabling us to assess estimates of 'residual' network structure, that is, structure that cannot be explained by known covariates and thus cannot be easily verified by manual inspection. We demonstrate the proposed methodology by analyzing student friendship networks from the National Longitudinal Survey of Adolescent Health that include race, gender, and school year as covariates. We employ a stochastic expectation-maximization algorithm to fit a logistic regression model that includes these explanatory variables as well as a latent stochastic blockmodel component and additional node-specific...

  13. Structural features of algebraic quantum notations

    Directory of Open Access Journals (Sweden)

    Elizabeth Gire

    2015-09-01

    Full Text Available [This paper is part of the Focused Collection on Upper Division Physics Courses.] The formalism of quantum mechanics includes a rich collection of representations for describing quantum systems, including functions, graphs, matrices, histograms of probabilities, and Dirac notation. The varied features of these representations affect how computations are performed. For example, identifying probabilities of measurement outcomes for a state described in Dirac notation may involve identifying expansion coefficients by inspection, but if the state is described as a function, identifying those expansion coefficients often involves performing integrals. In this study, we focus on three notational systems: Dirac notation, algebraic wave-function notation, and matrix notation. These quantum notations must include information about basis states and their associated complex probability amplitudes. In this theory paper, we identify four structural features of quantum notations, which we term individuation, degree of externalization, compactness, and symbolic support for computational rules. We illustrate how student reasoning interacts with these structural features with episodes from interviews with advanced undergraduate physics majors reasoning about a superposition state of an infinite square well system. We find evidence of the students coordinating different notations through the use of Dirac notation, using an expression in Dirac notation to guide their work in another notation. These uses are supported by the high degree of individuation, compactness, and symbolic support for computation and the moderate degree of externalization provided by Dirac notation.

  14. Structural determinants of criticality in biological networks.

    Science.gov (United States)

    Valverde, Sergi; Ohse, Sebastian; Turalska, Malgorzata; West, Bruce J; Garcia-Ojalvo, Jordi

    2015-01-01

    Many adaptive evolutionary systems display spatial and temporal features, such as long-range correlations, typically associated with the critical point of a phase transition in statistical physics. Empirical and theoretical studies suggest that operating near criticality enhances the functionality of biological networks, such as brain and gene networks, in terms for instance of information processing, robustness, and evolvability. While previous studies have explained criticality with specific system features, we still lack a general theory of critical behavior in biological systems. Here we look at this problem from the complex systems perspective, since in principle all critical biological circuits have in common the fact that their internal organization can be described as a complex network. An important question is how self-similar structure influences self-similar dynamics. Modularity and heterogeneity, for instance, affect the location of critical points and can be used to tune the system toward criticality. We review and discuss recent studies on the criticality of neuronal and genetic networks, and discuss the implications of network theory when assessing the evolutionary features of criticality.

  15. Structural Determinants of Criticality in Biological Networks

    Directory of Open Access Journals (Sweden)

    Sergi eValverde

    2015-05-01

    Full Text Available Many adaptive evolutionary systems display spatial and temporal features, such as long-range correlations, typically associated with the critical point of a phase transition in statistical physics. Empirical and theoretical studies suggest that operating near criticality enhances the functionality of biological networks, such as brain and gene networks, in terms for instance of information processing, robustness and evolvability. While previous studies have explained criticality with specific system features, we still lack a general theory of critical behaviour in biological systems. Here we look at this problem from the complex systems perspective, since in principle all critical biological circuits have in common the fact that their internal organisation can be described as a complex network. An important question is how self-similar structure influences self-similar dynamics. Modularity and heterogeneity, for instance, affect the location of critical points and can be used to tune the system towards criticality. We review and discuss recent studies on the criticality of neuronal and genetic networks, and discuss the implications of network theory when assessing the evolutionary features of criticality.

  16. Higher-order structure and epidemic dynamics in clustered networks.

    Science.gov (United States)

    Ritchie, Martin; Berthouze, Luc; House, Thomas; Kiss, Istvan Z

    2014-05-07

    Clustering is typically measured by the ratio of triangles to all triples regardless of whether open or closed. Generating clustered networks, and how clustering affects dynamics on networks, is reasonably well understood for certain classes of networks (Volz et al., 2011; Karrer and Newman, 2010), e.g. networks composed of lines and non-overlapping triangles. In this paper we show that it is possible to generate networks which, despite having the same degree distribution and equal clustering, exhibit different higher-order structure, specifically, overlapping triangles and other order-four (a closed network motif composed of four nodes) structures. To distinguish and quantify these additional structural features, we develop a new network metric capable of measuring order-four structure which, when used alongside traditional network metrics, allows us to more accurately describe a network׳s topology. Three network generation algorithms are considered: a modified configuration model and two rewiring algorithms. By generating homogeneous networks with equal clustering we study and quantify their structural differences, and using SIS (Susceptible-Infected-Susceptible) and SIR (Susceptible-Infected-Recovered) dynamics we investigate computationally how differences in higher-order structure impact on epidemic threshold, final epidemic or prevalence levels and time evolution of epidemics. Our results suggest that characterising and measuring higher-order network structure is needed to advance our understanding of the impact of network topology on dynamics unfolding on the networks. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  17. Understanding Protein-Protein Interactions Using Local Structural Features

    DEFF Research Database (Denmark)

    Planas-Iglesias, Joan; Bonet, Jaume; García-García, Javier

    2013-01-01

    Protein-protein interactions (PPIs) play a relevant role among the different functions of a cell. Identifying the PPI network of a given organism (interactome) is useful to shed light on the key molecular mechanisms within a biological system. In this work, we show the role of structural features...... (loops and domains) to comprehend the molecular mechanisms of PPIs. A paradox in protein-protein binding is to explain how the unbound proteins of a binary complex recognize each other among a large population within a cell and how they find their best docking interface in a short timescale. We use...... interacting and non-interacting protein pairs to classify the structural features that sustain the binding (or non-binding) behavior. Our study indicates that not only the interacting region but also the rest of the protein surface are important for the interaction fate. The interpretation...

  18. Extracting Conceptual Feature Structures from Text

    DEFF Research Database (Denmark)

    Andreasen, Troels; Bulskov, Henrik; Lassen, Tine

    2011-01-01

    This paper describes an approach to indexing texts by their conceptual content using ontologies along with lexico-syntactic information and semantic role assignment provided by lexical resources. The conceptual content of meaningful chunks of text is transformed into conceptual feature structures...... and mapped into concepts in a generative ontology. Synonymous but linguistically quite distinct expressions are mapped to the same concept in the ontology. This allows us to perform a content-based search which will retrieve relevant documents independently of the linguistic form of the query as well...

  19. Multiple Resting-State Networks Are Associated With Tremors and Cognitive Features in Essential Tremor.

    Science.gov (United States)

    Fang, Weidong; Chen, Huiyue; Wang, Hansheng; Zhang, Han; Liu, Mengqi; Puneet, Munankami; Lv, Fajin; Cheng, Oumei; Wang, Xuefeng; Lu, Xiurong; Luo, Tianyou

    2015-12-01

    The heterogeneous clinical features of essential tremor indicate that the dysfunctions of this syndrome are not confined to motor networks, but extend to nonmotor networks. Currently, these neural network dysfunctions in essential tremor remain unclear. In this study, independent component analysis of resting-state functional MRI was used to study these neural network mechanisms. Thirty-five essential tremor patients and 35 matched healthy controls with clinical and neuropsychological tests were included, and eight resting-state networks were identified. After considering the structure and head-motion factors and testing the reliability of the selected resting-state networks, we assessed the functional connectivity changes within or between resting-state networks. Finally, image-behavior correlation analysis was performed. Compared to healthy controls, essential tremor patients displayed increased functional connectivity in the sensorimotor and salience networks and decreased functional connectivity in the cerebellum network. Additionally, increased functional network connectivity was observed between anterior and posterior default mode networks, and a decreased functional network connectivity was noted between the cerebellum network and the sensorimotor and posterior default mode networks. Importantly, the functional connectivity changes within and between these resting-state networks were correlated with the tremor severity and total cognitive scores of essential tremor patients. The findings of this study provide the first evidence that functional connectivity changes within and between multiple resting-state networks are associated with tremors and cognitive features of essential tremor, and this work demonstrates a potential approach for identifying the underlying neural network mechanisms of this syndrome. © 2015 International Parkinson and Movement Disorder Society.

  20. Structurally Dynamic Spin Market Networks

    Science.gov (United States)

    Horváth, Denis; Kuscsik, Zoltán

    The agent-based model of stock price dynamics on a directed evolving complex network is suggested and studied by direct simulation. The stationary regime is maintained as a result of the balance between the extremal dynamics, adaptivity of strategic variables and reconnection rules. The inherent structure of node agent "brain" is modeled by a recursive neural network with local and global inputs and feedback connections. For specific parametric combination the complex network displays small-world phenomenon combined with scale-free behavior. The identification of a local leader (network hub, agent whose strategies are frequently adapted by its neighbors) is carried out by repeated random walk process through network. The simulations show empirically relevant dynamics of price returns and volatility clustering. The additional emerging aspects of stylized market statistics are Zipfian distributions of fitness.

  1. Teaching Practices and Peer Network Features in Elementary Classrooms

    Science.gov (United States)

    Gest, Scott D.; Rodkin, Philip C.

    2011-01-01

    The long-term goal of this program of research is to clarify how teachers may influence features of peer networks that, in turn, may affect students' perceptions of social support, achievement-related beliefs and academic achievement. As a first step in this process, in this study the authors focus on identifying teaching practices that are…

  2. Topological Embedding Feature Based Resource Allocation in Network Virtualization

    Directory of Open Access Journals (Sweden)

    Hongyan Cui

    2014-01-01

    Full Text Available Virtualization provides a powerful way to run multiple virtual networks on a shared substrate network, which needs accurate and efficient mathematical models. Virtual network embedding is a challenge in network virtualization. In this paper, considering the degree of convergence when mapping a virtual network onto substrate network, we propose a new embedding algorithm based on topology mapping convergence-degree. Convergence-degree means the adjacent degree of virtual network’s nodes when they are mapped onto a substrate network. The contributions of our method are as below. Firstly, we map virtual nodes onto the substrate nodes with the maximum convergence-degree. The simulation results show that our proposed algorithm largely enhances the network utilization efficiency and decreases the complexity of the embedding problem. Secondly, we define the load balance rate to reflect the load balance of substrate links. The simulation results show our proposed algorithm achieves better load balance. Finally, based on the feature of star topology, we further improve our embedding algorithm and make it suitable for application in the star topology. The test result shows it gets better performance than previous works.

  3. Predictive structural dynamic network analysis.

    Science.gov (United States)

    Chen, Rong; Herskovits, Edward H

    2015-04-30

    Classifying individuals based on magnetic resonance data is an important task in neuroscience. Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data. We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data. Our method generates a dynamic Bayesian network model for each group which represents complex spatiotemporal interactions among brain regions, and then calculates a score representing that subject's deviation from expected network patterns. This network-derived score, along with other candidate predictors, are used to construct predictive models. We validated the proposed method based on simulated data and the Alzheimer's Disease Neuroimaging Initiative study. For the Alzheimer's Disease Neuroimaging Initiative study, we built a predictive model based on the baseline biomarker characterizing the baseline state and the network-based score which was constructed based on the state transition probability matrix. We found that this combined model achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity. For the Alzheimer's Disease Neuroimaging Initiative study, the model based on the baseline biomarkers achieved 0.77 accuracy. The accuracy of our model is significantly better than the model based on the baseline biomarkers (p-value=0.002). We have presented a method to classify subjects based on structural dynamic network model based scores. This method is of great importance to distinguish subjects based on structural network dynamics and the understanding of the network architecture of brain processes and disorders. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. Structural Features of Caspase-Activating Complexes

    Directory of Open Access Journals (Sweden)

    Hyun Ho Park

    2012-04-01

    Full Text Available Apoptosis, also called programmed cell death, is an orderly cellular suicide program that is critical for the development, immune regulation and homeostasis of a multi-cellular organism. Failure to control this process can lead to serious human diseases, including many types of cancer, neurodegenerative diseases, and autoimmununity. The process of apoptosis is mediated by the sequential activation of caspases, which are cysteine proteases. Initiator caspases, such as caspase-2, -8, -9, and -10, are activated by formation of caspase-activating complexes, which function as a platform to recruit caspases, providing proximity for self-activation. Well-known initiator caspase-activating complexes include (1 DISC (Death Inducing Signaling Complex, which activates caspases-8 and 10; (2 Apoptosome, which activates caspase-9; and (3 PIDDosome, which activates caspase-2. Because of the fundamental biological importance of capases, many structural and biochemical studies to understand the molecular basis of assembly mechanism of caspase-activating complexes have been performed. In this review, we summarize previous studies that have examined the structural and biochemical features of caspase-activating complexes. By analyzing the structural basis for the assembly mechanism of the caspase-activating complex, we hope to provide a comprehensive understanding of caspase activation by these important oligomeric complexes.

  5. Textural features and neural network for image classification

    Science.gov (United States)

    Haddadi, Souad; Fernandez, C.; Abdelnour, F.

    1996-03-01

    In this paper, we present a neural network approach for scene analysis: detection of human beings in images. To solve this problem, a precise classification system is required, with adaptation systems based on data processing. These systems must be largely parallel, which is why neural networks have been chosen. The first part of this text is a brief introduction to neural networks and their applications. The second part is a description of the image base composed for experiments and the low-level processing used, then we detail the method used to extract the texture feature of images. The third part describes the Bayesian method and its application to our problem. Part four shows the association of these texture processes with the neural network for identification of human beings. Finally, we conclude with the validity of the method and its future applications.

  6. Stochastic margin-based structure learning of Bayesian network classifiers.

    Science.gov (United States)

    Pernkopf, Franz; Wohlmayr, Michael

    2013-02-01

    The margin criterion for parameter learning in graphical models gained significant impact over the last years. We use the maximum margin score for discriminatively optimizing the structure of Bayesian network classifiers. Furthermore, greedy hill-climbing and simulated annealing search heuristics are applied to determine the classifier structures. In the experiments, we demonstrate the advantages of maximum margin optimized Bayesian network structures in terms of classification performance compared to traditionally used discriminative structure learning methods. Stochastic simulated annealing requires less score evaluations than greedy heuristics. Additionally, we compare generative and discriminative parameter learning on both generatively and discriminatively structured Bayesian network classifiers. Margin-optimized Bayesian network classifiers achieve similar classification performance as support vector machines. Moreover, missing feature values during classification can be handled by discriminatively optimized Bayesian network classifiers, a case where purely discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.

  7. The effect of destination linked feature selection in real-time network intrusion detection

    CSIR Research Space (South Africa)

    Mzila, P

    2013-07-01

    Full Text Available techniques in the network intrusion detection system (NIDS) is the feature selection technique. The ability of NIDS to accurately identify intrusion from the network traffic relies heavily on feature selection, which describes the pattern of the network...

  8. Stability from Structure : Metabolic Networks Are Unlike Other Biological Networks

    NARCIS (Netherlands)

    Van Nes, P.; Bellomo, D.; Reinders, M.J.T.; De Ridder, D.

    2009-01-01

    In recent work, attempts have been made to link the structure of biochemical networks to their complex dynamics. It was shown that structurally stable network motifs are enriched in such networks. In this work, we investigate to what extent these findings apply to metabolic networks. To this end, we

  9. ANALYSIS OF CLINICAL AND DERMOSCOPIC FEATURES FOR BASAL CELL CARCINOMA NEURAL NETWORK CLASSIFICATION

    Science.gov (United States)

    Cheng, Beibei; Stanley, R. Joe; Stoecker, William V; Stricklin, Sherea M.; Hinton, Kristen A.; Nguyen, Thanh K.; Rader, Ryan K.; Rabinovitz, Harold S.; Oliviero, Margaret; Moss, Randy H.

    2012-01-01

    Background Basal cell carcinoma (BCC) is the most commonly diagnosed cancer in the United States. In this research, we examine four different feature categories used for diagnostic decisions, including patient personal profile (patient age, gender, etc.), general exam (lesion size and location), common dermoscopic (blue-gray ovoids, leaf-structure dirt trails, etc.), and specific dermoscopic lesion (white/pink areas, semitranslucency, etc.). Specific dermoscopic features are more restricted versions of the common dermoscopic features. Methods Combinations of the four feature categories are analyzed over a data set of 700 lesions, with 350 BCCs and 350 benign lesions, for lesion discrimination using neural network-based techniques, including Evolving Artificial Neural Networks and Evolving Artificial Neural Network Ensembles. Results Experiment results based on ten-fold cross validation for training and testing the different neural network-based techniques yielded an area under the receiver operating characteristic curve as high as 0.981 when all features were combined. The common dermoscopic lesion features generally yielded higher discrimination results than other individual feature categories. Conclusions Experimental results show that combining clinical and image information provides enhanced lesion discrimination capability over either information source separately. This research highlights the potential of data fusion as a model for the diagnostic process. PMID:22724561

  10. Are deep neural networks really learning relevant features?

    DEFF Research Database (Denmark)

    Kereliuk, Corey; Sturm, Bob L.; Larsen, Jan

    In recent years deep neural networks (DNNs) have become a popular choice for audio content analysis. This may be attributed to various factors including advancements in training algorithms, computational power, and the potential for DNNs to implicitly learn a set of feature detectors. We have...... recently re-examined two works \\cite{sigtiaimproved}\\cite{hamel2010learning} that consider DNNs for the task of music genre recognition (MGR). These papers conclude that frame-level features learned by DNNs offer an improvement over traditional, hand-crafted features such as Mel-frequency cepstrum...... leads one to question the degree to which the learned frame-level features are actually useful for MGR. We make available a reproducible software package allowing other researchers to completely duplicate our figures and results....

  11. Are deep neural networks really learning relevant features?

    DEFF Research Database (Denmark)

    Kereliuk, Corey Mose; Larsen, Jan; Sturm, Bob L.

    In recent years deep neural networks (DNNs) have become a popular choice for audio content analysis. This may be attributed to various factors including advancements in training algorithms, computational power, and the potential for DNNs to implicitly learn a set of feature detectors. We have...... recently re-examined two works that consider DNNs for the task of music genre recognition (MGR). These papers conclude that frame-level features learned by DNNs offer an improvement over traditional, hand-crafted features such as Mel-frequency cepstrum coefficients (MFCCs). However, these conclusions were...... drawn based on training/testing using the GTZAN dataset, which is now known to contain several flaws including replicated observations and artists. We illustrate how considering these flaws dramatically changes the results, which leads one to question the degree to which the learned frame-level features...

  12. Zinc(II) and lead(II) metal-organic networks driven by a multifunctional pyridine-carboxylate building block: Hydrothermal synthesis, structural and topological features, and luminescence properties

    Science.gov (United States)

    Yang, Ling; Li, Yu; You, Ao; Jiang, Juan; Zou, Xun-Zhong; Chen, Jin-Wei; Gu, Jin-Zhong; Kirillov, Alexander M.

    2016-09-01

    4-(5-Carboxypyridin-2-yl)isophthalic acid (H3L) was applied as a flexible, multifunctional N,O-building block for the hydrothermal self-assembly synthesis of two novel coordination compounds, namely 2D [Zn(μ3-HL)(H2O)]n·nH2O (1) and 3D [Pb2(μ5-HL)(μ6-HL)]n (2) coordination polymers (CPs). These compounds were obtained in aqueous medium from a mixture containing zinc(II) or lead(II) nitrate, H3L, and sodium hydroxide. The products were isolated as stable crystalline solids and were characterized by IR spectroscopy, elemental, thermogravimetric (TGA), powder (PXRD) and single-crystal X-ray diffraction analyses. Compound 1 possesses a 2D metal-organic layer with the fes topology, which is further extended into a 3D supramolecular framework via hydrogen bonds. In contrast, compound 2 features a very complex network structure, which was topologically classified as a binodal 5,6-connected net with the unique topology defined by the point symbol of (47.63)(49.66). Compounds 1 and 2 disclose an intense blue or green luminescent emission at room temperature.

  13. Between-Classroom Differences in Peer Network Features and Students' Perceptions of the Classroom Environment

    Science.gov (United States)

    Zadzora, Kathleen; Gest, Scott D.; Rodkin, Philip C.

    2011-01-01

    The objective of this poster is to examine whether differences in the structural features of classroom peer networks (tight-knittedness, hierarchy, salience norms) are associated with differences in how individual students perceive the classroom environment (relational support from teachers and peers) and express achievement-related beliefs…

  14. Boosting feature selection for Neural Network based regression.

    Science.gov (United States)

    Bailly, Kevin; Milgram, Maurice

    2009-01-01

    The head pose estimation problem is well known to be a challenging task in computer vision and is a useful tool for several applications involving human-computer interaction. This problem can be stated as a regression one where the input is an image and the output is pan and tilt angles. Finding the optimal regression is a hard problem because of the high dimensionality of the input (number of image pixels) and the large variety of morphologies and illumination. We propose a new method combining a boosting strategy for feature selection and a neural network for the regression. Potential features are a very large set of Haar-like wavelets which are well known to be adapted to face image processing. To achieve the feature selection, a new Fuzzy Functional Criterion (FFC) is introduced which is able to evaluate the link between a feature and the output without any estimation of the joint probability density function as in the Mutual Information. The boosting strategy uses this criterion at each step: features are evaluated by the FFC using weights on examples computed from the error produced by the neural network trained at the previous step. Tests are carried out on the commonly used Pointing 04 database and compared with three state-of-the-art methods. We also evaluate the accuracy of the estimation on FacePix, a database with a high angular resolution. Our method is compared positively to a Convolutional Neural Network, which is well known to incorporate feature extraction in its first layers.

  15. A Robust Method for Inferring Network Structures.

    Science.gov (United States)

    Yang, Yang; Luo, Tingjin; Li, Zhoujun; Zhang, Xiaoming; Yu, Philip S

    2017-07-12

    Inferring the network structure from limited observable data is significant in molecular biology, communication and many other areas. It is challenging, primarily because the observable data are sparse, finite and noisy. The development of machine learning and network structure study provides a great chance to solve the problem. In this paper, we propose an iterative smoothing algorithm with structure sparsity (ISSS) method. The elastic penalty in the model is introduced for the sparse solution, identifying group features and avoiding over-fitting, and the total variation (TV) penalty in the model can effectively utilize the structure information to identify the neighborhood of the vertices. Due to the non-smoothness of the elastic and structural TV penalties, an efficient algorithm with the Nesterov's smoothing optimization technique is proposed to solve the non-smooth problem. The experimental results on both synthetic and real-world networks show that the proposed model is robust against insufficient data and high noise. In addition, we investigate many factors that play important roles in identifying the performance of ISSS.

  16. Structural Behavioral Study on the General Aviation Network Based on Complex Network

    Science.gov (United States)

    Zhang, Liang; Lu, Na

    2017-12-01

    The general aviation system is an open and dissipative system with complex structures and behavioral features. This paper has established the system model and network model for general aviation. We have analyzed integral attributes and individual attributes by applying the complex network theory and concluded that the general aviation network has influential enterprise factors and node relations. We have checked whether the network has small world effect, scale-free property and network centrality property which a complex network should have by applying degree distribution of functions and proved that the general aviation network system is a complex network. Therefore, we propose to achieve the evolution process of the general aviation industrial chain to collaborative innovation cluster of advanced-form industries by strengthening network multiplication effect, stimulating innovation performance and spanning the structural hole path.

  17. Predictive brain networks for major depression in a semi-multimodal fusion hierarchical feature reduction framework.

    Science.gov (United States)

    Yang, Jie; Yin, Yingying; Zhang, Zuping; Long, Jun; Dong, Jian; Zhang, Yuqun; Xu, Zhi; Li, Lei; Liu, Jie; Yuan, Yonggui

    2017-12-05

    Major depressive disorder (MDD) is characterized by dysregulation of distributed structural and functional networks. It is now recognized that structural and functional networks are related at multiple temporal scales. The recent emergence of multimodal fusion methods has made it possible to comprehensively and systematically investigate brain networks and thereby provide essential information for influencing disease diagnosis and prognosis. However, such investigations are hampered by the inconsistent dimensionality features between structural and functional networks. Thus, a semi-multimodal fusion hierarchical feature reduction framework is proposed. Feature reduction is a vital procedure in classification that can be used to eliminate irrelevant and redundant information and thereby improve the accuracy of disease diagnosis. Our proposed framework primarily consists of two steps. The first step considers the connection distances in both structural and functional networks between MDD and healthy control (HC) groups. By adding a constraint based on sparsity regularization, the second step fully utilizes the inter-relationship between the two modalities. However, in contrast to conventional multi-modality multi-task methods, the structural networks were considered to play only a subsidiary role in feature reduction and were not included in the following classification. The proposed method achieved a classification accuracy, specificity, sensitivity, and area under the curve of 84.91%, 88.6%, 81.29%, and 0.91, respectively. Moreover, the frontal-limbic system contributed the most to disease diagnosis. Importantly, by taking full advantage of the complementary information from multimodal neuroimaging data, the selected consensus connections may be highly reliable biomarkers of MDD. Copyright © 2017. Published by Elsevier B.V.

  18. From network structure to network reorganization: implications for adult neurogenesis

    Science.gov (United States)

    Schneider-Mizell, Casey M.; Parent, Jack M.; Ben-Jacob, Eshel; Zochowski, Michal R.; Sander, Leonard M.

    2010-12-01

    Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells.

  19. Network Traffic Features for Anomaly Detection in Specific Industrial Control System Network

    Directory of Open Access Journals (Sweden)

    Matti Mantere

    2013-09-01

    Full Text Available The deterministic and restricted nature of industrial control system networks sets them apart from more open networks, such as local area networks in office environments. This improves the usability of network security, monitoring approaches that would be less feasible in more open environments. One of such approaches is machine learning based anomaly detection. Without proper customization for the special requirements of the industrial control system network environment, many existing anomaly or misuse detection systems will perform sub-optimally. A machine learning based approach could reduce the amount of manual customization required for different industrial control system networks. In this paper we analyze a possible set of features to be used in a machine learning based anomaly detection system in the real world industrial control system network environment under investigation. The network under investigation is represented by architectural drawing and results derived from network trace analysis. The network trace is captured from a live running industrial process control network and includes both control data and the data flowing between the control network and the office network. We limit the investigation to the IP traffic in the traces.

  20. Real一time Network Flow Feature Extraction System Design

    Directory of Open Access Journals (Sweden)

    CHEN Tao

    2017-04-01

    Full Text Available Aiming at the problem that packet sampling technique has lower flow feature extraction accuracy in high-speed network,a real-time network flow feature extraction system is implemented in NetFPGA. Making full use of NetFPGA high running speed and powerful parallel processing ability,the system can support gigabit data throughput. This real-time extraction system consists of two key elements,including address mapping module and flow table core processing module. The former uses pipeline technique to index flow record quickly through Bob Jenkins hash algorithm. The latter can update flow table rapidly by parallelizing query and match flow record. Online traffic test results show that the system can achieve real-time flow feature extraction in 1 Gbps Internet COTITIeCtI OTI.

  1. Supervised neural networks for the classification of structures.

    Science.gov (United States)

    Sperduti, A; Starita, A

    1997-01-01

    Standard neural networks and statistical methods are usually believed to be inadequate when dealing with complex structures because of their feature-based approach. In fact, feature-based approaches usually fail to give satisfactory solutions because of the sensitivity of the approach to the a priori selection of the features, and the incapacity to represent any specific information on the relationships among the components of the structures. However, we show that neural networks can, in fact, represent and classify structured patterns. The key idea underpinning our approach is the use of the so called "generalized recursive neuron", which is essentially a generalization to structures of a recurrent neuron. By using generalized recursive neurons, all the supervised networks developed for the classification of sequences, such as backpropagation through time networks, real-time recurrent networks, simple recurrent networks, recurrent cascade correlation networks, and neural trees can, on the whole, be generalized to structures. The results obtained by some of the above networks (with generalized recursive neurons) on the classification of logic terms are presented.

  2. Community structure of complex networks based on continuous neural network

    Science.gov (United States)

    Dai, Ting-ting; Shan, Chang-ji; Dong, Yan-shou

    2017-09-01

    As a new subject, the research of complex networks has attracted the attention of researchers from different disciplines. Community structure is one of the key structures of complex networks, so it is a very important task to analyze the community structure of complex networks accurately. In this paper, we study the problem of extracting the community structure of complex networks, and propose a continuous neural network (CNN) algorithm. It is proved that for any given initial value, the continuous neural network algorithm converges to the eigenvector of the maximum eigenvalue of the network modularity matrix. Therefore, according to the stability of the evolution of the network symbol will be able to get two community structure.

  3. Community structure in the phonological network

    Directory of Open Access Journals (Sweden)

    Cynthia S. Q. Siew

    2013-08-01

    Full Text Available Community structure, which refers to the presence of densely connected groups within a larger network, is a common feature of several real-world networks from a variety of domains such as the human brain, social networks of hunter-gatherers and business organizations, and the World Wide Web (Porter et al., 2009. Using a community detection technique known as the Louvain optimization method, 17 communities were extracted from the giant component of the phonological network described in Vitevitch (2008. Additional analyses comparing the lexical and phonological characteristics of words in these communities against words in randomly generated communities revealed several novel discoveries. Larger communities tend to consist of short, frequent words of high degree and low age of acquisition ratings, and smaller communities tend to consist of longer, less frequent words of low degree and high age of acquisition ratings. Real communities also contained fewer different phonological segments compared to random communities, although the number of occurrences of phonological segments found in real communities was much higher than that of the same phonological segments in random communities. Interestingly, the observation that relatively few biphones occur very frequently and a large number of biphones occur rarely within communities mirrors the pattern of the overall frequency of words in a language (Zipf, 1935. The present findings have important implications for understanding the dynamics of activation spread among words in the phonological network that are relevant to lexical processing, as well as understanding the mechanisms that underlie language acquisition and the evolution of language.

  4. Structural Analysis of Complex Networks

    CERN Document Server

    Dehmer, Matthias

    2011-01-01

    Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems. Applications to biology, chemistry, linguistics, and data analysis are emphasized. The book is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science,

  5. Topological properties of four networks in protein structures

    Science.gov (United States)

    Min, Seungsik; Kim, Kyungsik; Chang, Ki-Ho; Ha, Deok-Ho; Lee, Jun-Ho

    2017-11-01

    In this paper, we investigate the complex networks of interacting amino acids in protein structures. The cellular networks and their random controls are treated for the four threshold distances between atoms. The numerical simulation and analysis are relevant to the topological properties of the complex networks in the structural classification of proteins, and we mainly estimate the network's metrics from the resultant network. The cellular network is shown to exhibit a small-world feature regardless of their structural class. The protein structure presents the positive assortative coefficients, when the topological property is described as a tendency for connectivity of high-degree nodes. We particularly show that both the modularity and the small-wordness are significantly followed the increasing function against nodes.

  6. Neural network feature selection for breast cancer diagnosis

    Science.gov (United States)

    Kocur, Catherine M.; Rogers, Steven K.; Bauer, Kenneth W., Jr.; Steppe, Jean M.; Hoffmeister, Jeffrey W.

    1995-04-01

    More than 50 million women over the age of 40 are currently at risk for breast cancer in the United States. Computer-aided diagnosis, as a second opinion to radiologists, will aid in decreasing the number of false readings of mammograms. Neural network benefits are exploited at both the classification and feature selection stages in the development of a computer-aided breast cancer diagnostic system. The multilayer perceptron is used to classify and contrast three features (angular second moment, eigenmasses, and wavelets) developed to distinguish benign from malignant lesion in a database of 94 difficult-to-diagnose digitized microcalcification cases. System performance of 74 percent correct classifications is achieved. Feature selection techniques are presented which further improve performance. Neural and decision boundary-based methods are implemented, compared, and validated to isolate and remove useless features. The contribution from this analysis is an increase to 88 percent correct classification in system performance. These feature selection techniques can also process risk factor data.

  7. Characterization of computer network events through simultaneous feature selection and clustering of intrusion alerts

    Science.gov (United States)

    Chen, Siyue; Leung, Henry; Dondo, Maxwell

    2014-05-01

    As computer network security threats increase, many organizations implement multiple Network Intrusion Detection Systems (NIDS) to maximize the likelihood of intrusion detection and provide a comprehensive understanding of intrusion activities. However, NIDS trigger a massive number of alerts on a daily basis. This can be overwhelming for computer network security analysts since it is a slow and tedious process to manually analyse each alert produced. Thus, automated and intelligent clustering of alerts is important to reveal the structural correlation of events by grouping alerts with common features. As the nature of computer network attacks, and therefore alerts, is not known in advance, unsupervised alert clustering is a promising approach to achieve this goal. We propose a joint optimization technique for feature selection and clustering to aggregate similar alerts and to reduce the number of alerts that analysts have to handle individually. More precisely, each identified feature is assigned a binary value, which reflects the feature's saliency. This value is treated as a hidden variable and incorporated into a likelihood function for clustering. Since computing the optimal solution of the likelihood function directly is analytically intractable, we use the Expectation-Maximisation (EM) algorithm to iteratively update the hidden variable and use it to maximize the expected likelihood. Our empirical results, using a labelled Defense Advanced Research Projects Agency (DARPA) 2000 reference dataset, show that the proposed method gives better results than the EM clustering without feature selection in terms of the clustering accuracy.

  8. Distributed Structure-Searchable Toxicity Database Network

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Distributed Structure-Searchable Toxicity (DSSTox) Database Network provides a public forum for search and publishing downloadable, structure-searchable,...

  9. Structural Features of an OR37 Glomerulus: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Anna-Maria Maier

    2017-12-01

    Full Text Available In the olfactory bulb (OB a sophisticated neuronal network mediates the primary processing of sensory information and extensive investigations over the past decades have greatly improved our understanding of the morphology and neuronal organization of the OB. However, efforts have mostly been focused on the different radial layers, typical for the OB and little attention has been paid to individual odorant receptor specific glomeruli, the first relay station of sensory information. It has been assumed that glomeruli processing odorant information out of different contextual fields might require accordingly specialized neuronal networks. In this study, we have analyzed and compared the structural features as well as cell types in the periglomerular (PG region of three odorant receptor specific glomeruli. The investigations were focused on glomeruli of the receptor type OR37A, a member of the unique OR37 subsystem, in comparison to glomeruli of OR18-2, a class I odorant receptor and OR256-17, a class II receptor. Each of the odorant receptor types is known to be activated by distinct odorants and their glomeruli are located in different regions of the bulb. We found significant differences in the size of the glomeruli as well as in the variability of the glomerulus size in individual mice, whereby the OR37A glomeruli featured a remarkably stable size. The number of cells surrounding a given glomerulus correlated strongly with its size which allowed comparative analyses of the surrounding cell types for individual glomeruli. The proportion of PG cells labeled by NeuN as well as putative GABAergic neurons labeled by GAD65 was quite similar for the different glomerulus types. However, the number of cells expressing distinct calcium-binding proteins, namely parvalbumin (PV, calbindin (CB or calretinin (CR varied significantly among the three glomerulus types. These data suggest that each odorant receptor specific glomerulus type may be surrounded by a

  10. Special features in managing European Training Networks (ETN)

    Science.gov (United States)

    Henkel, Daniela; Eisenhauer, Anton; Drossou-Berendes, Alexandra

    2016-04-01

    The Marie Skłodowska-Curie European Training Networks (ETN) within Horizon 2020, the EU Framework Programme for Research and Innovation, aim to train a new generation of creative and innovative early-stage researchers with focus on both scientific excellence and researchers' career development extending the traditional academic research training, and providing researchers with tools to develop scientific expertise and transferable skills needed to establish career perspectives in academia as well as non-academia. This profile is different from what we know from "typical" collaborative projects, and project consortia face specific challenges with regard to international recruitment, joint organization of network activities, financial regulations, etc. The poster will give an overview of the main ETN features emphasizing special requirements and needs, and identifying main challenges, which may rise.

  11. Social structure of Facebook networks

    Science.gov (United States)

    Traud, Amanda L.; Mucha, Peter J.; Porter, Mason A.

    2012-08-01

    We study the social structure of Facebook “friendship” networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes-gender, class year, major, high school, and residence-at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on user characteristics. We thereby examine the relative importance of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further.

  12. The algebraic structure of morphosyntactic features

    Directory of Open Access Journals (Sweden)

    Sebastian Bank

    2015-04-01

    Full Text Available The most common way of separating homophony from syncretism — which is a basic challenge for any inflectional analysis: to distinguish between accidental and systematic form-identity — is attributing only the latter to a coherent feature combination instantiating a natural class. Features predetermine which form-identities can or cannot be analyzed as natural-class syncretism. Hence, they are crucial for the restrictiveness and predictions of morphological grammar. However, most current theoretical frameworks (e.g.] Anderson 1992, Corbett & Fraser 1993, Halle & Marantz 1993, Stump 2001 do not make explicit their assumptions regarding the formal status of features. They miss out on state-of-the-art formalisms to introduce feature notations like Formal Concept Analysis (FCA, going back to Wille 1982, Ganter & Wille 1999 which provides a formal model of conceptualization in general. In this paper, I will show how FCA provides an all-embracing terminology to reproduce, visualize, and compare feature systems from different morphological frameworks, enables more precise and consistent morphological analyses, and crucially serves to rule out excessively powerful notations where the feature combinatorics are decoupled from the distributional facts they represent.

  13. Exploring the Community Structure of Complex Networks

    OpenAIRE

    Drago, Carlo

    2016-01-01

    Regarding complex networks, one of the most relevant problems is to understand and to explore community structure. In particular it is important to define the network organization and the functions associated to the different network partitions. In this context, the idea is to consider some new approaches based on interval data in order to represent the different relevant network components as communities. The method is also useful to represent the network community structure, especially the ...

  14. Exploring community structure in biological networks with random graphs.

    Science.gov (United States)

    Sah, Pratha; Singh, Lisa O; Clauset, Aaron; Bansal, Shweta

    2014-06-25

    Community structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system's functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge. Here, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks. Our model allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems.

  15. Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

    Directory of Open Access Journals (Sweden)

    Seungsoo Nam

    2018-01-01

    Full Text Available This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector, which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures.

  16. Prediction of microRNAs involved in immune system diseases through network based features.

    Science.gov (United States)

    Prabahar, Archana; Natarajan, Jeyakumar

    2017-01-01

    MicroRNAs are a class of small non-coding regulatory RNA molecules that modulate the expression of several genes at post-transcriptional level and play a vital role in disease pathogenesis. Recent research shows that a range of miRNAs are involved in the regulation of immunity and its deregulation results in immune mediated diseases such as cancer, inflammation and autoimmune diseases. Computational discovery of these immune miRNAs using a set of specific features is highly desirable. In the current investigation, we present a SVM based classification system which uses a set of novel network based topological and motif features in addition to the baseline sequential and structural features to predict immune specific miRNAs from other non-immune miRNAs. The classifier was trained and tested on a balanced set of equal number of positive and negative examples to show the discriminative power of our network features. Experimental results show that our approach achieves an accuracy of 90.2% and outperforms the classification accuracy of 63.2% reported using the traditional miRNA sequential and structural features. The proposed classifier was further validated with two immune disease sub-class datasets related to multiple sclerosis microarray data and psoriasis RNA-seq data with higher accuracy. These results indicate that our classifier which uses network and motif features along with sequential and structural features will lead to significant improvement in classifying immune miRNAs and hence can be applied to identify other specific classes of miRNAs as an extensible miRNA classification system. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Leveraging Structure to Improve Classification Performance in Sparsely Labeled Networks

    Energy Technology Data Exchange (ETDEWEB)

    Gallagher, B; Eliassi-Rad, T

    2007-10-22

    We address the problem of classification in a partially labeled network (a.k.a. within-network classification), with an emphasis on tasks in which we have very few labeled instances to start with. Recent work has demonstrated the utility of collective classification (i.e., simultaneous inferences over class labels of related instances) in this general problem setting. However, the performance of collective classification algorithms can be adversely affected by the sparseness of labels in real-world networks. We show that on several real-world data sets, collective classification appears to offer little advantage in general and hurts performance in the worst cases. In this paper, we explore a complimentary approach to within-network classification that takes advantage of network structure. Our approach is motivated by the observation that real-world networks often provide a great deal more structural information than attribute information (e.g., class labels). Through experiments on supervised and semi-supervised classifiers of network data, we demonstrate that a small number of structural features can lead to consistent and sometimes dramatic improvements in classification performance. We also examine the relative utility of individual structural features and show that, in many cases, it is a combination of both local and global network structure that is most informative.

  18. Measuring the robustness of network community structure using assortativity

    Science.gov (United States)

    Shizuka, Daizaburo; Farine, Damien R.

    2016-01-01

    The existence of discrete social clusters, or ‘communities’, is a common feature of social networks in human and nonhuman animals. The level of such community structure in networks is typically measured using an index of modularity, Q. While modularity quantifies the degree to which individuals associate within versus between social communities and provides a useful measure of structure in the social network, it assumes that the network has been well sampled. However, animal social network data is typically subject to sampling errors. In particular, the associations among individuals are often not sampled equally, and animal social network studies are often based on a relatively small set of observations. Here, we extend an existing framework for bootstrapping network metrics to provide a method for assessing the robustness of community assignment in social networks using a metric we call community assortativity (rcom). We use simulations to demonstrate that modularity can reliably detect the transition from random to structured associations in networks that differ in size and number of communities, while community assortativity accurately measures the level of confidence based on the detectability of associations. We then demonstrate the use of these metrics using three publicly available data sets of avian social networks. We suggest that by explicitly addressing the known limitations in sampling animal social network, this approach will facilitate more rigorous analyses of population-level structural patterns across social systems. PMID:26949266

  19. A Bayesian network approach to feature selection in mass spectrometry data

    Directory of Open Access Journals (Sweden)

    Cazares Lisa H

    2010-04-01

    Full Text Available Abstract Background Time-of-flight mass spectrometry (TOF-MS has the potential to provide non-invasive, high-throughput screening for cancers and other serious diseases via detection of protein biomarkers in blood or other accessible biologic samples. Unfortunately, this potential has largely been unrealized to date due to the high variability of measurements, uncertainties in the distribution of proteins in a given population, and the difficulty of extracting repeatable diagnostic markers using current statistical tools. With studies consisting of perhaps only dozens of samples, and possibly hundreds of variables, overfitting is a serious complication. To overcome these difficulties, we have developed a Bayesian inductive method which uses model-independent methods of discovering relationships between spectral features. This method appears to efficiently discover network models which not only identify connections between the disease and key features, but also organizes relationships between features--and furthermore creates a stable classifier that categorizes new data at predicted error rates. Results The method was applied to artificial data with known feature relationships and typical TOF-MS variability introduced, and was able to recover those relationships nearly perfectly. It was also applied to blood sera data from a 2004 leukemia study, and showed high stability of selected features under cross-validation. Verification of results using withheld data showed excellent predictive power. The method showed improvement over traditional techniques, and naturally incorporated measurement uncertainties. The relationships discovered between features allowed preliminary identification of a protein biomarker which was consistent with other cancer studies and later verified experimentally. Conclusions This method appears to avoid overfitting in biologic data and produce stable feature sets in a network model. The network structure provides

  20. ADHD classification using bag of words approach on network features

    Science.gov (United States)

    Solmaz, Berkan; Dey, Soumyabrata; Rao, A. Ravishankar; Shah, Mubarak

    2012-02-01

    Attention Deficit Hyperactivity Disorder (ADHD) is receiving lots of attention nowadays mainly because it is one of the common brain disorders among children and not much information is known about the cause of this disorder. In this study, we propose to use a novel approach for automatic classification of ADHD conditioned subjects and control subjects using functional Magnetic Resonance Imaging (fMRI) data of resting state brains. For this purpose, we compute the correlation between every possible voxel pairs within a subject and over the time frame of the experimental protocol. A network of voxels is constructed by representing a high correlation value between any two voxels as an edge. A Bag-of-Words (BoW) approach is used to represent each subject as a histogram of network features; such as the number of degrees per voxel. The classification is done using a Support Vector Machine (SVM). We also investigate the use of raw intensity values in the time series for each voxel. Here, every subject is represented as a combined histogram of network and raw intensity features. Experimental results verified that the classification accuracy improves when the combined histogram is used. We tested our approach on a highly challenging dataset released by NITRC for ADHD-200 competition and obtained promising results. The dataset not only has a large size but also includes subjects from different demography and edge groups. To the best of our knowledge, this is the first paper to propose BoW approach in any functional brain disorder classification and we believe that this approach will be useful in analysis of many brain related conditions.

  1. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Abstract. Networks are widely used to represent interaction pattern among the components in complex systems. Structures of real networks from different domains may vary quite significantly. As there is an interplay between network architecture and dynamics, structure plays an impor- tant role in communication and ...

  2. True Nature of Supply Network Communication Structure

    Directory of Open Access Journals (Sweden)

    Lokhman Hakim bin Osman

    2016-04-01

    Full Text Available Globalization of world economy has altered the definition of organizational structure. Global supply chain can no longer be viewed as an arm-length structure. It has become more complex. The complexity demands deeper research and understanding. This research analyzed a structure of supply network in an attempt to elucidate the true structure of the supply network. Using the quantitative Social Network Analysis methodology, findings of this study indicated that, the structure of the supply network differs depending on the types of network relations. An important implication of these findings would be a more focus resource management upon network relationship development that is based on firms’ positions in the different network structure. This research also contributes to the various strategies of effective and efficient supply chain management.

  3. Chinese lexical networks: The structure, function and formation

    Science.gov (United States)

    Li, Jianyu; Zhou, Jie; Luo, Xiaoyue; Yang, Zhanxin

    2012-11-01

    In this paper Chinese phrases are modeled using complex networks theory. We analyze statistical properties of the networks and find that phrase networks display some important features: not only small world and the power-law distribution, but also hierarchical structure and disassortative mixing. These statistical traits display the global organization of Chinese phrases. The origin and formation of such traits are analyzed from a macroscopic Chinese culture and philosophy perspective. It is interesting to find that Chinese culture and philosophy may shape the formation and structure of Chinese phrases. To uncover the structural design principles of networks, network motif patterns are studied. It is shown that they serve as basic building blocks to form the whole phrase networks, especially triad 38 (feed forward loop) plays a more important role in forming most of the phrases and other motifs. The distinct structure may not only keep the networks stable and robust, but also be helpful for information processing. The results of the paper can give some insight into Chinese language learning and language acquisition. It strengthens the idea that learning the phrases helps to understand Chinese culture. On the other side, understanding Chinese culture and philosophy does help to learn Chinese phrases. The hub nodes in the networks show the close relationship with Chinese culture and philosophy. Learning or teaching the hub characters, hub-linking phrases and phrases which are meaning related based on motif feature should be very useful and important for Chinese learning and acquisition.

  4. Evolution of Scale-Free Wireless Sensor Networks with Feature of Small-World Networks

    Directory of Open Access Journals (Sweden)

    Ying Duan

    2017-01-01

    Full Text Available Scale-free network and small-world network are the most impacting discoveries in the complex networks theories and have already been successfully proved to be highly effective in improving topology structures of wireless sensor networks. However, currently both theories are not jointly applied to have further improvements in the generation of WSN topologies. Therefore, this paper proposes a cluster-structured evolution model of WSNs considering the characteristics of both networks. With introduction of energy sensitivity and maximum limitation of degrees that a cluster head could have, the performance of our model can be ensured. In order to give an overall assessment of lifting effects of shortcuts, four placement schemes of shortcuts are analyzed. The characteristics of small-world network and scale-free network of our model are proved via theoretical derivation and simulations. Besides, we find that, by introducing shortcuts into scale-free wireless sensor network, the performance of the network can be improved concerning energy-saving and invulnerability, and we discover that the schemes constructing shortcuts between cluster heads and the sink node have better promoted effects than the scheme building shortcuts between pairs of cluster heads, and the schemes based on the preferential principle are superior to the schemes based on the random principle.

  5. Global Electricity Trade Network: Structures and Implications

    Science.gov (United States)

    Ji, Ling; Jia, Xiaoping; Chiu, Anthony S. F.; Xu, Ming

    2016-01-01

    Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions. PMID:27504825

  6. Global Electricity Trade Network: Structures and Implications.

    Science.gov (United States)

    Ji, Ling; Jia, Xiaoping; Chiu, Anthony S F; Xu, Ming

    2016-01-01

    Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions.

  7. Some structural determinants of Pavlovian conditioning in artificial neural networks.

    Science.gov (United States)

    Sánchez, José M; Galeazzi, Juan M; Burgos, José E

    2010-05-01

    This paper investigates the possible role of neuroanatomical features in Pavlovian conditioning, via computer simulations with layered, feedforward artificial neural networks. The networks' structure and functioning are described by a strongly bottom-up model that takes into account the roles of hippocampal and dopaminergic systems in conditioning. Neuroanatomical features were simulated as generic structural or architectural features of neural networks. We focused on the number of units per hidden layer and connectivity. The effect of the number of units per hidden layer was investigated through simulations of resistance to extinction in fully connected networks. Large networks were more resistant to extinction than small networks, a stochastic effect of the asynchronous random procedure used in the simulator to update activations and weights. These networks did not simulate second-order conditioning because weight competition prevented conditioning to a stimulus after conditioning to another. Partially connected networks simulated second-order conditioning and devaluation of the second-order stimulus after extinction of a similar first-order stimulus. Similar stimuli were simulated as nonorthogonal input-vectors. Copyright (c) 2009 Elsevier B.V. All rights reserved.

  8. Taxonomies of networks from community structure

    Science.gov (United States)

    Onnela, Jukka-Pekka; Fenn, Daniel J.; Reid, Stephen; Porter, Mason A.; Mucha, Peter J.; Fricker, Mark D.; Jones, Nick S.

    2012-09-01

    The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: They can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi.

  9. Evaluation of feature detection algorithms for structure from motion

    CSIR Research Space (South Africa)

    Govender, N

    2009-11-01

    Full Text Available Structure from motion is a widely-used technique in computer vision to perform 3D reconstruction. The 3D structure is recovered by analysing the motion of an object, based on its features, over time. The typical steps involved in SFM are feature...

  10. Generic Structure Potential Analysis of Feature Articles in Nigerian ...

    African Journals Online (AJOL)

    This paper investigated the generic structure potential of feature articles in four Nigerian newspapers: The Punch, The Nation, Vanguard and Nigerian Tribune. Halliday and Hassan's concept of Generic Structure Potential served as the theoretical framework. The feature articles analysed had three obligatory elements: ...

  11. Robustness and structure of complex networks

    Science.gov (United States)

    Shao, Shuai

    This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack -- localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks. Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack -- localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system. In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component Pinfinity. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erdoḧs-Renyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent lambda. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks

  12. Immunization of networks with community structure

    Energy Technology Data Exchange (ETDEWEB)

    Masuda, Naoki [Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656 (Japan); PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012 (Japan)], E-mail: masuda@mist.i.u-tokyo.ac.jp

    2009-12-15

    In this study, an efficient method to immunize modular networks (i.e. networks with community structure) is proposed. The immunization of networks aims at fragmenting networks into small parts with a small number of removed nodes. Its applications include prevention of epidemic spreading, protection against intentional attacks on networks, and conservation of ecosystems. Although preferential immunization of hubs is efficient, good immunization strategies for modular networks have not been established. On the basis of an immunization strategy based on eigenvector centrality, we develop an analytical framework for immunizing modular networks. To this end, we quantify the contribution of each node to the connectivity in a coarse-grained network among modules. We verify the effectiveness of the proposed method by applying it to model and real networks with modular structure.

  13. Airline network structure in competitive market

    Directory of Open Access Journals (Sweden)

    Babić Danica D.

    2014-01-01

    Full Text Available Airline's network is the key element of its business strategy and selected network structure will not have influence only on the airline's costs but could gain some advantage in revenues, too. Network designing implies that an airline has to make decisions about markets that it will serve and how to serve those markets. Network choice raises the following questions for an airline: a what markets to serve, b how to serve selected markets, c what level of service to offer, d what are the benefits/cost of the that decisions and e what is the influence of the competition. We analyzed the existing airline business models and corresponding network structure. The paper highlights the relationship between the network structures and the airline business strategies. Using a simple model we examine the relationship between the network structure and service quality in deregulated market.

  14. Network Structure, Collaborative Context, and Individual Creativity

    DEFF Research Database (Denmark)

    Stea, Diego; Soda, Giuseppe; Pedersen, Torben

    2016-01-01

    outcomes often assumes that different network structures embody specific individual behaviors. This paper challenges the widespread assumption that dense, heavily bonded network structures imply a collaborative attitude on the part of network actors. We propose that collaboration can also be contextual......Network research has yet to determine whether bonding ties or bridging ties are more beneficial for individual creativity, but the debate has mostly overlooked the organizational context in which such ties are formed. In particular, the causal chain connecting network structures and individual...... and exogenous to a network’s structural characteristics, such that it moderates the effects of both dense and brokered networks on individual creativity. Specifically, we argue that knowledge acquisition and, in turn, individual creativity are more likely when an individual’s network position has a good fit...

  15. Unique structural features facilitate lizard tail autotomy

    DEFF Research Database (Denmark)

    Sanggaard, Kristian W; Danielsen, Carl Chr; Wogensen, Lise

    2012-01-01

    that tail shedding by the Tokay gecko (Gekko gecko) and the associated extracellular matrix (ECM) rupture were independent of proteolysis. Instead, lizard caudal autotomy relied on biological adhesion facilitated by surface microstructures. Results based on bio-imaging techniques demonstrated that the tail...... of Gekko gecko was pre-severed at distinct sites and that its structural integrity depended on the adhesion between these segments....

  16. The relevance of network micro-structure for neural dynamics

    Directory of Open Access Journals (Sweden)

    Volker ePernice

    2013-06-01

    Full Text Available The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previousstudies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neuronsin recurrent networks. However, typically very simple random network models are considered in such studies. Here weuse a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much morevariable than commonly used network models, and which therefore promise to sample the space of recurrent networks ina more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology insimulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive datasetof networks and neuronal simulations we assess statistical relations between features of the network structure and the spikingactivity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics ofboth single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistentrelations between activity characteristics like spike-train irregularity or correlations and network properties, for example thedistributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that itis possible to estimate structural characteristics of the network from activity data. We also assess higher order correlationsof spiking activity in the various networks considered here, and find that their occurrence strongly depends on the networkstructure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpretspike train recordings from neural circuits.

  17. PARALLEL ALGORITHM FOR BAYESIAN NETWORK STRUCTURE LEARNING

    Directory of Open Access Journals (Sweden)

    S. A. Arustamov

    2013-03-01

    Full Text Available The article deals with implementation of a scalable parallel algorithm for structure learning of Bayesian network. Comparative analysis of sequential and parallel algorithms is done.

  18. Featured Image: Structures in the Interstellar Medium

    Science.gov (United States)

    Kohler, Susanna

    2017-02-01

    This beautiful false-color image (which covers 57 degrees2; click for the full view!) reveals structures in the hydrogen gas that makes up the diffuse atomic interstellar medium at intermediate latitudes in our galaxy. The imagewas created by representing three velocity channels with colors red for gas moving at 7.59 km/s, green for 5.12 km/s, and blue for 2.64 km/s and it shows the dramatically turbulent and filamentary structure of this gas. This image is one of many stunning, high-resolution observations that came out of the DRAO HI Intermediate Galactic Latitude Survey, a program that used the Synthesis Telescope at the Dominion Radio Astrophysical Observatory in British Columbia to map faint hydrogen emission at intermediate latitudes in the Milky Way. The findings from the program were recently published in a study led by Kevin Blagrave (Canadian Institute for Theoretical Astrophysics, University of Toronto); to find out more about what they learned, check out the paper below!CitationK. Blagrave et al 2017 ApJ 834 126. doi:10.3847/1538-4357/834/2/126

  19. Sensitive Dependence of Optimal Network Dynamics on Network Structure

    Directory of Open Access Journals (Sweden)

    Takashi Nishikawa

    2017-11-01

    Full Text Available The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important long-standing problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here, we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, using diffusively coupled systems as examples, we demonstrate that the stability of a dynamical state can exhibit sensitivity to unweighted structural perturbations (i.e., link removals and node additions for undirected optimal networks and to weighted perturbations (i.e., small changes in link weights for directed optimal networks. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of undirected optimal networks and the prevalence of eigenvector degeneracy in directed optimal networks. These findings establish a unified characterization of networks optimized for dynamical stability, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, network diffusion, and several other network processes. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.

  20. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Among all biological networks studied here, the undirected structure of neuronal networks not only possesses the small-world property but the same is also expressed remarkably to a higher degree compared to any randomly generated network which possesses the same degree sequence. A relatively high percentage of ...

  1. Structure of triadic relations in multiplex networks

    Science.gov (United States)

    Cozzo, Emanuele; Kivelä, Mikko; De Domenico, Manlio; Solé-Ribalta, Albert; Arenas, Alex; Gómez, Sergio; Porter, Mason A.; Moreno, Yamir

    2015-07-01

    Recent advances in the study of networked systems have highlighted that our interconnected world is composed of networks that are coupled to each other through different ‘layers’ that each represent one of many possible subsystems or types of interactions. Nevertheless, it is traditional to aggregate multilayer networks into a single weighted network in order to take advantage of existing tools. This is admittedly convenient, but it is also extremely problematic, as important information can be lost as a result. It is therefore important to develop multilayer generalizations of network concepts. In this paper, we analyze triadic relations and generalize the idea of transitivity to multiplex networks. By focusing on triadic relations, which yield the simplest type of transitivity, we generalize the concept and computation of clustering coefficients to multiplex networks. We show how the layered structure of such networks introduces a new degree of freedom that has a fundamental effect on transitivity. We compute multiplex clustering coefficients for several real multiplex networks and illustrate why one must take great care when generalizing standard network concepts to multiplex networks. We also derive analytical expressions for our clustering coefficients for ensemble averages of networks in a family of random multiplex networks. Our analysis illustrates that social networks have a strong tendency to promote redundancy by closing triads at every layer and that they thereby have a different type of multiplex transitivity from transportation networks, which do not exhibit such a tendency. These insights are invisible if one only studies aggregated networks.

  2. Unique structural features facilitate lizard tail autotomy.

    Directory of Open Access Journals (Sweden)

    Kristian W Sanggaard

    Full Text Available Autotomy refers to the voluntary shedding of a body part; a renowned example is tail loss among lizards as a response to attempted predation. Although many aspects of lizard tail autotomy have been studied, the detailed morphology and mechanism remains unclear. In the present study, we showed that tail shedding by the Tokay gecko (Gekko gecko and the associated extracellular matrix (ECM rupture were independent of proteolysis. Instead, lizard caudal autotomy relied on biological adhesion facilitated by surface microstructures. Results based on bio-imaging techniques demonstrated that the tail of Gekko gecko was pre-severed at distinct sites and that its structural integrity depended on the adhesion between these segments.

  3. Network repair based on community structure

    Science.gov (United States)

    Wang, Tianyu; Zhang, Jun; Sun, Xiaoqian; Wandelt, Sebastian

    2017-06-01

    Real-world complex systems are often fragile under disruptions. Accordingly, research on network repair has been studied intensively. Recently proposed efficient strategies for network disruption, based on collective influence, call for more research on efficient network repair strategies. Existing strategies are often designed to repair networks with local information only. However, the absence of global information impedes the creation of efficient repairs. Motivated by this limitation, we propose a concept of community-level repair, which leverages the community structure of the network during the repair process. Moreover, we devise a general framework of network repair, with in total six instances. Evaluations on real-world and random networks show the effectiveness and efficiency of the community-level repair approaches, compared to local and random repairs. Our study contributes to a better understanding of repair processes, and reveals that exploitation of the community structure improves the repair process on a disrupted network significantly.

  4. Exponential random graph models for networks with community structure.

    Science.gov (United States)

    Fronczak, Piotr; Fronczak, Agata; Bujok, Maksymilian

    2013-09-01

    Although the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field.

  5. Features of micromorphological structure of medicinal hyssop

    Directory of Open Access Journals (Sweden)

    Lyudmyla A. Kotyuk

    2016-06-01

    Full Text Available Micromorphological peculiarities of the structure of vegetative and generative organs of Hyssopus officinalis were analyzed. The epidermis of H. officinalis reveals diacyctic stomata and external outgrowths: glandular and covering trichomes, as well as peltate essential oil glands. Capitate and bent indumentary (covering trichomes occur on the stem, while on the leaves peltate glands, conical and bent uni- and multicellular trichomes were observed. On the calyx, in the midrib region, there are peltate glands while the ribs are densely covered with indumentary and glandular trichomes. The corolla’s adaxial surface is covered with long indumentary trichomes, with sparse peltate glands occurring on the margins. The highest density of essential oil peltate glands is found on the adaxial surface of the calyx upper lip (15.8±2.54 pcs./mm2 and on the leaf abaxial surface (13.6±2.40 pcs./mm2. Glands with the largest diameter (47.82±2.82 μm are located on the leaf adaxial surface.

  6. STRUCTURE AND COOPTATION IN ORGANIZATION NETWORK

    Directory of Open Access Journals (Sweden)

    Valéria Riscarolli

    2007-10-01

    Full Text Available Business executive are rethinking business concept, based on horizontalization principles. As so, most organizational functions are outsourced, leading the enterprise to build business through a network of organizations. Here we study the case of Cia Hering’s network of organizations, a leader in knit apparel segment in Latin America (IEMI, 2004, looking at the network’s structure and levels of cooptation. A theoretical model was used using Quinn et al. (2001 “sun ray” network structure as basis to analyze the case study. Main results indicate higher degree of structural conformity, but incipient degree of coopetation in the network.

  7. Network structure of inter-industry flows

    Science.gov (United States)

    McNerney, James; Fath, Brian D.; Silverberg, Gerald

    2013-12-01

    We study the structure of inter-industry relationships using networks of money flows between industries in 45 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, service industries, and extraction industries.

  8. Network structure of inter-industry flows

    CERN Document Server

    McNerney, James; Silverberg, Gerald

    2012-01-01

    We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, service industries, and extraction industries.

  9. Network structure of inter-industry flows

    OpenAIRE

    McNerney, J.; Fath, B.D.; G. Silverberg

    2012-01-01

    We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, servic...

  10. Network Structure of Inter-Industry Flows

    NARCIS (Netherlands)

    McNerney, J.; Fath, B.D.; Silverberg, G.P.

    2015-01-01

    We study the structure of inter-industry relationships using networks of money flows between industries in 45 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community

  11. Learning Latent Structure in Complex Networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...... prediction performance of the learning based approaches and other widely used link prediction approaches in 14 networks ranging from medium size to large networks with more than a million nodes. While link prediction is typically well above chance for all networks, we find that the learning based mixed...... membership stochastic block model of Airoldi et al., performs well and often best in our experiments. The added complexity of the LD model improves link predictions for four of the 14 networks....

  12. Neural Networks for Medical Image Processing: A Study of Feature Identification

    OpenAIRE

    Dayhoff, Ruth E.; Dayhoff, Judith E.

    1988-01-01

    Neural networks, a parallel computing architecture modelled on living nervous systems, are able to “learn” by example. The ability of a simulated neural network to distinguish among simulated microscopic amoebae nuclei images was studied. The neural network was successfully shown to organize feature detectors without the intermediate step of manual identification of salient features. The feature detectors were mapped onto the image format and the issue of redundancy was examined.

  13. Feature Comparison in Structural Health Monitoring of a Vehicle Crane

    National Research Council Canada - National Science Library

    Kullaa, J; Heine, T

    2008-01-01

    Vibration-based structural health monitoring of a vehicle crane was studied. The performance of different features to detect damage was investigated after eliminating the normal operational variations using factor analysis...

  14. Network structure and travel time perception.

    Science.gov (United States)

    Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

    2013-01-01

    The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time.

  15. Deep Salient Feature Based Anti-Noise Transfer Network for Scene Classification of Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Xi Gong

    2018-03-01

    Full Text Available Remote sensing (RS scene classification is important for RS imagery semantic interpretation. Although tremendous strides have been made in RS scene classification, one of the remaining open challenges is recognizing RS scenes in low quality variance (e.g., various scales and noises. This paper proposes a deep salient feature based anti-noise transfer network (DSFATN method that effectively enhances and explores the high-level features for RS scene classification in different scales and noise conditions. In DSFATN, a novel discriminative deep salient feature (DSF is introduced by saliency-guided DSF extraction, which conducts a patch-based visual saliency (PBVS algorithm using “visual attention” mechanisms to guide pre-trained CNNs for producing the discriminative high-level features. Then, an anti-noise network is proposed to learn and enhance the robust and anti-noise structure information of RS scene by directly propagating the label information to fully-connected layers. A joint loss is used to minimize the anti-noise network by integrating anti-noise constraint and a softmax classification loss. The proposed network architecture can be easily trained with a limited amount of training data. The experiments conducted on three different scale RS scene datasets show that the DSFATN method has achieved excellent performance and great robustness in different scales and noise conditions. It obtains classification accuracy of 98.25%, 98.46%, and 98.80%, respectively, on the UC Merced Land Use Dataset (UCM, the Google image dataset of SIRI-WHU, and the SAT-6 dataset, advancing the state-of-the-art substantially.

  16. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    Bohr, Henrik

    1998-01-01

    This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

  17. Feature selection for anomaly–based network intrusion detection using cluster validity indices

    CSIR Research Space (South Africa)

    Naidoo, Tyrone

    2015-09-01

    Full Text Available data, which is rarely available in operational networks. It uses normalized cluster validity indices as an objective function that is optimized over the search space of candidate feature subsets via a genetic algorithm. Feature sets produced...

  18. Exploring the structural regularities in networks

    CERN Document Server

    Shen, Hua-Wei; Guo, Jia-Feng

    2011-01-01

    In this paper, we consider the problem of exploring structural regularities of networks by dividing the nodes of a network into groups such that the members of each group have similar patterns of connections to other groups. Specifically, we propose a general statistical model to describe network structure. In this model, group is viewed as hidden or unobserved quantity and it is learned by fitting the observed network data using the expectation-maximization algorithm. Compared with existing models, the most prominent strength of our model is the high flexibility. This strength enables it to possess the advantages of existing models and overcomes their shortcomings in a unified way. As a result, not only broad types of structure can be detected without prior knowledge of what type of intrinsic regularities exist in the network, but also the type of identified structure can be directly learned from data. Moreover, by differentiating outgoing edges from incoming edges, our model can detect several types of stru...

  19. Network Structure, Collaborative Context, and Individual Creativity

    DEFF Research Database (Denmark)

    Stea, Diego; Soda, Giuseppe; Pedersen, Torben

    2016-01-01

    and exogenous to a network’s structural characteristics, such that it moderates the effects of both dense and brokered networks on individual creativity. Specifically, we argue that knowledge acquisition and, in turn, individual creativity are more likely when an individual’s network position has a good fit...... with the network’s organizational context. Thus, actors in dense network structures acquire more knowledge and eventually become more creative in organizational contexts where collaboration is high. Conversely, brokers who arbitrage information across disconnected network contacts acquire more valuable knowledge...

  20. Object detection via structural feature selection and shape model.

    Science.gov (United States)

    Zhang, Huigang; Bai, Xiao; Zhou, Jun; Cheng, Jian; Zhao, Huijie

    2013-12-01

    In this paper, we propose an approach for object detection via structural feature selection and part-based shape model. It automatically learns a shape model from cluttered training images without need to explicitly use bounding boxes on objects. Our approach first builds a class-specific codebook of local contour features, and then generates structural feature descriptors by combining context shape information. These descriptors are robust to both within-class variations and scale changes. Through exploring pairwise image matching using fast earth mover's distance, feature weights can be iteratively updated. Those discriminative foreground features are assigned high weights and then selected to build a part-based shape model. Finally, object detection is performed by matching each testing image with this model. Experiments show that the proposed method is very effective. It has achieved comparable performance to the state-of-the-art shape-based detection methods, but requires much less training information.

  1. Structure Prediction for Gland Segmentation with Hand-Crafted and Deep Convolutional Features.

    Science.gov (United States)

    Manivannan, Siyamalan; Li, Wenqi; Zhang, Jianguo; Trucco, Emanuele; McKenna, Stephen

    2017-09-08

    We present a novel method to segment instances of glandular structures from colon histopathology images. We use a structure learning approach which represents local spatial configurations of class labels, capturing structural information normally ignored by sliding-window methods. This allows us to reveal different spatial structures of pixel labels (e.g., locations between adjacent glands, or far from glands), and to identify correctly neighbouring glandular structures as separate instances. Exemplars of label structures are obtained via clustering and used to train support vector machine classifiers. The label structures predicted are then combined and post-processed to obtain segmentation maps. We combine hand-crafted, multi-scale image features with features computed by a deep convolutional network trained to map images to segmentation maps. We evaluate the proposed method on the public domain GlaS dataset, which allows extensive comparisons with recent, alternative methods. Using the GlaS contest protocol, our method achieves the overall best performance.

  2. Information transfer in community structured multiplex networks

    Science.gov (United States)

    Solé Ribalta, Albert; Granell, Clara; Gómez, Sergio; Arenas, Alex

    2015-08-01

    The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  3. Information transfer in community structured multiplex networks

    Directory of Open Access Journals (Sweden)

    Albert eSolé Ribalta

    2015-08-01

    Full Text Available The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.. The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  4. Similarity-based Regularized Latent Feature Model for Link Prediction in Bipartite Networks.

    Science.gov (United States)

    Wang, Wenjun; Chen, Xue; Jiao, Pengfei; Jin, Di

    2017-12-05

    Link prediction is an attractive research topic in the field of data mining and has significant applications in improving performance of recommendation system and exploring evolving mechanisms of the complex networks. A variety of complex systems in real world should be abstractly represented as bipartite networks, in which there are two types of nodes and no links connect nodes of the same type. In this paper, we propose a framework for link prediction in bipartite networks by combining the similarity based structure and the latent feature model from a new perspective. The framework is called Similarity Regularized Nonnegative Matrix Factorization (SRNMF), which explicitly takes the local characteristics into consideration and encodes the geometrical information of the networks by constructing a similarity based matrix. We also develop an iterative scheme to solve the objective function based on gradient descent. Extensive experiments on a variety of real world bipartite networks show that the proposed framework of link prediction has a more competitive, preferable and stable performance in comparison with the state-of-art methods.

  5. Modeling structure and resilience of the dark network.

    Science.gov (United States)

    De Domenico, Manlio; Arenas, Alex

    2017-02-01

    While the statistical and resilience properties of the Internet are no longer changing significantly across time, the Darknet, a network devoted to keep anonymous its traffic, still experiences rapid changes to improve the security of its users. Here we study the structure of the Darknet and find that its topology is rather peculiar, being characterized by a nonhomogeneous distribution of connections, typical of scale-free networks; very short path lengths and high clustering, typical of small-world networks; and lack of a core of highly connected nodes. We propose a model to reproduce such features, demonstrating that the mechanisms used to improve cybersecurity are responsible for the observed topology. Unexpectedly, we reveal that its peculiar structure makes the Darknet much more resilient than the Internet (used as a benchmark for comparison at a descriptive level) to random failures, targeted attacks, and cascade failures, as a result of adaptive changes in response to the attempts of dismantling the network across time.

  6. Modeling structure and resilience of the dark network

    Science.gov (United States)

    De Domenico, Manlio; Arenas, Alex

    2017-02-01

    While the statistical and resilience properties of the Internet are no longer changing significantly across time, the Darknet, a network devoted to keep anonymous its traffic, still experiences rapid changes to improve the security of its users. Here we study the structure of the Darknet and find that its topology is rather peculiar, being characterized by a nonhomogeneous distribution of connections, typical of scale-free networks; very short path lengths and high clustering, typical of small-world networks; and lack of a core of highly connected nodes. We propose a model to reproduce such features, demonstrating that the mechanisms used to improve cybersecurity are responsible for the observed topology. Unexpectedly, we reveal that its peculiar structure makes the Darknet much more resilient than the Internet (used as a benchmark for comparison at a descriptive level) to random failures, targeted attacks, and cascade failures, as a result of adaptive changes in response to the attempts of dismantling the network across time.

  7. Information transfer in community structured multiplex networks

    CERN Document Server

    Solé-Ribalta, Albert; Gómez, Sergio; Arenas, Alex

    2015-01-01

    The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer ...

  8. STRUCTURAL FEATURES OF PLANT CHITINASES AND CHITIN-BINDING PROTEINS

    NARCIS (Netherlands)

    BEINTEMA, JJ

    1994-01-01

    Structural features of plant chitinases and chitin-binding proteins are discussed. Many of these proteins consist of multiple domains,of which the chitin-binding hevein domain is a predominant one. X-ray and NMR structures of representatives of the major classes of these proteins are available now,

  9. Industrial entrepreneurial network: Structural and functional analysis

    Science.gov (United States)

    Medvedeva, M. A.; Davletbaev, R. H.; Berg, D. B.; Nazarova, J. J.; Parusheva, S. S.

    2016-12-01

    Structure and functioning of two model industrial entrepreneurial networks are investigated in the present paper. One of these networks is forming when implementing an integrated project and consists of eight agents, which interact with each other and external environment. The other one is obtained from the municipal economy and is based on the set of the 12 real business entities. Analysis of the networks is carried out on the basis of the matrix of mutual payments aggregated over the certain time period. The matrix is created by the methods of experimental economics. Social Network Analysis (SNA) methods and instruments were used in the present research. The set of basic structural characteristics was investigated: set of quantitative parameters such as density, diameter, clustering coefficient, different kinds of centrality, and etc. They were compared with the random Bernoulli graphs of the corresponding size and density. Discovered variations of random and entrepreneurial networks structure are explained by the peculiarities of agents functioning in production network. Separately, were identified the closed exchange circuits (cyclically closed contours of graph) forming an autopoietic (self-replicating) network pattern. The purpose of the functional analysis was to identify the contribution of the autopoietic network pattern in its gross product. It was found that the magnitude of this contribution is more than 20%. Such value allows using of the complementary currency in order to stimulate economic activity of network agents.

  10. Structural features that predict real-value fluctuations of globular proteins.

    Science.gov (United States)

    Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

    2012-05-01

    It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real value of residue fluctuations using the support vector regression (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson's correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs is that the former predicts the real value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. Copyright © 2012 Wiley Periodicals, Inc.

  11. Nonparametric Statistical Structuring of Knowledge Systems Using Binary Feature Matches

    DEFF Research Database (Denmark)

    Mørup, Morten; Kano Glückstad, Fumiko; Herlau, Tue

    2014-01-01

    matches and structure the data into groups at the level in which they have statistical support. The model naturally extends to structuring and aligning an arbitrary number of systems. We analyze three datasets on educational concepts and their features and demonstrate how the proposed model can both...... statistical support and how this approach generalizes to the structuring and alignment of knowledge systems. We propose a non-parametric Bayesian generative model for structuring binary feature data that does not depend on a specific choice of similarity measure. We jointly model all combinations of binary...... be used to structure each system separately or to jointly align two or more systems. The proposed method forms a promising new framework for the statistical modeling and alignment of structure across an arbitrary number of systems....

  12. Nonparametric statistical structuring of knowledge systems using binary feature matches

    DEFF Research Database (Denmark)

    Mørup, Morten; Glückstad, Fumiko Kano; Herlau, Tue

    2014-01-01

    matches and structure the data into groups at the level in which they have statistical support. The model naturally extends to structuring and aligning an arbitrary number of systems. We analyze three datasets on educational concepts and their features and demonstrate how the proposed model can both...... statistical support and how this approach generalizes to the structuring and alignment of knowledge systems. We propose a non-parametric Bayesian generative model for structuring binary feature data that does not depend on a specific choice of similarity measure. We jointly model all combinations of binary...... be used to structure each system separately or to jointly align two or more systems. The proposed method forms a promising new framework for the statistical modeling and alignment of structure across an arbitrary number of systems....

  13. THE COMMERCIAL BANK AS NETWORK STRUCTURE

    Directory of Open Access Journals (Sweden)

    D. O. Dyl

    2010-05-01

    Full Text Available The article examines the problems of the modern enterprise as a network structure that meets the increasing processes of globalization and the rise of postmodern trends. The definition of the term «a network of commercial bank» and the main characteristics of such a definition are given.

  14. Feature Biases in Early Word Learning: Network Distinctiveness Predicts Age of Acquisition

    Science.gov (United States)

    Engelthaler, Tomas; Hills, Thomas T.

    2017-01-01

    Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge…

  15. The interplay between microscopic and mesoscopic structures in complex networks.

    Directory of Open Access Journals (Sweden)

    Jörg Reichardt

    Full Text Available Understanding a complex network's structure holds the key to understanding its function. The physics community has contributed a multitude of methods and analyses to this cross-disciplinary endeavor. Structural features exist on both the microscopic level, resulting from differences between single node properties, and the mesoscopic level resulting from properties shared by groups of nodes. Disentangling the determinants of network structure on these different scales has remained a major, and so far unsolved, challenge. Here we show how multiscale generative probabilistic exponential random graph models combined with efficient, distributive message-passing inference techniques can be used to achieve this separation of scales, leading to improved detection accuracy of latent classes as demonstrated on benchmark problems. It sheds new light on the statistical significance of motif-distributions in neural networks and improves the link-prediction accuracy as exemplified for gene-disease associations in the highly consequential Online Mendelian Inheritance in Man database.

  16. Learning Bayesian Network Model Structure from Data

    National Research Council Canada - National Science Library

    Margaritis, Dimitris

    2003-01-01

    In this thesis I address the important problem of the determination of the structure of directed statistical models, with the widely used class of Bayesian network models as a concrete vehicle of my ideas...

  17. Structural features on 4Vesta: Observations and analysis

    Science.gov (United States)

    Buczkowski, D.; Kahn, E.; Barnouin, O. S.; Wyrick, D. Y.; Gaskell, R. W.; Yingst, R.; Williams, D. A.; Garry, W. B.; LeCorre, L.; Nathues, A.; Scully, J. E.; Blewett, D. T.; Hiesinger, H.; Schenk, P.; Mest, S. C.; Schmedemann, N.; Krohn, K.; Jaumann, R.; Raymond, C. A.; Roatsch, T.; Preusker, F.; Reddy, V.; Denevi, B. W.; Filacchione, G.; Pieters, C. M.; Russell, C. T.

    2011-12-01

    Images of 4Vesta taken by the Framing Camera (FC) instrument on the Dawn spacecraft reveal the presence of large-scale structural features on the surface of the asteroid. We map these structures directly onto the Vesta shape model, using the Small Body Mapping Tool (SBMT). The structures can thus be modeled to define planes that cut through the asteroid, and the pole of the plane described by each individual lineament can be determined. We then compare these poles to determine if they are similarly oriented or if visual identification is confused by the shape of the asteroid. Structural features observed to date include an equatorial set of wide flat-floor troughs, bounded by steep scarps. In some regions of the asteroid these troughs are preferentially expressed as pit crater chains, a type of feature commonly associated with graben on other planetary bodies. A relatively muted flat-floor trough starting at ~75°E longitude extends to the northwest at an angle from the equatorial troughs; a set of grooves are similarly oriented and appear to be associated structures. At least two sets of grooves and ridges are identified on the floor of the South Polar Feature. The more prominent set of structures appear to trend along a north-south line and several cut the central hill. However, a set of smaller features are observed trending east-west. Analysis of these structures will contribute to our knowledge of the stress history of 4Vesta and may support models for the formation of the South Polar Feature. The authors gratefully acknowledge the support of the Dawn Instrument, Operations, and Science Teams.

  18. Nonparametric inference of network structure and dynamics

    Science.gov (United States)

    Peixoto, Tiago P.

    The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among

  19. Structural Features of Antitumor Titanium Agents and Related Compounds

    Science.gov (United States)

    Caruso, Francesco; Rossi, Miriam; Opazo, Cristian; Pettinari, Claudio

    2005-01-01

    Previous studies established some Ti compounds as having marked activity against tumors of the gastrointestinal tract and lack of side effects common to widely used cytostatic agents. We describe pertinent structural features of known antitumor Ti agents and other potentially active compounds. Particularly noteworthy features are that Ti-O bonds are short and Ti-O-Ti bond angles are large, demonstrating that in these compounds the O binding has high s-character approaching sp hybridization. PMID:18365107

  20. Structural Features of Antitumor Titanium Agents and Related Compounds

    OpenAIRE

    Caruso, Francesco; Rossi, Miriam; Opazo, Cristian; Pettinari, Claudio

    2005-01-01

    Previous studies established some Ti compounds as having marked activity against tumors of the gastrointestinal tract and lack of side effects common to widely used cytostatic agents. We describe pertinent structural features of known antitumor Ti agents and other potentially active compounds. Particularly noteworthy features are that Ti-O bonds are short and Ti-O-Ti bond angles are large, demonstrating that in these compounds the O binding has high s-character approaching sp hybridization.

  1. Network Structure, Collaborative Context, and Individual Creativity

    DEFF Research Database (Denmark)

    Soda, Giuseppe; Stea, Diego; Pedersen, Torben

    2017-01-01

    attitude on the part of the embedded actors and propose that the level of collaboration in a network can be independent from that network’s structural characteristics, such that it moderates the effects of closed and brokering network positions on the acquisition of knowledge that supports creativity....... Individuals embedded in closed networks acquire more knowledge and become more creative when the level of collaboration in their network is high. Brokers who arbitrage information across disconnected contacts acquire more knowledge and become more creative when collaboration is low. An analysis of employee...

  2. Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

    Directory of Open Access Journals (Sweden)

    Hongshan Zhao

    2012-05-01

    Full Text Available Short-term solar irradiance forecasting (STSIF is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV, and the Levenberg-Marquardt algorithm (LMA is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS, and the results indicated that the forecast accuracy is obviously improved under variable weather conditions.

  3. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

    Science.gov (United States)

    Sun, Wenqing; Zheng, Bin; Qian, Wei

    2017-10-01

    This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.

  4. The Deep Structure of Organizational Online Networking

    DEFF Research Database (Denmark)

    Trier, Matthias; Richter, Alexander

    2015-01-01

    While research on organizational online networking recently increased significantly, most studies adopt quantitative research designs with a focus on the consequences of social network configurations. Very limited attention is paid to comprehensive theoretical conceptions of the complex phenomenon...... of organizational online networking. We address this gap by adopting a theoretical framework of the deep structure of organizational online networking with a focus on their emerging meaning for the employees. We apply and assess the framework in a qualitative case study of a large-scale implementation...... of a corporate social network site (SNS) in a global organization. We reveal organizational online networking as a multi-dimensional phenomenon with multiplex relationships that are unbalanced, primarily consist of weak ties and are subject to temporal change. Further, we identify discourse drivers...

  5. Modelling the structure of complex networks

    DEFF Research Database (Denmark)

    Herlau, Tue

    networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex......A complex network is a systems in which a discrete set of units interact in a quantifiable manner. Representing systems as complex networks have become increasingly popular in a variety of scientific fields including biology, social sciences and economics. Parallel to this development complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...

  6. Fundamental structures of dynamic social networks

    DEFF Research Database (Denmark)

    Sekara, Vedran; Stopczynski, Arkadiusz; Jørgensen, Sune Lehmann

    2016-01-01

    , and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals...... and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection...... a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework...

  7. Feature Comparison in Structural Health Monitoring of a Vehicle Crane

    Directory of Open Access Journals (Sweden)

    J. Kullaa

    2008-01-01

    Full Text Available Vibration-based structural health monitoring of a vehicle crane was studied. The performance of different features to detect damage was investigated after eliminating the normal operational variations using factor analysis. Using eight accelerometers, ten AR parameters from each record were identified for damage detection. Also transmissibilities between sensors were estimated. Damage was introduced with additional masses at different locations of the structure. All damage cases could be detected from either features using control charts, but transmissibilities proved to be more sensitive to damage than the AR coefficients.

  8. Fractured reservoir discrete feature network technologies. Final report, March 7, 1996 to September 30, 1998

    Energy Technology Data Exchange (ETDEWEB)

    Dershowitz, William S.; Einstein, Herbert H.; LaPoint, Paul R.; Eiben, Thorsten; Wadleigh, Eugene; Ivanova, Violeta

    1998-12-01

    This report summarizes research conducted for the Fractured Reservoir Discrete Feature Network Technologies Project. The five areas studied are development of hierarchical fracture models; fractured reservoir compartmentalization, block size, and tributary volume analysis; development and demonstration of fractured reservoir discrete feature data analysis tools; development of tools for data integration and reservoir simulation through application of discrete feature network technologies for tertiary oil production; quantitative evaluation of the economic value of this analysis approach.

  9. Comparison and validation of community structures in complex networks

    Science.gov (United States)

    Gustafsson, Mika; Hörnquist, Michael; Lombardi, Anna

    2006-07-01

    The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information. Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.

  10. FCAAIS: Anomaly based network intrusion detection through feature correlation analysis and association impact scale

    Directory of Open Access Journals (Sweden)

    V. Jyothsna

    2016-09-01

    Full Text Available Due to the sensitivity of the information required to detect network intrusions efficiently, collecting huge amounts of network transactions is inevitable and the volume and details of network transactions available in recent years have been high. The meta-heuristic anomaly based assessment is vital in an exploratory analysis of intrusion related network transaction data. In order to forecast and deliver predictions about intrusion possibility from the available details of the attributes involved in network transaction. In this regard, a meta-heuristic assessment model called the feature correlation analysis and association impact scale is explored to estimate the degree of intrusion scope threshold from the optimal features of network transaction data available for training. With the motivation gained from the model called “network intrusion detection by feature association impact scale” that was explored in our earlier work, a novel and improved meta-heuristic assessment strategy for intrusion prediction is derived. In this strategy, linear canonical correlation for feature optimization is used and feature association impact scale is explored from the selected optimal features. The experimental result indicates that the feature correlation has a significant impact towards minimizing the computational and time complexity of measuring the feature association impact scale.

  11. Structural measures for multiplex networks.

    Science.gov (United States)

    Battiston, Federico; Nicosia, Vincenzo; Latora, Vito

    2014-03-01

    Many real-world complex systems consist of a set of elementary units connected by relationships of different kinds. All such systems are better described in terms of multiplex networks, where the links at each layer represent a different type of interaction between the same set of nodes rather than in terms of (single-layer) networks. In this paper we present a general framework to describe and study multiplex networks, whose links are either unweighted or weighted. In particular, we propose a series of measures to characterize the multiplexicity of the systems in terms of (i) basic node and link properties such as the node degree, and the edge overlap and reinforcement, (ii) local properties such as the clustering coefficient and the transitivity, and (iii) global properties related to the navigability of the multiplex across the different layers. The measures we introduce are validated on a genuinely multiplex data set of Indonesian terrorists, where information among 78 individuals are recorded with respect to mutual trust, common operations, exchanged communications, and business relationships.

  12. Measure the structure similarity of nodes in complex networks based on relative entropy

    Science.gov (United States)

    Zhang, Qi; Li, Meizhu; Deng, Yong

    2018-02-01

    Similarity of nodes is a basic structure quantification in complex networks. Lots of methods in research on complex networks are based on nodes' similarity such as node's classification, network's community structure detection, network's link prediction and so on. Therefore, how to measure nodes' similarity is an important problem in complex networks. In this paper, a new method is proposed to measure nodes' structure similarity based on relative entropy and each node's local structure. In the new method, each node's structure feature can be quantified as a special kind of information. The quantification of similarity between different pair of nodes can be replaced as the quantification of similarity in structural information. Then relative entropy is used to measure the difference between each pair of nodes' structural information. At last the value of relative entropy between each pair of nodes is used to measure nodes' structure similarity in complex networks. Comparing with existing methods the new method is more accuracy to measure nodes' structure similarity.

  13. Wireless sensor networks for structural health monitoring

    CERN Document Server

    Cao, Jiannong

    2016-01-01

    This brief covers the emerging area of wireless sensor network (WSN)-based structural health monitoring (SHM) systems, and introduces the authors’ WSN-based platform called SenetSHM. It helps the reader differentiate specific requirements of SHM applications from other traditional WSN applications, and demonstrates how these requirements are addressed by using a series of systematic approaches. The brief serves as a practical guide, explaining both the state-of-the-art technologies in domain-specific applications of WSNs, as well as the methodologies used to address the specific requirements for a WSN application. In particular, the brief offers instruction for problem formulation and problem solving based on the authors’ own experiences implementing SenetSHM. Seven concise chapters cover the development of hardware and software design of SenetSHM, as well as in-field experiments conducted while testing the platform. The brief’s exploration of the SenetSHM platform is a valuable feature for civil engine...

  14. Controlling congestion on complex networks: fairness, efficiency and network structure.

    Science.gov (United States)

    Buzna, Ľuboš; Carvalho, Rui

    2017-08-22

    We consider two elementary (max-flow and uniform-flow) and two realistic (max-min fairness and proportional fairness) congestion control schemes, and analyse how the algorithms and network structure affect throughput, the fairness of flow allocation, and the location of bottleneck edges. The more realistic proportional fairness and max-min fairness algorithms have similar throughput, but path flow allocations are more unequal in scale-free than in random regular networks. Scale-free networks have lower throughput than their random regular counterparts in the uniform-flow algorithm, which is favoured in the complex networks literature. We show, however, that this relation is reversed on all other congestion control algorithms for a region of the parameter space given by the degree exponent γ and average degree 〈k〉. Moreover, the uniform-flow algorithm severely underestimates the network throughput of congested networks, and a rich phenomenology of path flow allocations is only present in the more realistic α-fair family of algorithms. Finally, we show that the number of paths passing through an edge characterises the location of a wide range of bottleneck edges in these algorithms. Such identification of bottlenecks could provide a bridge between the two fields of complex networks and congestion control.

  15. Structure and function of complex brain networks

    Science.gov (United States)

    Sporns, Olaf

    2013-01-01

    An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a “rich club,” centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed. PMID:24174898

  16. New damage-sensitive feature for structures with bolted joints

    Science.gov (United States)

    Luo, W. F.; Yu, L.

    2017-05-01

    Compared with riveted and welded joints, bolted joints have advantages of easily dismantled, high load carrying and load-transferring capacity. However, bolted joints are also weaker components of assembled structures. Structural damage detection (SDD) on bolted joints is much required in the field of structural health monitoring (SHM). A new SDD method is proposed for damage identification of structures with bolted joints based on residual error of AR model in time series analysis. Firstly, a new data standardization process is defined to maintain the information of damage location. Then, a new structural damage feature sensitive to structural damage is developed based on the standard deviation of AR model residual errors. To verify the proposed method, a bolted joint structure is designed and fabricated in laboratory, connection damages of structures are simulated by loosening the bolted joints. The acceleration responses of structures with bolted joints under healthy and damage cases are acquired. Finally, the SDD is performed by traditional DSF and the new DSF. The illustrated results show that the proposed method is a hybrid tool for the bolted joint damage detection with the new damage-sensitive feature. In addition, some related issues will be discussed as well.

  17. Real-time object-to-features vectorisation via Siamese neural networks

    Science.gov (United States)

    Fedorenko, Fedor; Usilin, Sergey

    2017-03-01

    Object-to-features vectorisation is a hard problem to solve for objects that can be hard to distinguish. Siamese and Triplet neural networks are one of the more recent tools used for such task. However, most networks used are very deep networks that prove to be hard to compute in the Internet of Things setting. In this paper, a computationally efficient neural network is proposed for real-time object-to-features vectorisation into a Euclidean metric space. We use L2 distance to reflect feature vector similarity during both training and testing. In this way, feature vectors we develop can be easily classified using K-Nearest Neighbours classifier. Such approach can be used to train networks to vectorise such "problematic" objects like images of human faces, keypoint image patches, like keypoints on Arctic maps and surrounding marine areas.

  18. Structural Connectivity Networks of Transgender People

    NARCIS (Netherlands)

    Hahn, Andreas; Kranz, Georg S; Küblböck, Martin; Kaufmann, Ulrike; Ganger, Sebastian; Hummer, Allan; Seiger, Rene; Spies, Marie; Winkler, Dietmar; Kasper, Siegfried; Windischberger, Christian; Swaab, Dick F; Lanzenberger, Rupert

    2015-01-01

    Although previous investigations of transsexual people have focused on regional brain alterations, evaluations on a network level, especially those structural in nature, are largely missing. Therefore, we investigated the structural connectome of 23 female-to-male (FtM) and 21 male-to-female (MtF)

  19. Analysis of Conserved Structural Features of Selenoprotein K | Al ...

    African Journals Online (AJOL)

    Selenium plays important roles in human health and these roles may be exerted through its presence in selenoproteins. Among the 25 selenoproteins in human is selenoprotein K (SelK) whose exact function is still unclear. Here, we investigated the conserved structural features of SelK using bioinformatics as an approach ...

  20. Composition and Structure of a Large Online Social Network in the Netherlands

    Science.gov (United States)

    Corten, Rense

    2012-01-01

    Limitations in data collection have long been an obstacle in research on friendship networks. Most earlier studies use either a sample of ego-networks, or complete network data on a relatively small group (e.g., a single organization). The rise of online social networking services such as Friendster and Facebook, however, provides researchers with opportunities to study friendship networks on a much larger scale. This study uses complete network data from Hyves, a popular online social networking service in the Netherlands, comprising over eight million members and over 400 million online friendship relations. In the first study of its kind for the Netherlands, I examine the structure of this network in terms of the degree distribution, characteristic path length, clustering, and degree assortativity. Results indicate that this network shares features of other large complex networks, but also deviates in other respects. In addition, a comparison with other online social networks shows that these networks show remarkable similarities. PMID:22523557

  1. Composition and structure of a large online social network in The Netherlands.

    Directory of Open Access Journals (Sweden)

    Rense Corten

    Full Text Available Limitations in data collection have long been an obstacle in research on friendship networks. Most earlier studies use either a sample of ego-networks, or complete network data on a relatively small group (e.g., a single organization. The rise of online social networking services such as Friendster and Facebook, however, provides researchers with opportunities to study friendship networks on a much larger scale. This study uses complete network data from Hyves, a popular online social networking service in The Netherlands, comprising over eight million members and over 400 million online friendship relations. In the first study of its kind for The Netherlands, I examine the structure of this network in terms of the degree distribution, characteristic path length, clustering, and degree assortativity. Results indicate that this network shares features of other large complex networks, but also deviates in other respects. In addition, a comparison with other online social networks shows that these networks show remarkable similarities.

  2. Structure formation in active networks

    CERN Document Server

    Köhler, Simone; Bausch, Andreas R

    2011-01-01

    Structure formation and constant reorganization of the actin cytoskeleton are key requirements for the function of living cells. Here we show that a minimal reconstituted system consisting of actin filaments, crosslinking molecules and molecular-motor filaments exhibits a generic mechanism of structure formation, characterized by a broad distribution of cluster sizes. We demonstrate that the growth of the structures depends on the intricate balance between crosslinker-induced stabilization and simultaneous destabilization by molecular motors, a mechanism analogous to nucleation and growth in passive systems. We also show that the intricate interplay between force generation, coarsening and connectivity is responsible for the highly dynamic process of structure formation in this heterogeneous active gel, and that these competing mechanisms result in anomalous transport, reminiscent of intracellular dynamics.

  3. Multiplex networks in metropolitan areas: generic features and local effects

    CERN Document Server

    Strano, Emanuele; Dobson, Simon; Barthelemy, Marc

    2015-01-01

    Most large cities are spanned by more than one transportation system. These different modes of transport have usually been studied separately: it is however important to understand the impact on urban systems of the coupling between them and we report in this paper an empirical analysis of the coupling between the street network and the subway for the two large metropolitan areas of London and New York. We observe a similar behaviour for network quantities related to quickest paths suggesting the existence of generic mechanisms operating beyond the local peculiarities of the specific cities studied. An analysis of the betweenness centrality distribution shows that the introduction of underground networks operate as a decentralising force creating congestions in places located at the end of underground lines. Also, we find that increasing the speed of subways is not always beneficial and may lead to unwanted uneven spatial distributions of accessibility. In fact, for London -- but not for New York -- there is ...

  4. Maximum entropy methods for extracting the learned features of deep neural networks.

    Science.gov (United States)

    Finnegan, Alex; Song, Jun S

    2017-10-01

    New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

  5. Identifying community structure in complex networks

    Science.gov (United States)

    Shao, Chenxi; Duan, Yubing

    2015-07-01

    A wide variety of applications could be formulated to resolve the problem of finding all communities from a given network, ranging from social and biological network analysis to web mining and searching. In this study, we propose the concept of virtual attractive strength between each pair of node in networks, and then give the definition of community structure based on the proposed attractive strength. Furthermore, we present a community detection method by moving vertices to the clusters that produce the largest attractive strengths to them until the division of network reaches unchanged. Experimental results on synthetic and real networks indicate that the proposed approach has favorite effectiveness and fast convergence speed, which provides an efficient method for exploring and analyzing complex systems.

  6. Surface structure feature matching algorithm for cardiac motion estimation.

    Science.gov (United States)

    Zhang, Zhengrui; Yang, Xuan; Tan, Cong; Guo, Wei; Chen, Guoliang

    2017-12-20

    Cardiac diseases represent the leading cause of sudden death worldwide. During the development of cardiac diseases, the left ventricle (LV) changes obviously in structure and function. LV motion estimation plays an important role for diagnosis and treatment of cardiac diseases. To estimate LV motion accurately for cine magnetic resonance (MR) cardiac images, we develop an algorithm by combining point set matching with surface structure features of myocardium. The structure features of myocardial wall are described by estimating the normal directions of points locating on the myocardium contours using an approximation approach. The Gaussian mixture model (GMM) of structure features is used to represent LV structure feature distribution. A new cost function is defined to represent the differences between two Gaussian mixture models, which are the GMM of structure features and the GMM of positions of two point sets. To optimize the cost function, its gradient is derived to use the Quasi-Newton (QN). Furthermore, to resolve the dis-convergence issue of Quasi-Newton for high-dimensional parameter space, Stochastic Gradient Descent (SGD) is used and SGD gradient is derived. Finally, the new cost function is solved by optimization combining SGD with QN. With the closed form expression of gradient, this paper provided a computationally efficient registration algorithm. Three public datasets are employed to verify the performance of our algorithm, including cardiac MR image sequences acquired from 33 subjects, 14 inter-subject heart cases, and the data obtained in MICCAI 2009s 3D Segmentation Challenge for Clinical Applications. We compare our results with those of the other point set registration methods for LV motion estimation. The obtained results demonstrate that our algorithm shows inherent statistical robustness, due to the combination of SGD and Quasi-Newton optimization. Furthermore, our method is shown to outperform other point set matching methods in the

  7. Integrated visual analysis of protein structures, sequences, and feature data.

    Science.gov (United States)

    Stolte, Christian; Sabir, Kenneth S; Heinrich, Julian; Hammang, Christopher J; Schafferhans, Andrea; O'Donoghue, Seán I

    2015-01-01

    To understand the molecular mechanisms that give rise to a protein's function, biologists often need to (i) find and access all related atomic-resolution 3D structures, and (ii) map sequence-based features (e.g., domains, single-nucleotide polymorphisms, post-translational modifications) onto these structures. To streamline these processes we recently developed Aquaria, a resource offering unprecedented access to protein structure information based on an all-against-all comparison of SwissProt and PDB sequences. In this work, we provide a requirements analysis for several frequently occuring tasks in molecular biology and describe how design choices in Aquaria meet these requirements. Finally, we show how the interface can be used to explore features of a protein and gain biologically meaningful insights in two case studies conducted by domain experts. The user interface design of Aquaria enables biologists to gain unprecedented access to molecular structures and simplifies the generation of insight. The tasks involved in mapping sequence features onto structures can be conducted easier and faster using Aquaria.

  8. Multiplex networks in metropolitan areas: generic features and local effects.

    Science.gov (United States)

    Strano, Emanuele; Shai, Saray; Dobson, Simon; Barthelemy, Marc

    2015-10-06

    Most large cities are spanned by more than one transportation system. These different modes of transport have usually been studied separately: it is however important to understand the impact on urban systems of coupling different modes and we report in this paper an empirical analysis of the coupling between the street network and the subway for the two large metropolitan areas of London and New York. We observe a similar behaviour for network quantities related to quickest paths suggesting the existence of generic mechanisms operating beyond the local peculiarities of the specific cities studied. An analysis of the betweenness centrality distribution shows that the introduction of underground networks operate as a decentralizing force creating congestion in places located at the end of underground lines. Also, we find that increasing the speed of subways is not always beneficial and may lead to unwanted uneven spatial distributions of accessibility. In fact, for London—but not for New York—there is an optimal subway speed in terms of global congestion. These results show that it is crucial to consider the full, multimodal, multilayer network aspects of transportation systems in order to understand the behaviour of cities and to avoid possible negative side-effects of urban planning decisions. © 2015 The Author(s).

  9. Community Structure in Online Collegiate Social Networks

    Science.gov (United States)

    Traud, Amanda; Kelsic, Eric; Mucha, Peter; Porter, Mason

    2009-03-01

    Online social networking sites have become increasingly popular with college students. The networks we studied are defined through ``friendships'' indicated by Facebook users from UNC, Oklahoma, Caltech, Georgetown, and Princeton. We apply the tools of network science to study the Facebook networks from these five different universities at a single point in time. We investigate each single-institution network's community structure, which we obtain through partitioning the graph using an eigenvector method. We use both graphical and quantitative tools, including pair-counting methods, which we interpret through statistical analysis and permutation tests to measure the correlations between the network communities and a set of characteristics given by each user (residence, class year, major, and high school). We also analyze the single gender subsets of these networks, and the impact of missing demographical data. Our study allows us to compare the online social networks for the five schools as well as infer differences in offline social interactions. At the schools studied, we were able to define which characteristics of the Facebook users correlate best with friendships.

  10. Genotype 1 hepatitis C virus envelope features that determine antiviral response assessed through optimal covariance networks.

    Directory of Open Access Journals (Sweden)

    John M Murray

    Full Text Available The poor response to the combined antiviral therapy of pegylated alfa-interferon and ribavarin for hepatitis C virus (HCV infection may be linked to mutations in the viral envelope gene E1E2 (env, which can result in escape from the immune response and higher efficacy of viral entry. Mutations that result in failure of therapy most likely require compensatory mutations to achieve sufficient change in envelope structure and function. Compensatory mutations were investigated by determining positions in the E1E2 gene where amino acids (aa covaried across groups of individuals. We assessed networks of covarying positions in E1E2 sequences that differentiated sustained virological response (SVR from non-response (NR in 43 genotype 1a (17 SVR, and 49 genotype 1b (25 SVR chronically HCV-infected individuals. Binary integer programming over covariance networks was used to extract aa combinations that differed between response groups. Genotype 1a E1E2 sequences exhibited higher degrees of covariance and clustered into 3 main groups while 1b sequences exhibited no clustering. Between 5 and 9 aa pairs were required to separate SVR from NR in each genotype. aa in hypervariable region 1 were 6 times more likely than chance to occur in the optimal networks. The pair 531-626 (EI appeared frequently in the optimal networks and was present in 6 of 9 NR in one of the 1a clusters. The most frequent pairs representing SVR were 431-481 (EE, 500-522 (QA in 1a, and 407-434 (AQ in 1b. Optimal networks based on covarying aa pairs in HCV envelope can indicate features that are associated with failure or success to antiviral therapy.

  11. Structure and Controls of the Global Virtual Water Trade Network

    Science.gov (United States)

    Suweis, S. S.

    2011-12-01

    Recurrent or ephemeral water shortages are a crucial global challenge, in particular because of their impacts on food production. The global character of this challenge is reflected in the trade among nations of virtual water, i.e. the amount of water used to produce a given commodity. We build, analyze and model the network describing the transfer of virtual water between world nations for staple food products. We find that all the key features of the network are well described by a model, the fitness model, that reproduces both the topological and weighted properties of the global virtual water trade network, by assuming as sole controls each country's gross domestic product and yearly rainfall on agricultural areas. We capture and quantitatively describe the high degree of globalization of water trade and show that a small group of nations play a key role in the connectivity of the network and in the global redistribution of virtual water. Finally, we illustrate examples of prediction of the structure of the network under future political, economic and climatic scenarios, suggesting that the crucial importance of the countries that trade large volumes of water will be strengthened. Our results show the importance of incorporating a network framework in the study of virtual water trades and provide a model to study the structure and resilience of the GVWTN under future scenarios for social, economic and climate change.

  12. Rumor propagation on networks with community structure

    Science.gov (United States)

    Zhang, Ruixia; Li, Deyu

    2017-10-01

    In this paper, based on growth and preferential attachment mechanism, we give a network generation model aiming at generating networks with community structure. There are three characteristics for the networks generated by the generation model. The first is that the community sizes can be nonuniform. The second is that there are bridge hubs in each community. The third is that the strength of community structure is adjustable. Next, we investigate rumor propagation behavior on the generated networks by performing Monte Carlo simulations to reveal the influence of bridge hubs, nonuniformity of community sizes and the strength of community structure on the dynamic behavior of the rumor propagation. We find that bridge hubs have outstanding performance in propagation speed and propagation size, and larger modularity can reduce rumor propagation. Furthermore, when the decay rate of rumor spreading β is large, the final density of the stiflers is larger if the rumor originates in larger community. Additionally, when on networks with different strengths of community structure, rumor propagation exhibits greater difference in the density of stiflers and in the peak prevalence if the decay rate β is larger.

  13. Structural systems identification of genetic regulatory networks.

    Science.gov (United States)

    Xiong, Hao; Choe, Yoonsuck

    2008-02-15

    Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit. In this report, we extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles. We applied our method to synthetic data and to SOS data and showed that our method significantly outperforms the regular EM without structural constraints. The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his website, http://www.che.udel.edu/systems/people/zak.

  14. Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features.

    Science.gov (United States)

    Singh, Onkar; Su, Emily Chia-Yu

    2016-12-23

    The human immunodeficiency virus type 1 (HIV-1) aspartic protease is an important enzyme owing to its imperative part in viral development and a causative agent of deadliest disease known as acquired immune deficiency syndrome (AIDS). Development of HIV-1 protease inhibitors can help understand the specificity of substrates which can restrain the replication of HIV-1, thus antagonize AIDS. However, experimental methods in identification of HIV-1 protease cleavage sites are generally time-consuming and labor-intensive. Therefore, using computational methods to predict cleavage sites has become highly desirable. In this study, we propose a prediction method in which sequence, structural, and physicochemical features are incorporated in various machine learning algorithms. Then, a bidirectional stepwise selection algorithm is incorporated in feature selection to identify discriminative features. Further, only the selected features are calculated by various encoding schemes and used as input for decision trees, logistic regression, and artificial neural networks. Moreover, a more rigorous three-way data split procedure is applied to evaluate the objective performance of cleavage site prediction. Four benchmark datasets collected from previous studies are used to evaluate the predictive performance. Experiment results showed that combinations of sequence, structure, and physicochemical features performed better than single feature type for identification of HIV-1 protease cleavage sites. In addition, incorporation of stepwise feature selection is effective to identify interpretable biological features to depict specificity of the substrates. Moreover, artificial neural networks perform significantly better than the other two classifiers. Finally, the proposed method achieved 80.0% ~ 97.4% in accuracy and 0.815 ~ 0.995 evaluated by independent test sets in a three-way data split procedure.

  15. Prediction of protein secondary structure using probability based features and a hybrid system.

    Science.gov (United States)

    Ghanty, Pradip; Pal, Nikhil R; Mudi, Rajani K

    2013-10-01

    In this paper, we propose some co-occurrence probability-based features for prediction of protein secondary structure. The features are extracted using occurrence/nonoccurrence of secondary structures in the protein sequences. We explore two types of features: position-specific (based on position of amino acid on fragments of protein sequences) as well as position-independent (independent of amino acid position on fragments of protein sequences). We use a hybrid system, NEUROSVM, consisting of neural networks and support vector machines for classification of secondary structures. We propose two schemes NSVMps and NSVM for protein secondary structure prediction. The NSVMps uses position-specific probability-based features and NEUROSVM classifier whereas NSVM uses the same classifier with position-independent probability-based features. The proposed method falls in the single-sequence category of methods because it does not use any sequence profile information such as position specific scoring matrices (PSSM) derived from PSI-BLAST. Two widely used datasets RS126 and CB513 are used in the experiments. The results obtained using the proposed features and NEUROSVM classifier are better than most of the existing single-sequence prediction methods. Most importantly, the results using NSVMps that are obtained using lower dimensional features, are comparable to those by other existing methods. The NSVMps and NSVM are finally tested on target proteins of the critical assessment of protein structure prediction experiment-9 (CASP9). A larger dataset is used to compare the performance of the proposed methods with that of two recent single-sequence prediction methods. We also investigate the impact of presence of different amino acid residues (in protein sequences) that are responsible for the formation of different secondary structures.

  16. Structural health monitoring using wireless sensor networks

    Science.gov (United States)

    Sreevallabhan, K.; Nikhil Chand, B.; Ramasamy, Sudha

    2017-11-01

    Monitoring and analysing health of large structures like bridges, dams, buildings and heavy machinery is important for safety, economical, operational, making prior protective measures, and repair and maintenance point of view. In recent years there is growing demand for such larger structures which in turn make people focus more on safety. By using Microelectromechanical Systems (MEMS) Accelerometer we can perform Structural Health Monitoring by studying the dynamic response through measure of ambient vibrations and strong motion of such structures. By using Wireless Sensor Networks (WSN) we can embed these sensors in wireless networks which helps us to transmit data wirelessly thus we can measure the data wirelessly at any remote location. This in turn reduces heavy wiring which is a cost effective as well as time consuming process to lay those wires. In this paper we developed WSN based MEMS-accelerometer for Structural to test the results in the railway bridge near VIT University, Vellore campus.

  17. Emergence of Slow-Switching Assemblies in Structured Neuronal Networks.

    Directory of Open Access Journals (Sweden)

    Michael T Schaub

    2015-07-01

    Full Text Available Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal networks is a key step to advance our understanding of neuronal information processing. Here we investigate how particular features of network connectivity underpin the propensity of neural networks to generate slow-switching assembly (SSA dynamics, i.e., sustained epochs of increased firing within assemblies of neurons which transition slowly between different assemblies throughout the network. We show that the emergence of SSA activity is linked to spectral properties of the asymmetric synaptic weight matrix. In particular, the leading eigenvalues that dictate the slow dynamics exhibit a gap with respect to the bulk of the spectrum, and the associated Schur vectors exhibit a measure of block-localization on groups of neurons, thus resulting in coherent dynamical activity on those groups. Through simple rate models, we gain analytical understanding of the origin and importance of the spectral gap, and use these insights to develop new network topologies with alternative connectivity paradigms which also display SSA activity. Specifically, SSA dynamics involving excitatory and inhibitory neurons can be achieved by modifying the connectivity patterns between both types of neurons. We also show that SSA activity can occur at multiple timescales reflecting a hierarchy in the connectivity, and demonstrate the emergence of SSA in small-world like networks. Our work provides a step towards understanding how network structure (uncovered through advancements in neuroanatomy and connectomics can impact on spatio-temporal neural activity and constrain the resulting dynamics.

  18. Emergence of Slow-Switching Assemblies in Structured Neuronal Networks.

    Science.gov (United States)

    Schaub, Michael T; Billeh, Yazan N; Anastassiou, Costas A; Koch, Christof; Barahona, Mauricio

    2015-07-01

    Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal networks is a key step to advance our understanding of neuronal information processing. Here we investigate how particular features of network connectivity underpin the propensity of neural networks to generate slow-switching assembly (SSA) dynamics, i.e., sustained epochs of increased firing within assemblies of neurons which transition slowly between different assemblies throughout the network. We show that the emergence of SSA activity is linked to spectral properties of the asymmetric synaptic weight matrix. In particular, the leading eigenvalues that dictate the slow dynamics exhibit a gap with respect to the bulk of the spectrum, and the associated Schur vectors exhibit a measure of block-localization on groups of neurons, thus resulting in coherent dynamical activity on those groups. Through simple rate models, we gain analytical understanding of the origin and importance of the spectral gap, and use these insights to develop new network topologies with alternative connectivity paradigms which also display SSA activity. Specifically, SSA dynamics involving excitatory and inhibitory neurons can be achieved by modifying the connectivity patterns between both types of neurons. We also show that SSA activity can occur at multiple timescales reflecting a hierarchy in the connectivity, and demonstrate the emergence of SSA in small-world like networks. Our work provides a step towards understanding how network structure (uncovered through advancements in neuroanatomy and connectomics) can impact on spatio-temporal neural activity and constrain the resulting dynamics.

  19. Online social network size is reflected in human brain structure.

    Science.gov (United States)

    Kanai, R; Bahrami, B; Roylance, R; Rees, G

    2012-04-07

    The increasing ubiquity of web-based social networking services is a striking feature of modern human society. The degree to which individuals participate in these networks varies substantially for reasons that are unclear. Here, we show a biological basis for such variability by demonstrating that quantitative variation in the number of friends an individual declares on a web-based social networking service reliably predicted grey matter density in the right superior temporal sulcus, left middle temporal gyrus and entorhinal cortex. Such regions have been previously implicated in social perception and associative memory, respectively. We further show that variability in the size of such online friendship networks was significantly correlated with the size of more intimate real-world social groups. However, the brain regions we identified were specifically associated with online social network size, whereas the grey matter density of the amygdala was correlated both with online and real-world social network sizes. Taken together, our findings demonstrate that the size of an individual's online social network is closely linked to focal brain structure implicated in social cognition.

  20. Subsidence feature discrimination using deep convolutional neral networks in synthetic aperture radar imagery

    CSIR Research Space (South Africa)

    Schwegmann, Colin P

    2017-07-01

    Full Text Available International Geoscience and Remote Sensing Symposium (IGARSS), 23-28 July 2017, Fort Worth, TX, USA SUBSIDENCE FEATURE DISCRIMINATION USING DEEP CONVOLUTIONAL NEURAL NETWORKS IN SYNTHETIC APERTURE RADAR IMAGERY Schwegmann, Colin P Kleynhans, Waldo...

  1. Feature Acquisition and Analysis for Facial Expression Recognition Using Convolutional Neural Networks

    National Research Council Canada - National Science Library

    Taiki Nishime; Satoshi Endo; Naruaki Toma; Koji Yamada; Yuhei Akamine

    2017-01-01

    .... Therefore, it is difficult to evaluate the reliability of the result from recognition accuracy alone, and the analysis for explaining the result and feature learned by Convolutional Neural Networks (CNN...

  2. ECG Identification System Using Neural Network with Global and Local Features

    Science.gov (United States)

    Tseng, Kuo-Kun; Lee, Dachao; Chen, Charles

    2016-01-01

    This paper proposes a human identification system via extracted electrocardiogram (ECG) signals. Two hierarchical classification structures based on global shape feature and local statistical feature is used to extract ECG signals. Global shape feature represents the outline information of ECG signals and local statistical feature extracts the…

  3. Validation of protein structure models using network similarity score.

    Science.gov (United States)

    Ghosh, Sambit; Gadiyaram, Vasundhara; Vishveshwara, Saraswathi

    2017-09-01

    Accurate structural validation of proteins is of extreme importance in studies like protein structure prediction, analysis of molecular dynamic simulation trajectories and finding subtle changes in very similar structures. The benchmarks for today's structure validation are scoring methods like global distance test-total structure (GDT-TS), TM-score and root mean square deviations (RMSD). However, there is a lack of methods that look at both the protein backbone and side-chain structures at the global connectivity level and provide information about the differences in connectivity. To address this gap, a graph spectral based method (NSS-network similarity score) which has been recently developed to rigorously compare networks in diverse fields, is adopted to compare protein structures both at the backbone and at the side-chain noncovalent connectivity levels. In this study, we validate the performance of NSS by investigating protein structures from X-ray structures, modeling (including CASP models), and molecular dynamics simulations. Further, we systematically identify the local and the global regions of the structures contributing to the difference in NSS, through the components of the score, a feature unique to this spectral based scoring scheme. It is demonstrated that the method can quantify subtle differences in connectivity compared to a reference protein structure and can form a robust basis for protein structure comparison. Additionally, we have also introduced a network-based method to analyze fluctuations in side chain interactions (edge-weights) in an ensemble of structures, which can be an useful tool for the analysis of MD trajectories. © 2017 Wiley Periodicals, Inc.

  4. Robustness in Weighted Networks with Cluster Structure

    Directory of Open Access Journals (Sweden)

    Yi Zheng

    2014-01-01

    Full Text Available The vulnerability of complex systems induced by cascade failures revealed the comprehensive interaction of dynamics with network structure. The effect on cascade failures induced by cluster structure was investigated on three networks, small-world, scale-free, and module networks, of which the clustering coefficient is controllable by the random walk method. After analyzing the shifting process of load, we found that the betweenness centrality and the cluster structure play an important role in cascading model. Focusing on this point, properties of cascading failures were studied on model networks with adjustable clustering coefficient and fixed degree distribution. In the proposed weighting strategy, the path length of an edge is designed as the product of the clustering coefficient of its end nodes, and then the modified betweenness centrality of the edge is calculated and applied in cascade model as its weights. The optimal region of the weighting scheme and the size of the survival components were investigated by simulating the edge removing attack, under the rule of local redistribution based on edge weights. We found that the weighting scheme based on the modified betweenness centrality makes all three networks have better robustness against edge attack than the one based on the original betweenness centrality.

  5. The trade-off between wiring cost and network topology in white matter structural networks in health and migraine.

    Science.gov (United States)

    Liu, Jixin; Zhao, Ling; Nan, Jiaofen; Li, Guoying; Xiong, Shiwei; von Deneen, Karen M; Gong, Qiyong; Liang, Fanrong; Qin, Wei; Tian, Jie

    2013-10-01

    The human brain organization of cortical networks has optimized trade-off architecture for the economical minimization of connection distance and maximizing valuable topological properties; however, whether this network configuration is disrupted in chronic migraine remains unknown. Here, employing the diffusion tensor imaging and graph theory approaches to construct white matter networks in 26 patients with migraine (PM) and 26 gender-matched healthy controls (HC), we investigated relationships between structural connectivity, cortical network architecture and anatomical distance in the two groups separately. Compared with the HC group, the patients showed longer global distance connection in PM, with proportionally less short-distance and more medium-distance; correspondingly, the patients showed abnormal global topology in their structural networks, mainly presented as a higher clustering coefficient. Moreover, the abnormal association between these two network features was also found. Intriguingly, the network measure that combined the nodal anatomical distance and network topology could distinguish PM from HC with high accuracy of 90.4%. We also demonstrated a high reproducibility of our findings across different parcellation schemes. Our results demonstrated that long-term migraine may result in a abnormal optimization of a trade-off between wiring cost and network topology in white matter structural networks and highlights the potential for combining spatial and topological aspects as a network marker, which may provide valuable insights into the understanding of brain network reorganization that could be attributed to the underlying pathophysiology resulting from migraine. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

  6. Gait features analysis using artificial neural networks - testing the footwear effect.

    Science.gov (United States)

    Wang, Jikun; Zielińska, Teresa

    2017-01-01

    The aim of this paper is to provide the methods for automatic detection of the difference in gait features depending on a footwear. Artificial neural networks were applied in the study. The gait data were recorded during the walk with different footwear for testing and validation of the proposed method. The gait properties were analyzed considering EMG (electromyography) signals and using two types of artificial neural networks: the learning vector quantization (LVQ) classifying network, and the clustering competitive network. Obtained classification and clustering results were discussed. For comparative studies, velocities of the leg joint trajectories, and accelerations were used. The features indicated by neural networks were compared with the conclusions formulated analyzing the above mentioned trajectories for ankle and knee joints. The matching between experimentally recorded joint trajectories and the results given by neural networks was studied. It was indicated what muscles are most influenced by the footwear, the relation between the footwear type and the muscles work was concluded.

  7. A Survey of Neural Network Techniques for Feature Extraction from Text

    OpenAIRE

    John, Vineet

    2017-01-01

    This paper aims to catalyze the discussions about text feature extraction techniques using neural network architectures. The research questions discussed in the paper focus on the state-of-the-art neural network techniques that have proven to be useful tools for language processing, language generation, text classification and other computational linguistics tasks.

  8. Daily temporal structure in African savanna flower visitation networks and consequences for network sampling.

    Science.gov (United States)

    Baldock, Katherine C R; Memmott, Jane; Ruiz-Guajardo, Juan Carlos; Roze, Denis; Stone, Graham N

    2011-03-01

    Ecological interaction networks are a valuable approach to understanding plant-pollinator interactions at the community level. Highly structured daily activity patterns are a feature of the biology of many flower visitors, particularly provisioning female bees, which often visit different floral sources at different times. Such temporal structure implies that presence/absence and relative abundance of specific flower-visitor interactions (links) in interaction networks may be highly sensitive to the daily timing of data collection. Further, relative timing of interactions is central to their possible role in competition or facilitation of seed set among coflowering plants sharing pollinators. To date, however, no study has examined the network impacts of daily temporal variation in visitor activity at a community scale. Here we use temporally structured sampling to examine the consequences of daily activity patterns upon network properties using fully quantified flower-visitor interaction data for a Kenyan savanna habitat. Interactions were sampled at four sequential three-hour time intervals between 06:00 and 18:00, across multiple seasonal time points for two sampling sites. In all data sets the richness and relative abundance of links depended critically on when during the day visitation was observed. Permutation-based null modeling revealed significant temporal structure across daily time intervals at three of the four seasonal time points, driven primarily by patterns in bee activity. This sensitivity of network structure shows the need to consider daily time in network sampling design, both to maximize the probability of sampling links relevant to plant reproductive success and to facilitate appropriate interpretation of interspecific relationships. Our data also suggest that daily structuring at a community level could reduce indirect competitive interactions when coflowering plants share pollinators, as is commonly observed during flowering in highly

  9. Product Cost Management Structures: a review and neural network modelling

    Directory of Open Access Journals (Sweden)

    P. Jha

    2003-11-01

    Full Text Available This paper reviews the growth of approaches in product costing and draws synergies with information management and resource planning systems, to investigate potential application of state of the art modelling techniques of neural networks. Increasing demands on costing systems to serve multiple decision-making objectives, have made it essential to use better techniques for analysis of available data. This need is highlighted in the paper. The approach of neural networks, which have several analogous facets to complement and aid the information demands of modern product costing, Enterprise Resource Planning (ERP structures and the dominant-computing environment (for information management in the object oriented paradigm form the domain for investigation. Simulated data is used in neural network applications across activities that consume resources and deliver products, to generate information for monitoring and control decisions. The results in application for feature extraction and variation detection and their implications are presented in the paper.

  10. An Evaluation Framework for Large-Scale Network Structures

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Knudsen, Thomas Phillip; Madsen, Ole Brun

    2004-01-01

    An evaluation framework for large-scale network structures is presented, which facilitates evaluations and comparisons of different physical network structures. A number of quantitative and qualitative parameters are presented, and their importance to networks discussed. Choosing a network...... is closed by an example of how the framework can be used. The framework supports network planners in decision-making and researchers in evaluation and development of network structures....

  11. Critical Features of Fragment Libraries for Protein Structure Prediction

    Science.gov (United States)

    dos Santos, Karina Baptista

    2017-01-01

    The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction. PMID:28085928

  12. Critical Features of Fragment Libraries for Protein Structure Prediction.

    Science.gov (United States)

    Trevizani, Raphael; Custódio, Fábio Lima; Dos Santos, Karina Baptista; Dardenne, Laurent Emmanuel

    2017-01-01

    The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction.

  13. THE GOVERNANCE STRUCTURE OF COOPERATIVE NETWORKS

    National Research Council Canada - National Science Library

    Rosileia Milagres

    2014-01-01

    .... The analysis shows that the governance structure is influenced by the objectives established, the partners' experience, the types of knowledge and the context where network is inserted. The case highlights the importance of learning during the process, but, although present, it can be negatively influenced by the context and the possibility of future partnerships.

  14. Social Network Structures among Groundnut Farmers

    Science.gov (United States)

    Thuo, Mary; Bell, Alexandra A.; Bravo-Ureta, Boris E.; Okello, David K.; Okoko, Evelyn Nasambu; Kidula, Nelson L.; Deom, C. Michael; Puppala, Naveen

    2013-01-01

    Purpose: Groundnut farmers in East Africa have experienced declines in production despite research and extension efforts to increase productivity. This study examined how social network structures related to acquisition of information about new seed varieties and productivity among groundnut farmers in Uganda and Kenya.…

  15. Structural network efficiency predicts conversion to dementia

    NARCIS (Netherlands)

    Tuladhar, A.; van Uden, I.W.M.; Rutten-Jacobs, L.C.A.; van der Holst, H.; van Norden, A.; de Laat, K.; Dijk, E.; Claassen, J.A.H.R.; Kessels, R.P.C.; Markus, H.S.; Norris, David Gordon; de Leeuw, F.E.

    2016-01-01

    Objective: To examine whether structural network connectivity at baseline predicts incident all-cause dementia in a prospective hospital-based cohort of elderly participants with MRI evidence of small vessel disease (SVD). Methods: A total of 436 participants from the Radboud University Nijmegen

  16. Structures and Statistics of Citation Networks

    Science.gov (United States)

    2011-05-01

    assignment procedure ( QAP ) (14) and its regression counterpart MRQAP (15) have been used to detect structural significance and compare networks in...Correcting Codes. Hamming, R.W. 2, s.l. : Bell System Technical Journal, 1950, Vol. 29, pp. 147--160. 14. QAP Partialling as a Test of Spuriousness* 1

  17. Feature selection for Bayesian network classifiers using the MDL-FS score

    NARCIS (Netherlands)

    Drugan, Madalina M.; Wiering, Marco A.

    When constructing a Bayesian network classifier from data, the more or less redundant features included in a dataset may bias the classifier and as a consequence may result in a relatively poor classification accuracy. In this paper, we study the problem of selecting appropriate subsets of features

  18. Classification of complex networks based on similarity of topological network features

    Science.gov (United States)

    Attar, Niousha; Aliakbary, Sadegh

    2017-09-01

    Over the past few decades, networks have been widely used to model real-world phenomena. Real-world networks exhibit nontrivial topological characteristics and therefore, many network models are proposed in the literature for generating graphs that are similar to real networks. Network models reproduce nontrivial properties such as long-tail degree distributions or high clustering coefficients. In this context, we encounter the problem of selecting the network model that best fits a given real-world network. The need for a model selection method reveals the network classification problem, in which a target-network is classified into one of the candidate network models. In this paper, we propose a novel network classification method which is independent of the network size and employs an alignment-free metric of network comparison. The proposed method is based on supervised machine learning algorithms and utilizes the topological similarities of networks for the classification task. The experiments show that the proposed method outperforms state-of-the-art methods with respect to classification accuracy, time efficiency, and robustness to noise.

  19. A Feature Selection Method for Large-Scale Network Traffic Classification Based on Spark

    Directory of Open Access Journals (Sweden)

    Yong Wang

    2016-02-01

    Full Text Available Currently, with the rapid increasing of data scales in network traffic classifications, how to select traffic features efficiently is becoming a big challenge. Although a number of traditional feature selection methods using the Hadoop-MapReduce framework have been proposed, the execution time was still unsatisfactory with numeral iterative computations during the processing. To address this issue, an efficient feature selection method for network traffic based on a new parallel computing framework called Spark is proposed in this paper. In our approach, the complete feature set is firstly preprocessed based on Fisher score, and a sequential forward search strategy is employed for subsets. The optimal feature subset is then selected using the continuous iterations of the Spark computing framework. The implementation demonstrates that, on the precondition of keeping the classification accuracy, our method reduces the time cost of modeling and classification, and improves the execution efficiency of feature selection significantly.

  20. Automated analysis of Physarum network structure and dynamics

    Science.gov (United States)

    Fricker, Mark D.; Akita, Dai; Heaton, Luke LM; Jones, Nick; Obara, Boguslaw; Nakagaki, Toshiyuki

    2017-06-01

    We evaluate different ridge-enhancement and segmentation methods to automatically extract the network architecture from time-series of Physarum plasmodia withdrawing from an arena via a single exit. Whilst all methods gave reasonable results, judged by precision-recall analysis against a ground-truth skeleton, the mean phase angle (Feature Type) from intensity-independent, phase-congruency edge enhancement and watershed segmentation was the most robust to variation in threshold parameters. The resultant single pixel-wide segmented skeleton was converted to a graph representation as a set of weighted adjacency matrices containing the physical dimensions of each vein, and the inter-vein regions. We encapsulate the complete image processing and network analysis pipeline in a downloadable software package, and provide an extensive set of metrics that characterise the network structure, including hierarchical loop decomposition to analyse the nested structure of the developing network. In addition, the change in volume for each vein and intervening plasmodial sheet was used to predict the net flow across the network. The scaling relationships between predicted current, speed and shear force with vein radius were consistent with predictions from Murray’s law. This work was presented at PhysNet 2015.

  1. Probabilistic diffusion tractography reveals improvement of structural network in musicians.

    Science.gov (United States)

    Li, Jianfu; Luo, Cheng; Peng, Yueheng; Xie, Qiankun; Gong, Jinnan; Dong, Li; Lai, Yongxiu; Li, Hong; Yao, Dezhong

    2014-01-01

    Musicians experience a large amount of information transfer and integration of complex sensory, motor, and auditory processes when training and playing musical instruments. Therefore, musicians are a useful model in which to investigate neural adaptations in the brain. Here, based on diffusion-weighted imaging, probabilistic tractography was used to determine the architecture of white matter anatomical networks in musicians and non-musicians. Furthermore, the features of the white matter networks were analyzed using graph theory. Small-world properties of the white matter network were observed in both groups. Compared with non-musicians, the musicians exhibited significantly increased connectivity strength in the left and right supplementary motor areas, the left calcarine fissure and surrounding cortex and the right caudate nucleus, as well as a significantly larger weighted clustering coefficient in the right olfactory cortex, the left medial superior frontal gyrus, the right gyrus rectus, the left lingual gyrus, the left supramarginal gyrus, and the right pallidum. Furthermore, there were differences in the node betweenness centrality in several regions. However, no significant differences in topological properties were observed at a global level. We illustrated preliminary findings to extend the network level understanding of white matter plasticity in musicians who have had long-term musical training. These structural, network-based findings may indicate that musicians have enhanced information transmission efficiencies in local white matter networks that are related to musical training.

  2. Probabilistic diffusion tractography reveals improvement of structural network in musicians.

    Directory of Open Access Journals (Sweden)

    Jianfu Li

    Full Text Available PURPOSE: Musicians experience a large amount of information transfer and integration of complex sensory, motor, and auditory processes when training and playing musical instruments. Therefore, musicians are a useful model in which to investigate neural adaptations in the brain. METHODS: Here, based on diffusion-weighted imaging, probabilistic tractography was used to determine the architecture of white matter anatomical networks in musicians and non-musicians. Furthermore, the features of the white matter networks were analyzed using graph theory. RESULTS: Small-world properties of the white matter network were observed in both groups. Compared with non-musicians, the musicians exhibited significantly increased connectivity strength in the left and right supplementary motor areas, the left calcarine fissure and surrounding cortex and the right caudate nucleus, as well as a significantly larger weighted clustering coefficient in the right olfactory cortex, the left medial superior frontal gyrus, the right gyrus rectus, the left lingual gyrus, the left supramarginal gyrus, and the right pallidum. Furthermore, there were differences in the node betweenness centrality in several regions. However, no significant differences in topological properties were observed at a global level. CONCLUSIONS: We illustrated preliminary findings to extend the network level understanding of white matter plasticity in musicians who have had long-term musical training. These structural, network-based findings may indicate that musicians have enhanced information transmission efficiencies in local white matter networks that are related to musical training.

  3. Information diffusion in structured online social networks

    Science.gov (United States)

    Li, Pei; Zhang, Yini; Qiao, Fengcai; Wang, Hui

    2015-05-01

    Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

  4. Tensegrity II. How structural networks influence cellular information processing networks

    Science.gov (United States)

    Ingber, Donald E.

    2003-01-01

    The major challenge in biology today is biocomplexity: the need to explain how cell and tissue behaviors emerge from collective interactions within complex molecular networks. Part I of this two-part article, described a mechanical model of cell structure based on tensegrity architecture that explains how the mechanical behavior of the cell emerges from physical interactions among the different molecular filament systems that form the cytoskeleton. Recent work shows that the cytoskeleton also orients much of the cell's metabolic and signal transduction machinery and that mechanical distortion of cells and the cytoskeleton through cell surface integrin receptors can profoundly affect cell behavior. In particular, gradual variations in this single physical control parameter (cell shape distortion) can switch cells between distinct gene programs (e.g. growth, differentiation and apoptosis), and this process can be viewed as a biological phase transition. Part II of this article covers how combined use of tensegrity and solid-state mechanochemistry by cells may mediate mechanotransduction and facilitate integration of chemical and physical signals that are responsible for control of cell behavior. In addition, it examines how cell structural networks affect gene and protein signaling networks to produce characteristic phenotypes and cell fate transitions during tissue development.

  5. FeatureMap3D - a tool to map protein features and sequence conservation onto homologous structures in the PDB

    DEFF Research Database (Denmark)

    Wernersson, Rasmus; Rapacki, Krzysztof; Stærfeldt, Hans Henrik

    2006-01-01

    FeatureMap3D is a web-based tool that maps protein features onto 3D structures. The user provides sequences annotated with any feature of interest, such as post-translational modifications, protease cleavage sites or exonic structure and FeatureMap3D will then search the Protein Data Bank (PDB......) for structures of homologous proteins. The results are displayed both as an annotated sequence alignment, where the user-provided annotations as well as the sequence conservation between the query and the target sequence are displayed, and also as a publication-quality image of the 3D protein structure...... with the selected features and sequence conservation enhanced. The results are also returned in a readily parsable text format as well as a PyMol (http://pymol.sourceforge.net/) script file, which allows the user to easily modify the protein structure image to suit a specific purpose. FeatureMap3D can also be used...

  6. Network structure of multivariate time series

    Science.gov (United States)

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-01

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

  7. The fundamental structures of dynamic social networks

    CERN Document Server

    Sekara, Vedran; Lehmann, Sune

    2015-01-01

    Networks provide a powerful mathematical framework for analyzing the structure and dynamics of complex systems (1-3). The study of group behavior has deep roots in the social science literature (4,5) and community detection is a central part of modern network science. Network communities have been found to be highly overlapping and organized in a hierarchical structure (6-9). Recent technological advances have provided a toolset for measuring the detailed social dynamics at scale (10,11). In spite of great progress, a quantitative description of the complex temporal behavior of social groups-with dynamics spanning from minute-by-minute changes to patterns expressed on the timescale of years-is still absent. Here we uncover a class of fundamental structures embedded within highly dynamic social networks. On the shortest time-scale, we find that social gatherings are fluid, with members coming and going, but organized via a stable core of individuals. We show that cores represent social contexts (9), with recur...

  8. Extracting salient features for network intrusion detection using machine learning methods

    Directory of Open Access Journals (Sweden)

    Ralf C. Staudemeyer

    2014-06-01

    Full Text Available This work presents a data preprocessing and feature selection framework to support data mining and network security experts in minimal feature set selection of intrusion detection data. This process is supported by detailed visualisation and examination of class distributions. Distribution histograms, scatter plots and information gain are presented as supportive feature reduction tools. The feature reduction process applied is based on decision tree pruning and backward elimination. This paper starts with an analysis of the KDD Cup '99 datasets and their potential for feature reduction. The dataset consists of connection records with 41 features whose relevance for intrusion detection are not clear. All traffic is either classified `normal' or into the four attack types denial-of-service, network probe, remote-to-local or user-to-root. Using our custom feature selection process, we show how we can significantly reduce the number features in the dataset to a few salient features. We conclude by presenting minimal sets with 4--8 salient features for two-class and multi-class categorisation for detecting intrusions, as well as for the detection of individual attack classes; the performance using a static classifier compares favourably to the performance using all features available. The suggested process is of general nature and can be applied to any similar dataset.

  9. Feature Extraction Method for High Impedance Ground Fault Localization in Radial Power Distribution Networks

    DEFF Research Database (Denmark)

    Jensen, Kåre Jean; Munk, Steen M.; Sørensen, John Aasted

    1998-01-01

    A new approach to the localization of high impedance ground faults in compensated radial power distribution networks is presented. The total size of such networks is often very large and a major part of the monitoring of these is carried out manually. The increasing complexity of industrial...... of three phase voltages and currents. The method consists of a feature extractor, based on a grid description of the feeder by impulse responses, and a neural network for ground fault localization. The emphasis of this paper is the feature extractor, and the detection of the time instance of a ground fault...... processes and communication systems lead to demands for improved monitoring of power distribution networks so that the quality of power delivery can be kept at a controlled level. The ground fault localization method for each feeder in a network is based on the centralized frequency broadband measurement...

  10. Methods and applications for detecting structure in complex networks

    Science.gov (United States)

    Leicht, Elizabeth A.

    The use of networks to represent systems of interacting components is now common in many fields including the biological, physical, and social sciences. Network models are widely applicable due to their relatively simple framework of vertices and edges. Network structure, patterns of connection between vertices, impacts both the functioning of networks and processes occurring on networks. However, many aspects of network structure are still poorly understood. This dissertation presents a set of network analysis methods and applications to real-world as well as simulated networks. The methods are divided into two main types: linear algebra formulations and probabilistic mixture model techniques. Network models lend themselves to compact mathematical representation as matrices, making linear algebra techniques useful probes of network structure. We present methods for the detection of two distinct, but related, network structural forms. First, we derive a measure of vertex similarity based upon network structure. The method builds on existing ideas concerning calculation of vertex similarity, but generalizes and extends the scope to large networks. Second, we address the detection of communities or modules in a specific class of networks, directed networks. We propose a method for detecting community structure in directed networks, which is an extension of a community detection method previously only known for undirected networks. Moving away from linear algebra formulations, we propose two methods for network structure detection based on probabilistic techniques. In the first method, we use the machinery of the expectation-maximization (EM) algorithm to probe patterns of connection among vertices in static networks. The technique allows for the detection of a broad range of types of structure in networks. The second method focuses on time evolving networks. We propose an application of the EM algorithm to evolving networks that can reveal significant structural

  11. Network Intrusion Detection through Discriminative Feature Selection by Using Sparse Logistic Regression

    Directory of Open Access Journals (Sweden)

    Reehan Ali Shah

    2017-11-01

    Full Text Available Intrusion detection system (IDS is a well-known and effective component of network security that provides transactions upon the network systems with security and safety. Most of earlier research has addressed difficulties such as overfitting, feature redundancy, high-dimensional features and a limited number of training samples but feature selection. We approach the problem of feature selection via sparse logistic regression (SPLR. In this paper, we propose a discriminative feature selection and intrusion classification based on SPLR for IDS. The SPLR is a recently developed technique for data analysis and processing via sparse regularized optimization that selects a small subset from the original feature variables to model the data for the purpose of classification. A linear SPLR model aims to select the discriminative features from the repository of datasets and learns the coefficients of the linear classifier. Compared with the feature selection approaches, like filter (ranking and wrapper methods that separate the feature selection and classification problems, SPLR can combine feature selection and classification into a unified framework. The experiments in this correspondence demonstrate that the proposed method has better performance than most of the well-known techniques used for intrusion detection.

  12. Structure and mechanics of aegagropilae fiber network.

    Science.gov (United States)

    Verhille, Gautier; Moulinet, Sébastien; Vandenberghe, Nicolas; Adda-Bedia, Mokhtar; Le Gal, Patrice

    2017-05-02

    Fiber networks encompass a wide range of natural and manmade materials. The threads or filaments from which they are formed span a wide range of length scales: from nanometers, as in biological tissues and bundles of carbon nanotubes, to millimeters, as in paper and insulation materials. The mechanical and thermal behavior of these complex structures depends on both the individual response of the constituent fibers and the density and degree of entanglement of the network. A question of paramount importance is how to control the formation of a given fiber network to optimize a desired function. The study of fiber clustering of natural flocs could be useful for improving fabrication processes, such as in the paper and textile industries. Here, we use the example of aegagropilae that are the remains of a seagrass (Posidonia oceanica) found on Mediterranean beaches. First, we characterize different aspects of their structure and mechanical response, and second, we draw conclusions on their formation process. We show that these natural aggregates are formed in open sea by random aggregation and compaction of fibers held together by friction forces. Although formed in a natural environment, thus under relatively unconstrained conditions, the geometrical and mechanical properties of the resulting fiber aggregates are quite robust. This study opens perspectives for manufacturing complex fiber network materials.

  13. Structure of the human chromosome interaction network.

    Directory of Open Access Journals (Sweden)

    Sergio Sarnataro

    Full Text Available New Hi-C technologies have revealed that chromosomes have a complex network of spatial contacts in the cell nucleus of higher organisms, whose organisation is only partially understood. Here, we investigate the structure of such a network in human GM12878 cells, to derive a large scale picture of nuclear architecture. We find that the intensity of intra-chromosomal interactions is power-law distributed. Inter-chromosomal interactions are two orders of magnitude weaker and exponentially distributed, yet they are not randomly arranged along the genomic sequence. Intra-chromosomal contacts broadly occur between epigenomically homologous regions, whereas inter-chromosomal contacts are especially associated with regions rich in highly expressed genes. Overall, genomic contacts in the nucleus appear to be structured as a network of networks where a set of strongly individual chromosomal units, as envisaged in the 'chromosomal territory' scenario derived from microscopy, interact with each other via on average weaker, yet far from random and functionally important interactions.

  14. NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

    Directory of Open Access Journals (Sweden)

    Joeri Ruyssinck

    Full Text Available One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made

  15. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Directory of Open Access Journals (Sweden)

    Liangji Zhou

    2017-01-01

    Full Text Available As a typical deep-learning model, Convolutional Neural Networks (CNNs can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases.

  16. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Science.gov (United States)

    Huo, Guanying

    2017-01-01

    As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

  17. Improving the Robustness of Complex Networks with Preserving Community Structure

    Science.gov (United States)

    Yang, Yang; Li, Zhoujun; Chen, Yan; Zhang, Xiaoming; Wang, Senzhang

    2015-01-01

    Complex networks are everywhere, such as the power grid network, the airline network, the protein-protein interaction network, and the road network. The networks are ‘robust yet fragile’, which means that the networks are robust against random failures but fragile under malicious attacks. The cascading failures, system-wide disasters and intentional attacks on these networks are deserving of in-depth study. Researchers have proposed many solutions to improve the robustness of these networks. However whilst many solutions preserve the degree distribution of the networks, little attention is paid to the community structure of these networks. We argue that the community structure of a network is a defining characteristic of a network which identifies its functionality and thus should be preserved. In this paper, we discuss the relationship between robustness and the community structure. Then we propose a 3-step strategy to improve the robustness of a network, while retaining its community structure, and also its degree distribution. With extensive experimentation on representative real-world networks, we demonstrate that our method is effective and can greatly improve the robustness of networks, while preserving community structure and degree distribution. Finally, we give a description of a robust network, which is useful not only for improving robustness, but also for designing robust networks and integrating networks. PMID:25674786

  18. Seasonal changes in the structure of rhesus macaque social networks.

    Science.gov (United States)

    Brent, Lauren J N; Maclarnon, Ann; Platt, Michael L; Semple, Stuart

    2013-03-01

    Social structure emerges from the patterning of interactions between individuals and plays a critical role in shaping some of the main characteristics of animal populations. The topological features of social structure, such as the extent to which individuals interact in clusters, can influence many biologically important factors, including the persistence of cooperation, and the rate of spread of disease. Yet the extent to which social structure topology fluctuates over relatively short periods of time in relation to social, demographic or environmental events remains unclear. Here, we use social network analysis to examine seasonal changes in the topology of social structures that emerge from socio-positive associations in adult female rhesus macaques ( Macaca mulatta ). Behavioral data for two different association types (grooming, spatial proximity) were collected for females in two free-ranging groups during two seasons: the mating and birth seasons. Stronger dyadic bonds resulted in social structures that were more tightly connected (i.e. of greater density) in the mating season compared to the birth season. Social structures were also more centralized around a subset of individuals, and were more clustered in the mating season than the birth season, although the latter differences were mostly driven by differences in density alone. Our results suggest a degree of temporal variation in the topological features of social structure in this population. Such variation may feed back on interactions, hence affecting the behaviors of individuals, and may therefore be important to take into account in studies of animal behavior.

  19. Feature Extraction and Fusion Using Deep Convolutional Neural Networks for Face Detection

    Directory of Open Access Journals (Sweden)

    Xiaojun Lu

    2017-01-01

    Full Text Available This paper proposes a method that uses feature fusion to represent images better for face detection after feature extraction by deep convolutional neural network (DCNN. First, with Clarifai net and VGG Net-D (16 layers, we learn features from data, respectively; then we fuse features extracted from the two nets. To obtain more compact feature representation and mitigate computation complexity, we reduce the dimension of the fused features by PCA. Finally, we conduct face classification by SVM classifier for binary classification. In particular, we exploit offset max-pooling to extract features with sliding window densely, which leads to better matches of faces and detection windows; thus the detection result is more accurate. Experimental results show that our method can detect faces with severe occlusion and large variations in pose and scale. In particular, our method achieves 89.24% recall rate on FDDB and 97.19% average precision on AFW.

  20. Community structure in introductory physics course networks

    CERN Document Server

    Traxler, Adrienne L

    2015-01-01

    Student-to-student interactions are foundational to many active learning environments, but are most often studied using qualitative methods. Network analysis tools provide a quantitative complement to this picture, allowing researchers to describe the social interactions of whole classrooms as systems. Past results from introductory physics courses have suggested a sharp division in the formation of social structure between large lecture sections and small studio classroom environments. Extending those results, this study focuses on calculus-based introductory physics courses at a large public university with a heavily commuter and nontraditional student population. Community detection network methods are used to characterize pre- and post-course collaborative structure in several sections, and differences are considered between small and large classes. These results are compared with expectations from earlier findings, and comment on implications for instruction and further study.

  1. Finding local community structure in networks

    Science.gov (United States)

    Clauset, Aaron

    2005-08-01

    Although the inference of global community structure in networks has recently become a topic of great interest in the physics community, all such algorithms require that the graph be completely known. Here, we define both a measure of local community structure and an algorithm that infers the hierarchy of communities that enclose a given vertex by exploring the graph one vertex at a time. This algorithm runs in time O(k2d) for general graphs when d is the mean degree and k is the number of vertices to be explored. For graphs where exploring a new vertex is time consuming, the running time is linear, O(k) . We show that on computer-generated graphs the average behavior of this technique approximates that of algorithms that require global knowledge. As an application, we use this algorithm to extract meaningful local clustering information in the large recommender network of an online retailer.

  2. Hierarchical Neural Network Structures for Phoneme Recognition

    CERN Document Server

    Vasquez, Daniel; Minker, Wolfgang

    2013-01-01

    In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are evaluated on the phoneme recognition task where a  Hybrid Hidden Markov Model/Artificial Neural Network paradigm is used. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron. Additionally, the output of the first level serves as a second level input. The computational speed of the phoneme recognizer can be substantially increased by removing redundant information still contained at the first level output. Several techniques based on temporal and phonetic criteria have been investigated to remove this redundant information. The computational time could be reduced by 57% whilst keeping the system accuracy comparable to the baseline hierarchical approach.

  3. Walk modularity and community structure in networks

    OpenAIRE

    Mehrle, David; Strosser, Amy; Harkin, Anthony

    2014-01-01

    Modularity maximization has been one of the most widely used approaches in the last decade for discovering community structure in networks of practical interest in biology, computing, social science, statistical mechanics, and more. Modularity is a quality function that measures the difference between the number of edges found within clusters minus the number of edges one would statistically expect to find based on random chance. We present a natural generalization of modularity based on the ...

  4. Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning.

    Science.gov (United States)

    Paisitkriangkrai, Sakrapee; Shen, Chunhua; Hengel, Anton van den

    2016-06-01

    Many typical applications of object detection operate within a prescribed false-positive range. In this situation the performance of a detector should be assessed on the basis of the area under the ROC curve over that range, rather than over the full curve, as the performance outside the prescribed range is irrelevant. This measure is labelled as the partial area under the ROC curve (pAUC). We propose a novel ensemble learning method which achieves a maximal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using structured learning. In addition, in order to achieve high object detection performance, we propose a new approach to extracting low-level visual features based on spatial pooling. Incorporating spatial pooling improves the translational invariance and thus the robustness of the detection process. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of our approach, and we show that it is possible to train state-of-the-art pedestrian detectors using the proposed structured ensemble learning method with spatially pooled features. The result is the current best reported performance on the Caltech-USA pedestrian detection dataset.

  5. Structure and dynamics of core-periphery networks

    CERN Document Server

    Csermely, Peter; Wu, Ling-Yun; Uzzi, Brian

    2013-01-01

    Recent studies uncovered important core/periphery network structures characterizing complex sets of cooperative and competitive interactions between network nodes, be they proteins, cells, species or humans. Better characterization of the structure, dynamics and function of core/periphery networks is a key step of our understanding cellular functions, species adaptation, social and market changes. Here we summarize the current knowledge of the structure and dynamics of "traditional" core/periphery networks, rich-clubs, nested, bow-tie and onion networks. Comparing core/periphery structures with network modules, we discriminate between global and local cores. The core/periphery network organization lies in the middle of several extreme properties, such as random/condensed structures, clique/star configurations, network symmetry/asymmetry, network assortativity/disassortativity, as well as network hierarchy/anti-hierarchy. These properties of high complexity together with the large degeneracy of core pathways e...

  6. Epidemic spreading on dual-structure networks with mobile agents

    Science.gov (United States)

    Yao, Yiyang; Zhou, Yinzuo

    2017-02-01

    The rapid development of modern society continually transforms the social structure which leads to an increasingly distinct dual structure of higher population density in urban areas and lower density in rural areas. Such structure may induce distinctive spreading behavior of epidemics which does not happen in a single type structure. In this paper, we study the epidemic spreading of mobile agents on dual structure networks based on SIRS model. First, beyond the well known epidemic threshold for generic epidemic model that when the infection rate is below the threshold a pertinent infectious disease will die out, we find the other epidemic threshold which appears when the infection rate of a disease is relatively high. This feature of two thresholds for the SIRS model may lead to the elimination of infectious disease when social network has either high population density or low population density. Interestingly, however, we find that when a high density area is connected to a low density may cause persistent spreading of the infectious disease, even though the same disease will die out when it spreads in each single area. This phenomenon indicates the critical role of the connection between the two areas which could radically change the behavior of spreading dynamics. Our findings, therefore, provide new understanding of epidemiology pertinent to the characteristic modern social structure and have potential to develop controlling strategies accordingly.

  7. Complex network perspective on structure and function of ...

    Indian Academy of Sciences (India)

    , uncovering complex network structure and function from these networks is becoming one of the most important topics in system biology. This work aims at studying the structure and function of Staphylococcus aureus (S. aureus) metabolic ...

  8. Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion

    Directory of Open Access Journals (Sweden)

    Lijun Zhang

    2018-02-01

    Full Text Available Aiming at the pitting fault of deep groove ball bearing during service, this paper uses the vibration signal of five different states of deep groove ball bearing and extracts the relevant features, then uses a neural network to model the degradation for identifying and classifying the fault type. By comparing the effects of training samples with different capacities through performance indexes such as the accuracy and convergence speed, it is proven that an increase in the sample size can improve the performance of the model. Based on the polynomial fitting principle and Pearson correlation coefficient, fusion features based on the skewness index are proposed, and the performance improvement of the model after incorporating the fusion features is also validated. A comparison of the performance of the support vector machine (SVM model and the neural network model on this dataset is given. The research shows that neural networks have more potential for complex and high-volume datasets.

  9. IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK FOR FACE RECOGNITION USING GABOR FEATURE EXTRACTION

    Directory of Open Access Journals (Sweden)

    Muthukannan K

    2013-11-01

    Full Text Available Face detection and recognition is the first step for many applications in various fields such as identification and is used as a key to enter into the various electronic devices, video surveillance, and human computer interface and image database management. This paper focuses on feature extraction in an image using Gabor filter and the extracted image feature vector is then given as an input to the neural network. The neural network is trained with the input data. The Gabor wavelet concentrates on the important components of the face including eye, mouth, nose, cheeks. The main requirement of this technique is the threshold, which gives privileged sensitivity. The threshold values are the feature vectors taken from the faces. These feature vectors are given into the feed forward neural network to train the network. Using the feed forward neural network as a classifier, the recognized and unrecognized faces are classified. This classifier attains a higher face deduction rate. By training more input vectors the system proves to be effective. The effectiveness of the proposed method is demonstrated by the experimental results.

  10. Microfabricated structures and devices featuring nanostructured titania thin films

    Science.gov (United States)

    Monkowski, Adam J.

    2007-05-01

    When titanium reacts with hydrogen peroxide at 80°C--100°C, a nanostructured titania (NST) thin film is formed on the titanium surface. This nanostructured film is particularly suited for integration with thin film and bulk microfabrication techniques. The ability to manufacture devices in a batch format is a principal advantage of microfabrication-based production. To reliably produce arrays of micro-patterned NST thin films on the wafer scale, a patterning guideline must be considered. The formation of NST relies on a re-deposition process; adequate ti-peroxo species must be generated and remain at the solid-solution interface. Numerical analysis of the characteristic transient diffusion behavior for various micro-patterns has been compared with experimental data to generate a criterion to guide the design of NST micro-patterns. Wafer scale arrays of NST micro gas-sensors have been fabricated using standard thin film techniques. Sensing elements are 20 mum on a side. High sensitivity to hydrogen is achieved by modification of the sensors with platinum nanoparticles. When exposed to a 10 mT partial pressure of hydrogen at 250°C, the functionalized devices exhibit more than one order of magnitude resistance decrease with a response time of approximately 7 sec. Titanium microstructures formed using the titanium ICP deep etch (TIDE) process have been integrated with NST films to produce an ordered nanostructure-microstructure composite (3D-NST). The process developed allows for the incorporation of a planar top surface, advantageous for bonding and sealing applications, in which the nanostructured thin film is formed only on feature sidewalls and floors. When titanium microstructures are spaced less than 1 mum apart, titania nanostructures bridge adjacent features. NST and 3D-NST structures have been assembled and tested in a dye-sensitized solar cell (DSSC) device. The NST film is approximately 900nm thick; this yielded a DSSC with an efficiency of 1.8%, similar

  11. Structural host-microbiota interaction networks.

    Science.gov (United States)

    Guven-Maiorov, Emine; Tsai, Chung-Jung; Nussinov, Ruth

    2017-10-01

    Hundreds of different species colonize multicellular organisms making them "metaorganisms". A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole-may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.

  12. Verification of the authenticity of handwritten signature using structure neural-network-type OCON

    Science.gov (United States)

    Molina, M. L.; Arias, N. A.; Gualdron, Oscar

    2004-10-01

    A method in order to carry out the verification of handwritten signatures is described. The method keeps in mind global features and local features that encode the shape and the dynamics of the signatures. Signatures are recorded with a digital tablet that can read the position and pressure of the pen. Input patterns are considered time and space dependent. Before extracting the information of the static features such as total length or height/width ratio, and the dynamic features such as speed or acceleration, the signature is normalized for position, size and orientation using its Fourier Descriptors. The comparison stage is carried out for algorithms of neurals networks. For each one of the sets of features a special two stage Perceptron OCON (one-class-one-network) classification structure has been implemented. In the first stage networks multilayer perceptron with few neurons are used. The classifier combines the decision results of the neural networks and the Euclidean distance obtained using the two feature sets. The results of the first-stage classifier feed a second-stage radial basis function (RBF) neural network structure, which makes the final decision. The entire system was extensively tested, 160 neurals networks has been implemented.

  13. Structure Learning in Power Distribution Networks

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-01-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as these related to demand response, outage detection and management, and improved load-monitoring. Here, inspired by proliferation of the metering technology, we discuss statistical estimation problems in structurally loopy but operationally radial distribution grids consisting in learning operational layout of the network from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time – which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  14. The complex channel networks of bone structure

    CERN Document Server

    Costa, Luciano da Fontoura; Beletti, Marcelo E

    2006-01-01

    Bone structure in mammals involves a complex network of channels (Havers and Volkmann channels) required to nourish the bone marrow cells. This work describes how three-dimensional reconstructions of such systems can be obtained and represented in terms of complex networks. Three important findings are reported: (i) the fact that the channel branching density resembles a power law implies the existence of distribution hubs; (ii) the conditional node degree density indicates a clear tendency of connection between nodes with degrees 2 and 4; and (iii) the application of the recently introduced concept of hierarchical clustering coefficient allows the identification of typical scales of channel redistribution. A series of important biological insights is drawn and discussed

  15. Unraveling the disease consequences and mechanisms of modular structure in animal social networks.

    Science.gov (United States)

    Sah, Pratha; Leu, Stephan T; Cross, Paul C; Hudson, Peter J; Bansal, Shweta

    2017-04-18

    Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

  16. Unraveling the disease consequences and mechanisms of modular structure in animal social networks

    Science.gov (United States)

    Sah, Pratha; Leu, Stephan T.; Cross, Paul C.; Hudson, Peter J.; Bansal, Shweta

    2017-01-01

    Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

  17. The sequence, structure and evolutionary features of HOTAIR in mammals

    Science.gov (United States)

    2011-01-01

    . Conclusions HOTAIR exists in mammals, has poorly conserved sequences and considerably conserved structures, and has evolved faster than nearby HoxC genes. Exons of HOTAIR show distinct evolutionary features, and a 239 bp domain in the 1804 bp exon6 is especially conserved. These features, together with the absence of some exons and sequences in mouse, rat and kangaroo, suggest ab initio generation of HOTAIR in marsupials. Structure prediction identifies two fragments in the 5' end exon1 and the 3' end domain B of exon6, with sequence and structure invariably occurring in various predicted structures of exon1, the domain B of exon6 and the full HOTAIR. PMID:21496275

  18. Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification.

    Science.gov (United States)

    Chen, Xiaobo; Zhang, Han; Lee, Seong-Whan; Shen, Dinggang

    2017-07-01

    Conventional Functional connectivity (FC) analysis focuses on characterizing the correlation between two brain regions, whereas the high-order FC can model the correlation between two brain region pairs. To reduce the number of brain region pairs, clustering is applied to group all the brain region pairs into a small number of clusters. Then, a high-order FC network can be constructed based on the clustering result. By varying the number of clusters, multiple high-order FC networks can be generated and the one with the best overall performance can be finally selected. However, the important information contained in other networks may be simply discarded. To address this issue, in this paper, we propose to make full use of the information contained in all high-order FC networks. First, an agglomerative hierarchical clustering technique is applied such that the clustering result in one layer always depends on the previous layer, thus making the high-order FC networks in the two consecutive layers highly correlated. As a result, the features extracted from high-order FC network in each layer can be decomposed into two parts (blocks), i.e., one is redundant while the other might be informative or complementary, with respect to its previous layer. Then, a selective feature fusion method, which combines sequential forward selection and sparse regression, is developed to select a feature set from those informative feature blocks for classification. Experimental results confirm that our novel method outperforms the best single high-order FC network in diagnosis of mild cognitive impairment (MCI) subjects.

  19. Holistic Network Defense: Fusing Host and Network Features for Attack Classification

    Science.gov (United States)

    2011-03-01

    is, it tries to log in, to install a clandestine “back door” to the internet, and then to contact a server in Denmark or Malaysia for instructions...sniffer also used by Erskine to monitor a network device for inbound and outbound network packets and save the entire session into a PCAP file for later...Engineering Conference, Chiang Mai, Thailand , (2000). 27. Kim, H. "Internet Traffic Classification Demystified: Myths, Caveats, and Best Practices

  20. A diagnosis model for early Tourette syndrome children based on brain structural network characteristics

    Science.gov (United States)

    Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

    2016-03-01

    Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.

  1. Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network

    Science.gov (United States)

    Li, Huajiao; An, Haizhong; Wang, Yue; Huang, Jiachen; Gao, Xiangyun

    2016-05-01

    Keeping abreast of trends in the articles and rapidly grasping a body of article's key points and relationship from a holistic perspective is a new challenge in both literature research and text mining. As the important component, keywords can present the core idea of the academic article. Usually, articles on a single theme or area could share one or some same keywords, and we can analyze topological features and evolution of the articles co-keyword networks and keywords co-occurrence networks to realize the in-depth analysis of the articles. This paper seeks to integrate statistics, text mining, complex networks and visualization to analyze all of the academic articles on one given theme, complex network(s). All 5944 ;complex networks; articles that were published between 1990 and 2013 and are available on the Web of Science are extracted. Based on the two-mode affiliation network theory, a new frontier of complex networks, we constructed two different networks, one taking the articles as nodes, the co-keyword relationships as edges and the quantity of co-keywords as the weight to construct articles co-keyword network, and another taking the articles' keywords as nodes, the co-occurrence relationships as edges and the quantity of simultaneous co-occurrences as the weight to construct keyword co-occurrence network. An integrated method for analyzing the topological features and evolution of the articles co-keyword network and keywords co-occurrence networks is proposed, and we also defined a new function to measure the innovation coefficient of the articles in annual level. This paper provides a useful tool and process for successfully achieving in-depth analysis and rapid understanding of the trends and relationships of articles in a holistic perspective.

  2. Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations.

    Directory of Open Access Journals (Sweden)

    Benjamin A Logsdon

    Full Text Available Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL, which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1, and genes involved in endocytosis (RCY1, the spindle checkpoint (BUB2, sulfonate catabolism (JLP1, and cell-cell communication (PRM7. Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data.

  3. The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

    Directory of Open Access Journals (Sweden)

    Rutger Goekoop

    Full Text Available INTRODUCTION: Human personality is described preferentially in terms of factors (dimensions found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. AIM: To directly compare the ability of network community detection (NCD and principal component factor analysis (PCA to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R. METHODS: 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. RESULTS: At facet level, NCS showed a best match (96.2% with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80% with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. CONCLUSION: We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.

  4. The Network Structure of Human Personality According to the NEO-PI-R: Matching Network Community Structure to Factor Structure

    Science.gov (United States)

    Goekoop, Rutger; Goekoop, Jaap G.; Scholte, H. Steven

    2012-01-01

    Introduction Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. Aim To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R). Methods 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. Results At facet level, NCS showed a best match (96.2%) with a ‘confirmatory’ 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with ‘confirmatory’ 5-FS and ‘exploratory’ 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. Conclusion We present the first optimized network graph of personality traits according to the NEO-PI-R: a ‘Personality Web’. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network. PMID:23284713

  5. The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

    Science.gov (United States)

    Goekoop, Rutger; Goekoop, Jaap G; Scholte, H Steven

    2012-01-01

    Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R). 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. At facet level, NCS showed a best match (96.2%) with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.

  6. Paradoxes of Social Networking in a Structured Web 2.0 Language Learning Community

    Science.gov (United States)

    Loiseau, Mathieu; Zourou, Katerina

    2012-01-01

    This paper critically inquires into social networking as a set of mechanisms and associated practices developed in a structured Web 2.0 language learning community. This type of community can be roughly described as learning spaces featuring (more or less) structured language learning resources displaying at least some notions of language learning…

  7. Information diversity in structure and dynamics of simulated neuronal networks.

    Science.gov (United States)

    Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Nykter, Matti; Kesseli, Juha; Ruohonen, Keijo; Yli-Harja, Olli; Linne, Marja-Leena

    2011-01-01

    Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviors are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses. We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.

  8. Information Diversity in Structure and Dynamics of Simulated Neuronal Networks

    Directory of Open Access Journals (Sweden)

    Tuomo eMäki-Marttunen

    2011-06-01

    Full Text Available Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance (NCD. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviours are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses.We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.

  9. Features of deformation of poroelastic media with low structural strength

    Science.gov (United States)

    Goldstein, Robert; Gordeev, Yurii; Kornev, Konstantin

    Many natural and technological processes are associated with deformation and fracture of saturated or being saturated poroelastic media. Among such processes one can mention fluid soaking through a dam, fluid inflow to the cracks of hydraulic fracture, polishing using porous materials and special fluids, flow in catalytic pellets. All these processes are accompanied by deformation and fracture of a matrix with fluid flow. The effects at the interface porous body-fluid are essential for the processes. The specific features of deformation of poroelastic media with low structural strength are considered in this paper. The compressibility of the matrix skeleton is larger as compared to the compressibility of the saturating fluid in such media. It is shown that the oozing of the fluid at the surface of the poroelastic medium occurs in the consolidated flow regime under the action of `fluid piston' like loads if the structural strength of the medium is low. This result is obtained for both plane (deformation of a layer or halfinfinite medium) and centrally symmetric (deformation of a sphere) problems.

  10. Structure removal: An argument for feature-driven Merge

    Directory of Open Access Journals (Sweden)

    Gereon Müller

    2017-04-01

    Full Text Available Assuming that it needs to be decided at some point whether a given Merge('α','β' operation is legitimate, there are two basic options. The first possibility is that one of the two categories is equipped with an intrinsic formal property (typically encoded as a feature requiring the other one to combine with it. The second possibility is that Merge applies freely throughout, and that filters check the output representation and decide about the legitimacy of the operation. The two approaches are often extensionally equivalent. In this paper, I provide an argument for the first view that is based on the hypothesis that in addition to the Merge operation that 'builds 'structure, there is also a mirror image operation Remove that 'removes 'structure: If such an operation exists, the legitimacy of the original Merge operation cannot be checked by output filters anymore. Empirical evidence for an elementary syntactic operation Remove is drawn from four domains of German syntax: passive, applicative, restructuring, and complex prefields. This article is part of the special collection: What drives syntactic computation?

  11. Stable Matching with Incomplete Information in Structured Networks

    OpenAIRE

    Ling, Ying; Wan, Tao; Qin, Zengchang

    2015-01-01

    In this paper, we investigate stable matching in structured networks. Consider case of matching in social networks where candidates are not fully connected. A candidate on one side of the market gets acquaintance with which one on the heterogeneous side depends on the structured network. We explore four well-used structures of networks and define the social circle by the distance between each candidate. When matching within social circle, we have equilibrium distinguishes from each other sinc...

  12. Feature selection for anomaly–based network intrusion detection using cluster validity indices

    CSIR Research Space (South Africa)

    Naidoo, T

    2015-09-01

    Full Text Available by the algorithm are shown to improve the classification performance of an anomaly–based network intrusion detection system over the NSL-KDD dataset. The system approaches the performance attained by using feature sets derived from labelled training data via...

  13. Contribution of collagen network features to functional properties of engineered cartilage

    NARCIS (Netherlands)

    Bastiaansen-Jenniskens, Y.M.; Koevoet, W.; Bart, A.C.W. de; Linden, J.C. van der; Zuurmond, A.M.; Weinans, H.; Verhaar, J.A.N.; Osch, G.J.V.M. van; Groot, J. de

    2008-01-01

    Background: Damage to articular cartilage is one of the features of osteoarthritis (OA). Cartilage damage is characterised by a net loss of collagen and proteoglycans. The collagen network is considered highly important for cartilage function but little is known about processes that control

  14. Featuring Multiple Local Optima to Assist the User in the Interpretation of Induced Bayesian Network Models

    DEFF Research Database (Denmark)

    Dalgaard, Jens; Pena, Jose; Kocka, Tomas

    2004-01-01

    We propose a method to assist the user in the interpretation of the best Bayesian network model indu- ced from data. The method consists in extracting relevant features from the model (e.g. edges, directed paths and Markov blankets) and, then, assessing the con¯dence in them by studying multiple...

  15. Spectral and bispectral feature-extraction neural networks for texture classification

    Science.gov (United States)

    Kameyama, Keisuke; Kosugi, Yukio

    1997-10-01

    A neural network model (Kernel Modifying Neural Network: KM Net) specialized for image texture classification, which unifies the filtering kernels for feature extraction and the layered network classifier, will be introduced. The KM Net consists of a layer of convolution kernels that are constrained to be 2D Gabor filters to guarantee efficient spectral feature localization. The KM Net enables an automated feature extraction in multi-channel texture classification through simultaneous modification of the Gabor kernel parameters (central frequency and bandwidth) and the connection weights of the subsequent classifier layers by a backpropagation-based training rule. The capability of the model and its training rule was verified via segmentation of common texture mosaic images. In comparison with the conventional multi-channel filtering method which uses numerous filters to cover the spatial frequency domain, the proposed strategy can greatly reduce the computational cost both in feature extraction and classification. Since the adaptive Gabor filtering scheme is also applicable to band selection in moment spectra of higher orders, the network model was extended for adaptive bispectral filtering for extraction of the phase relation among the frequency components. The ability of this Bispectral KM Net was demonstrated in the discrimination of visually discriminable synthetic textures with identical local power spectral distributions.

  16. A Closer Look at Deep Learning Neural Networks with Low-level Spectral Periodicity Features

    DEFF Research Database (Denmark)

    Sturm, Bob L.; Kereliuk, Corey; Pikrakis, Aggelos

    2014-01-01

    Systems built using deep learning neural networks trained on low-level spectral periodicity features (DeSPerF) reproduced the most “ground truth” of the systems submitted to the MIREX 2013 task, “Audio Latin Genre Classification.” To answer why this was the case, we take a closer look...

  17. Features of Digital African American Language in a Social Network Site

    Science.gov (United States)

    Cunningham, Jennifer M.

    2014-01-01

    This study examines a social network site (SNS) where specific interlocutors communicate by combining aspects of academic American English (AE), digital language (DL), and African American Language (AAL)--creating a digital form of AAL or digital AAL (DAAL). This article describes the features of DAAL in the discursive, online context of MySpace,…

  18. AUTOMATIC SHIP DETECTION IN SINGLE-POL SAR IMAGES USING TEXTURE FEATURES IN ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    E. Khesali

    2015-12-01

    Full Text Available This paper presents a novel method for detecting ships from high-resolution synthetic aperture radar (SAR images. This method categorizes ship targets from single-pol SAR images using texture features in artificial neural networks. As such, the method tries to overcome the lack of an operational solution that is able to reliably detect ships with one SAR channel. The method has the following three main stages: 1 feature extraction; 2 feature selection; and 3 ship detection. The first part extracts different texture features from SAR image. These textures include occurrence and co occurrence measures with different window sizes. Then, best features are selected. Finally, the artificial neural network is used to extract ship pixels from sea ones. In post processing stage some morphological filters are used to improve the result. The effectiveness of the proposed method is verified using Sentinel-1 data in VV polarization. Experimental results indicate that the proposed algorithm can be implemented with time-saving, high precision ship extraction, feature analysis, and detection. The results also show that using texture features the algorithm properly discriminates speckle noise from ships.

  19. Retinal structure, function, and molecular pathologic features in gyrate atrophy.

    Science.gov (United States)

    Sergouniotis, Panagiotis I; Davidson, Alice E; Lenassi, Eva; Devery, Sophie R; Moore, Anthony T; Webster, Andrew R

    2012-03-01

    To describe phenotypic variability and to report novel mutational data in patients with gyrate atrophy. Retrospective case series. Seven unrelated patients (10 to 52 years of age) with clinical and biochemical evidence of gyrate atrophy. Detailed ophthalmologic examination, fundus photography, fundus autofluorescence (FAF) imaging, spectral-domain optical coherence tomography, and microperimetry testing were performed. The coding region and intron-exon boundaries of ornithine aminotransferase (OAT) were analyzed. OAT mRNA was isolated from peripheral blood leucocytes of 1 patient and analyzed. OAT mutation status and resultant clinical, structural, and functional characteristics. Funduscopy revealed circular areas of chorioretinal atrophy, and FAF imaging showed sharply demarcated areas of increased or preserved signal in all 7 patients. Spectral-domain optical coherence tomography revealed multiple intraretinal cystic spaces and hyperreflective deposit in the ganglion cell layer of all study subjects. Round tubular, rosette-like structures located in the outer nuclear layer of the retinae of the 4 older patients were observed (termed outer retinal tubulation). Thickening was evident in the foveolae of younger patients, despite the posterior pole appearing relatively preserved. Macular function, assessed by microperimetry, was preserved over areas of normal or increased autofluorescence. However, sensitivity was reduced even in structurally intact parts of the retina. The molecular pathologic features were determined in all study subjects: 9 mutations, 4 novel, were detected in the OAT gene. OAT mRNA was isolated from blood leukocytes, and monoallelic expression of a mutated allele was demonstrated in 1 patient. Fundus autofluorescence imaging can reveal the extent of neurosensory dysfunction in gyrate atrophy patients. Macular edema is a uniform finding; the fovea is relatively thick in early stages of disease and retinal tubulation is present in advanced disease

  20. Dynamical Structure of a Traditional Amazonian Social Network

    Directory of Open Access Journals (Sweden)

    Paul L. Hooper

    2013-11-01

    Full Text Available Reciprocity is a vital feature of social networks, but relatively little is known about its temporal structure or the mechanisms underlying its persistence in real world behavior. In pursuit of these two questions, we study the stationary and dynamical signals of reciprocity in a network of manioc beer (Spanish: chicha; Tsimane’: shocdye’ drinking events in a Tsimane’ village in lowland Bolivia. At the stationary level, our analysis reveals that social exchange within the community is heterogeneously patterned according to kinship and spatial proximity. A positive relationship between the frequencies at which two families host each other, controlling for kinship and proximity, provides evidence for stationary reciprocity. Our analysis of the dynamical structure of this network presents a novel method for the study of conditional, or non-stationary, reciprocity effects. We find evidence that short-timescale reciprocity (within three days is present among non- and distant-kin pairs; conversely, we find that levels of cooperation among close kin can be accounted for on the stationary hypothesis alone.

  1. Feature extraction using convolutional neural network for classifying breast density in mammographic images

    Science.gov (United States)

    Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

    2017-03-01

    Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is

  2. Inferring the mesoscale structure of layered, edge-valued and time-varying networks

    CERN Document Server

    Peixoto, Tiago P

    2015-01-01

    Many network systems are composed of interdependent but distinct types of interactions, which cannot be fully understood in isolation. These different types of interactions are often represented as layers, attributes on the edges or as a time-dependence of the network structure. Although they are crucial for a more comprehensive scientific understanding, these representations offer substantial challenges. Namely, it is an open problem how to precisely characterize the large or mesoscale structure of network systems in relation to these additional aspects. Furthermore, the direct incorporation of these features invariably increases the effective dimension of the network description, and hence aggravates the problem of overfitting, i.e. the use of overly-complex characterizations that mistake purely random fluctuations for actual structure. In this work, we propose a robust and principled method to tackle these problems, by constructing generative models of modular network structure, incorporating layered, attr...

  3. Time Score: A New Feature for Link Prediction in Social Networks

    Science.gov (United States)

    Munasinghe, Lankeshwara; Ichise, Ryutaro

    Link prediction in social networks, such as friendship networks and coauthorship networks, has recently attracted a great deal of attention. There have been numerous attempts to address the problem of link prediction through diverse approaches. In the present paper, we focus on the temporal behavior of the link strength, particularly the relationship between the time stamps of interactions or links and the temporal behavior of link strength and how link strength affects future link evolution. Most previous studies have not sufficiently discussed either the impact of time stamps of the interactions or time stamps of the links on link evolution. The gap between the current time and the time stamps of the interactions or links is also important to link evolution. In the present paper, we introduce a new time-aware feature, referred to as time score, that captures the important aspects of time stamps of interactions and the temporality of the link strengths. We also analyze the effectiveness of time score with different parameter settings for different network data sets. The results of the analysis revealed that the time score was sensitive to different networks and different time measures. We applied time score to two social network data sets, namely, Facebook friendship network data set and a coauthorship network data set. The results revealed a significant improvement in predicting future links.

  4. Lung cancer risk prediction method based on feature selection and artificial neural network.

    Science.gov (United States)

    Xie, Nan-Nan; Hu, Liang; Li, Tai-Hui

    2014-01-01

    A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisher and ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer risk prediction. The process featured two steps, firstly choosing the risk factors by combining two feature selection algorithms, then providing the predictive value by neural network. Based on the method framework, an algorithm LCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practical applications. The proposed method is suitable for health monitoring and self-testing. Experiments showed it can actually provide satisfactory accuracy under low dimensions of risk factors.

  5. Mapping the structural and dynamical features of kinesin motor domains.

    Directory of Open Access Journals (Sweden)

    Guido Scarabelli

    Full Text Available Kinesin motor proteins drive intracellular transport by coupling ATP hydrolysis to conformational changes that mediate directed movement along microtubules. Characterizing these distinct conformations and their interconversion mechanism is essential to determining an atomic-level model of kinesin action. Here we report a comprehensive principal component analysis of 114 experimental structures along with the results of conventional and accelerated molecular dynamics simulations that together map the structural dynamics of the kinesin motor domain. All experimental structures were found to reside in one of three distinct conformational clusters (ATP-like, ADP-like and Eg5 inhibitor-bound. These groups differ in the orientation of key functional elements, most notably the microtubule binding α4-α5, loop8 subdomain and α2b-β4-β6-β7 motor domain tip. Group membership was found not to correlate with the nature of the bound nucleotide in a given structure. However, groupings were coincident with distinct neck-linker orientations. Accelerated molecular dynamics simulations of ATP, ADP and nucleotide free Eg5 indicate that all three nucleotide states could sample the major crystallographically observed conformations. Differences in the dynamic coupling of distal sites were also evident. In multiple ATP bound simulations, the neck-linker, loop8 and the α4-α5 subdomain display correlated motions that are absent in ADP bound simulations. Further dissection of these couplings provides evidence for a network of dynamic communication between the active site, microtubule-binding interface and neck-linker via loop7 and loop13. Additional simulations indicate that the mutations G325A and G326A in loop13 reduce the flexibility of these regions and disrupt their couplings. Our combined results indicate that the reported ATP and ADP-like conformations of kinesin are intrinsically accessible regardless of nucleotide state and support a model where neck

  6. Structural Properties of the Brazilian Air Transportation Network

    Directory of Open Access Journals (Sweden)

    GUILHERME S. COUTO

    2015-09-01

    Full Text Available The air transportation network in a country has a great impact on the local, national and global economy. In this paper, we analyze the air transportation network in Brazil with complex network features to better understand its characteristics. In our analysis, we built networks composed either by national or by international flights. We also consider the network when both types of flights are put together. Interesting conclusions emerge from our analysis. For instance, Viracopos Airport (Campinas City is the most central and connected airport on the national flights network. Any operational problem in this airport separates the Brazilian national network into six distinct subnetworks. Moreover, the Brazilian air transportation network exhibits small world characteristics and national connections network follows a power law distribution. Therefore, our analysis sheds light on the current Brazilian air transportation infrastructure, bringing a novel understanding that may help face the recent fast growth in the usage of the Brazilian transport network.

  7. Fundamental structures of dynamic social networks

    DEFF Research Database (Denmark)

    Sekara, Vedran; Stopczynski, Arkadiusz; Jørgensen, Sune Lehmann

    2016-01-01

    unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit...... a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework......Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships...

  8. Dynamics and control of diseases in networks with community structure.

    Directory of Open Access Journals (Sweden)

    Marcel Salathé

    2010-04-01

    Full Text Available The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc. depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.

  9. Fracture network topology and characterization of structural permeability

    Science.gov (United States)

    Hansberry, Rowan; King, Rosalind; Holford, Simon

    2017-04-01

    other permeability indicators such as drilling fluid losses, and pore pressure measurements. Initial work with these techniques has led to new developments in our ability to image subsurface faults and fractures at a variety of scales from independent datasets. We establish a strong relationship between features identified using seismic attribute analysis and interpreted natural fractures. However, care must be taken to use these methods in a case-by-case basis, as controls on fracture distribution and orientation can vary significantly with both regional and local influences. These results outline and effective method by which structural permeability can be assessed with existing petroleum datasets. However, unlike the broad stress field, mapping fracture orientation and characteristics within the Australian Continent is complicated as the distribution, geometry, areal extent and connectivity of fracture networks can vary significantly.

  10. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    1995-01-01

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  11. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  12. Structural diversity in bacterial ribosomes: mycobacterial 70S ribosome structure reveals novel features.

    Science.gov (United States)

    Shasmal, Manidip; Sengupta, Jayati

    2012-01-01

    Here we present analysis of a 3D cryo-EM map of the 70S ribosome from Mycobacterium smegmatis, a saprophytic cousin of the etiological agent of tuberculosis in humans, Mycobacterium tuberculosis. In comparison with the 3D structures of other prokaryotic ribosomes, the density map of the M. smegmatis 70S ribosome reveals unique structural features and their relative orientations in the ribosome. Dramatic changes in the periphery due to additional rRNA segments and extra domains of some of the peripheral ribosomal proteins like S3, S5, S16, L17, L25, are evident. One of the most notable features appears in the large subunit near L1 stalk as a long helical structure next to helix 54 of the 23S rRNA. The sharp upper end of this structure is located in the vicinity of the mRNA exit channel. Although the M. smegmatis 70S ribosome possesses conserved core structure of bacterial ribosome, the new structural features, unveiled in this study, demonstrates diversity in the 3D architecture of bacterial ribosomes. We postulate that the prominent helical structure related to the 23S rRNA actively participates in the mechanisms of translation in mycobacteria.

  13. Neural development features: Spatio-temporal development of the Caenorhabditis elegans neuronal network

    CERN Document Server

    Varier, Sreedevi; 10.1371/journal.pcbi.1001044

    2011-01-01

    The nematode Caenorhabditis elegans, with information on neural connectivity, three-dimensional position and cell linage provides a unique system for understanding the development of neural networks. Although C. elegans has been widely studied in the past, we present the first statistical study from a developmental perspective, with findings that raise interesting suggestions on the establishment of long-distance connections and network hubs. Here, we analyze the neuro-development for temporal and spatial features, using birth times of neurons and their three-dimensional positions. Comparisons of growth in C. elegans with random spatial network growth highlight two findings relevant to neural network development. First, most neurons which are linked by long-distance connections are born around the same time and early on, suggesting the possibility of early contact or interaction between connected neurons during development. Second, early-born neurons are more highly connected (tendency to form hubs) than late...

  14. Discriminative feature representation for image classification via multimodal multitask deep neural networks

    Science.gov (United States)

    Mei, Shuang; Yang, Hua; Yin, Zhouping

    2017-01-01

    A good image feature representation is crucial for image classification tasks. Many traditional applications have attempted to design single-modal features for image classification; however, these may have difficulty extracting sufficient information, resulting in misjudgments for various categories. Recently, researchers have focused on designing multimodal features, which have been successfully employed in many situations. However, there are still some problems in this research area, including selecting efficient features for each modality, transforming them to the subspace feature domain, and removing the heterogeneities among different modalities. We propose an end-to-end multimodal deep neural network (MDNN) framework to automate the feature selection and transformation procedures for image classification. Furthermore, inspired by Fisher's theory of linear discriminant analysis, we improve the proposed MDNN by further proposing a multimodal multitask deep neural network (M2DNN) model. The motivation behind M2DNN is to improve the classification performance by incorporating an auxiliary discriminative constraint to the subspace representation. Experimental results on five representative datasets (NUS-WIDE, Scene-15, Texture-25, Indoor-67, and Caltech-101) demonstrate the effectiveness of the proposed MDNN and M2DNN models. In addition, experimental comparisons of the Fisher score criterion exhibit that M2DNN is more robust and has better discriminative power than other approaches.

  15. Genetic algorithm for the optimization of features and neural networks in ECG signals classification.

    Science.gov (United States)

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-31

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

  16. Genetic algorithm for the optimization of features and neural networks in ECG signals classification

    Science.gov (United States)

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-01

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

  17. Structural feature study of benzofuran derivatives as farnesyltransferase inhibitors.

    Science.gov (United States)

    Moorthy, N S Hari Narayana; Sousa, Sergio F; Ramos, Maria J; Fernandes, Pedro A

    2011-12-01

    Ras proteins are small GTPases (G-proteins) that play a key role in cell growth and cell proliferation in the mitogen-activated protein kinase signal transduction pathway. Farnesylation is a critical step for membrane binding and the biological function of G-proteins. In the present investigation, we have studied the structural features of some molecules that are acting on the farnesyltransferase (FTase) enzyme for the inhibition of the farnesylation step in G-proteins. The benzofuran derivatives have activity against FTase inhibition and antiproliferative activity on QG56 cell lines. The result obtained from the quantitative structure-activity relationship study of these compounds shows that the models have significant predictive power and stability, as shown by statistical parameters such as R(2), Q(2), R(2)(pred), R(2)(m), F-value, Durbin-Watson, variable inflation factor values, Mahalanobis, and Cook's distances. The contribution of each descriptor for the activities (β-coefficients) reveals that the P-VSA descriptors (van der Waals surface area descriptors) such as vsa_pol, vsa_acc and SMR_VSA3 are the major contributors for the activity, along with other descriptors such as the partition coefficient, the partial charge, the atom and bond count and the adjacency, and distance descriptors. Earlier study on the FTase enzyme in our laboratory reveals that the existence of positively-charged groups on the FTase active site is important for drug design. It is also showing that the presence of hydrogen bonding donor and acceptor groups, together with negatively charged substituents is critical for improved activity by this series of molecules. These results offer important clues for the development of novel FTase inhibitors.

  18. How structure determines correlations in neuronal networks

    National Research Council Canada - National Science Library

    Pernice, Volker; Staude, Benjamin; Cardanobile, Stefano; Rotter, Stefan

    2011-01-01

    Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting...

  19. Towards structural controllability of local-world networks

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Shiwen, E-mail: sunsw80@126.com [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China); Ma, Yilin; Wu, Yafang; Wang, Li; Xia, Chengyi [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China)

    2016-05-20

    Controlling complex networks is of vital importance in science and engineering. Meanwhile, local-world effect is an important ingredient which should be taken into consideration in the complete description of real-world complex systems. In this letter, structural controllability of a class of local-world networks is investigated. Through extensive numerical simulations, firstly, effects of local world size M and network size N on structural controllability are examined. For local-world networks with sparse topological configuration, compared to network size, local-world size can induce stronger influence on controllability, however, for dense networks, controllability is greatly affected by network size and local-world effect can be neglected. Secondly, relationships between controllability and topological properties are analyzed. Lastly, the robustness of local-world networks under targeted attacks regarding structural controllability is discussed. These results can help to deepen the understanding of structural complexity and connectivity patterns of complex systems. - Highlights: • Structural controllability of a class of local-world networks is investigated. • For sparse local-world networks, compared to network size, local-world size can bring stronger influence on controllability. • For dense networks, controllability is greatly affected by network size and the effect of local-world size can be neglected. • Structural controllability against targeted node attacks is discussed.

  20. Potential applications of ferrocene as a structural feature in antioxidants.

    Science.gov (United States)

    Liu, Zai-Qun

    2011-04-01

    Comparing with the wide usage of ferrocene in novel materials, ferrocene was unusually applied to be a structural feature in designing drugs even though some researchers pointed out that ferrocene and its derivatives possessed potential pharmacological applications. This was due to that low polarity limited bioavailability of ferrocene in vivo. Since ferrocene was inert to the oxidation at atmosphere, it was deduced that synthetic derivatives of ferrocene may be a novel kind of antioxidant, in which other organic groups may enhance the bioavailability of ferrocene, or large conjugated system formed among ferrocenyl and other organic groups may increase the antioxidant effectiveness. Thus, synthetic derivatives of ferrocene were divided into nonconjugated and conjugated ones in this review. For nonconjugated ferrocenyl derivatives, carbon chain or simple group attached one or two cyclopentadienyl rings in ferrocene to form a novel molecule with ferrocenyl group. The aim of synthesis of nonconjugated ferrocenyl compounds was to increase the bioavailability of ferrocene in vivo. On the other hand, the conjugated ferrocenyl derivatives referred to introduce other group to form a conjugated system with the cyclopentadienyl ring in ferrocene. The large conjugated system was beneficial for the single electron to dispense among the whole molecule while forming radicals, and enhanced the antioxidant capacity of the whole molecule. This review summarized the potential usage of ferrocene in antioxidants.

  1. Structural features of DNA interaction with caffeine and theophylline

    Science.gov (United States)

    Nafisi, Shohreh; Manouchehri, Firouzeh; Tajmir-Riahi, Heidar-Ali; Varavipour, Maryam

    2008-03-01

    Caffeine and theophylline are strong antioxidants that prevent DNA damage. The anticancer and antiviral activities of these natural products are implicated in their mechanism of actions. However, there has been no information on the interactions of these xanthine derivatives with individual DNA at molecular level. The aim of this study was to examine the stability and structural features of calf-thymus DNA complexes with caffeine and theophylline in aqueous solution, using constant DNA concentration (6.25 mM) and various caffeine or theophylline/DNA(P) ratios of 1/80, 1/40, 1/20, 1/10, 1/5, 1/2 and 1/1. FTIR, UV-visible spectroscopic methods were used to determine the ligand external binding modes, the binding constant and the stability of caffeine, theophylline-DNA complexes in aqueous solution. Spectroscopic evidence showed that the complexation of caffeine and theophylline with DNA occurred via G-C and A-T and PO 2 group with overall binding constants of K(caffeine-DNA) = 9.7 × 10 3 M -1 and K(theophylline-DNA) = 1.7 × 10 4 M -1. The affinity of ligand-DNA binding is in the order of theophylline > caffeine. A partial B to A-DNA transition occurs upon caffeine and theophylline complexation.

  2. Large-scale identification of human protein function using topological features of interaction network

    Science.gov (United States)

    Li, Zhanchao; Liu, Zhiqing; Zhong, Wenqian; Huang, Menghua; Wu, Na; Xie, Yun; Dai, Zong; Zou, Xiaoyong

    2016-11-01

    The annotation of protein function is a vital step to elucidate the essence of life at a molecular level, and it is also meritorious in biomedical and pharmaceutical industry. Developments of sequencing technology result in constant expansion of the gap between the number of the known sequences and their functions. Therefore, it is indispensable to develop a computational method for the annotation of protein function. Herein, a novel method is proposed to identify protein function based on the weighted human protein-protein interaction network and graph theory. The network topology features with local and global information are presented to characterise proteins. The minimum redundancy maximum relevance algorithm is used to select 227 optimized feature subsets and support vector machine technique is utilized to build the prediction models. The performance of current method is assessed through 10-fold cross-validation test, and the range of accuracies is from 67.63% to 100%. Comparing with other annotation methods, the proposed way possesses a 50% improvement in the predictive accuracy. Generally, such network topology features provide insights into the relationship between protein functions and network architectures. The source code of Matlab is freely available on request from the authors.

  3. Towards benchmarking citizen observatories: Features and functioning of online amateur weather networks.

    Science.gov (United States)

    Gharesifard, Mohammad; Wehn, Uta; van der Zaag, Pieter

    2017-05-15

    Crowd-sourced environmental observations are increasingly being considered as having the potential to enhance the spatial and temporal resolution of current data streams from terrestrial and areal sensors. The rapid diffusion of ICTs during the past decades has facilitated the process of data collection and sharing by the general public and has resulted in the formation of various online environmental citizen observatory networks. Online amateur weather networks are a particular example of such ICT-mediated observatories that are rooted in one of the oldest and most widely practiced citizen science activities, namely amateur weather observation. The objective of this paper is to introduce a conceptual framework that enables a systematic review of the features and functioning of these expanding networks. This is done by considering distinct dimensions, namely the geographic scope and types of participants, the network's establishment mechanism, revenue stream(s), existing communication paradigm, efforts required by data sharers, support offered by platform providers, and issues such as data accessibility, availability and quality. An in-depth understanding of these dimensions helps to analyze various dynamics such as interactions between different stakeholders, motivations to run the networks, and their sustainability. This framework is then utilized to perform a critical review of six existing online amateur weather networks based on publicly available data. The main findings of this analysis suggest that: (1) there are several key stakeholders such as emergency services and local authorities that are not (yet) engaged in these networks; (2) the revenue stream(s) of online amateur weather networks is one of the least discussed but arguably most important dimensions that is crucial for the sustainability of these networks; and (3) all of the networks included in this study have one or more explicit modes of bi-directional communication, however, this is limited to

  4. Disk hernia and spondylolisthesis diagnosis using biomechanical features and neural network.

    Science.gov (United States)

    Oyedotun, Oyebade K; Olaniyi, Ebenezer O; Khashman, Adnan

    2016-01-01

    Artificial neural networks have found applications in various areas of medical diagnosis. The capability of neural networks to learn medical data, mining useful and complex relationships that exist between attributes has earned it a major domain in decision support systems. This paper proposes a fast automatic system for the diagnosis of disk hernia and spondylolisthesis using biomechanical features and neural network. Such systems as described within this work allow the diagnosis of new cases using trained neural networks; patients are classified as either having disk hernia, spondylolisthesis, or normal. Generally, both disk hernia and spondylolisthesis present similar symptoms; hence, diagnosis is prone to inter-misclassification error. This work is significant in that the proposed systems are capable of making fast decisions on such somewhat difficult diagnoses with reasonable accuracies. Feedforward neural network and radial basis function networks are trained on data obtained from a public database. The results obtained within this research are promising and show that neural networks can find applications as efficient and effective expert systems for the diagnosis of disk hernia and spondylolisthesis.

  5. Social inheritance can explain the structure of animal social networks

    Science.gov (United States)

    Ilany, Amiyaal; Akçay, Erol

    2016-01-01

    The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals. But as of yet, no general theory of social network structure exists that can explain the diversity of social networks observed in nature, and serve as a null model for detecting species and population-specific factors. Here we propose a simple and generally applicable model of social network structure. We consider the emergence of network structure as a result of social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly. We compare model output with data from several species, showing that it can generate networks with properties such as those observed in real social systems. Our model demonstrates that important observed properties of social networks, including heritability of network position or assortative associations, can be understood as consequences of social inheritance. PMID:27352101

  6. Social inheritance can explain the structure of animal social networks.

    Science.gov (United States)

    Ilany, Amiyaal; Akçay, Erol

    2016-06-28

    The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals. But as of yet, no general theory of social network structure exists that can explain the diversity of social networks observed in nature, and serve as a null model for detecting species and population-specific factors. Here we propose a simple and generally applicable model of social network structure. We consider the emergence of network structure as a result of social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly. We compare model output with data from several species, showing that it can generate networks with properties such as those observed in real social systems. Our model demonstrates that important observed properties of social networks, including heritability of network position or assortative associations, can be understood as consequences of social inheritance.

  7. PROSPECTS OF REGIONAL NETWORK STRUCTURES IN THE SOUTHERN FEDERAL DISTRICT

    Directory of Open Access Journals (Sweden)

    I. V. Morozov

    2014-01-01

    Full Text Available The article reveals the possibility of the Southern Federal District to form regional network structures. The prospects for the formation of networks in the region in relation to the Olympic Winter Games Sochi 2014.

  8. Exploration of the structural features and magnetic behaviour in a ...

    Indian Academy of Sciences (India)

    of single molecular magnetic (SMM) behaviour of the complex. 4. Conclusion. We have reported here the synthesis and characteriza- tion of a novel Co(II)-based 3D framework. The com- plex is formed of two-dimensional grid-like network which showed inclined interpenetration with other 2D networks giving rise to 2D ...

  9. Unique Structural Features of Influenza Virus H15 Hemagglutinin

    Energy Technology Data Exchange (ETDEWEB)

    Tzarum, Netanel; McBride, Ryan; Nycholat, Corwin M.; Peng, Wenjie; Paulson, James C.; Wilson, Ian A. (Scripps)

    2017-04-12

    Influenza A H15 viruses are members of a subgroup (H7-H10-H15) of group 2 hemagglutinin (HA) subtypes that include H7N9 and H10N8 viruses that were isolated from humans during 2013. The isolation of avian H15 viruses is, however, quite rare and, until recently, geographically restricted to wild shorebirds and waterfowl in Australia. The HAs of H15 viruses contain an insertion in the 150-loop (loop beginning at position 150) of the receptor-binding site common to this subgroup and a unique insertion in the 260-loop compared to any other subtype. Here, we show that the H15 HA has a high preference for avian receptor analogs by glycan array analyses. The H15 HA crystal structure reveals that it is structurally closest to H7N9 HA, but the head domain of the H15 trimer is wider than all other HAs due to a tilt and opening of the HA1 subunits of the head domain. The extended 150-loop of the H15 HA retains the conserved conformation as in H7 and H10 HAs. Furthermore, the elongated 260-loop increases the exposed HA surface and can contribute to antigenic variation in H15 HAs. Since avian-origin H15 HA viruses have been shown to cause enhanced disease in mammalian models, further characterization and immune surveillance of H15 viruses are warranted.

    IMPORTANCEIn the last 2 decades, an apparent increase has been reported for cases of human infection by emerging avian influenza A virus subtypes, including H7N9 and H10N8 viruses isolated during 2013. H15 is the other member of the subgroup of influenza A virus group 2 hemagglutinins (HAs) that also include H7 and H10. H15 viruses have been restricted to Australia, but recent isolation of H15 viruses in western Siberia suggests that they could be spread more globally via the avian flyways that converge and emanate from this region. Here we report on characterization of the three-dimensional structure and receptor specificity of the H15 hemagglutinin, revealing distinct features and specificities that can

  10. Community Structure in Time-Dependent, Multiscale, and Multiplex Networks

    OpenAIRE

    Mucha, Peter J; Richardson, Thomas; Macon, Kevin; Porter, Mason A.; Onnela, Jukka-Pekka

    2009-01-01

    Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly-connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that con...

  11. Building a sense of virtual community: the role of the features of social networking sites.

    Science.gov (United States)

    Chen, Chi-Wen; Lin, Chiun-Sin

    2014-07-01

    In recent years, social networking sites have received increased attention because of the potential of this medium to transform business by building virtual communities. However, theoretical and empirical studies investigating how specific features of social networking sites contribute to building a sense of virtual community (SOVC)-an important dimension of a successful virtual community-are rare. Furthermore, SOVC scales have been developed, and research on this issue has been called for, but few studies have heeded this call. On the basis of prior literature, this study proposes that perceptions of the three most salient features of social networking sites-system quality (SQ), information quality (IQ), and social information exchange (SIE)-play a key role in fostering SOVC. In particular, SQ is proposed to increase IQ and SIE, and SIE is proposed to enhance IQ, both of which thereafter build SOVC. The research model was examined in the context of Facebook, one of the most popular social networking sites in the world. We adopted Blanchard's scales to measure SOVC. Data gathered using a Web-based questionnaire, and analyzed with partial least squares, were utilized to test the model. The results demonstrate that SIE, SQ, and IQ are the factors that form SOVC. The findings also suggest that SQ plays a fundamental role in supporting SIE and IQ in social networking sites. Implications for theory, practice, and future research directions are discussed.

  12. A Decomposition Algorithm for Learning Bayesian Network Structures from Data

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Cordero Hernandez, Jorge

    2008-01-01

    It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....

  13. Inferring the mesoscale structure of layered, edge-valued, and time-varying networks

    Science.gov (United States)

    Peixoto, Tiago P.

    2015-10-01

    Many network systems are composed of interdependent but distinct types of interactions, which cannot be fully understood in isolation. These different types of interactions are often represented as layers, attributes on the edges, or as a time dependence of the network structure. Although they are crucial for a more comprehensive scientific understanding, these representations offer substantial challenges. Namely, it is an open problem how to precisely characterize the large or mesoscale structure of network systems in relation to these additional aspects. Furthermore, the direct incorporation of these features invariably increases the effective dimension of the network description, and hence aggravates the problem of overfitting, i.e., the use of overly complex characterizations that mistake purely random fluctuations for actual structure. In this work, we propose a robust and principled method to tackle these problems, by constructing generative models of modular network structure, incorporating layered, attributed and time-varying properties, as well as a nonparametric Bayesian methodology to infer the parameters from data and select the most appropriate model according to statistical evidence. We show that the method is capable of revealing hidden structure in layered, edge-valued, and time-varying networks, and that the most appropriate level of granularity with respect to the additional dimensions can be reliably identified. We illustrate our approach on a variety of empirical systems, including a social network of physicians, the voting correlations of deputies in the Brazilian national congress, the global airport network, and a proximity network of high-school students.

  14. Phenology drives mutualistic network structure and diversity

    NARCIS (Netherlands)

    Encinas Viso, Francisco; Revilla, Tomas A; Etienne, Rampal S.

    Several network properties have been identified as determinants of the stability and complexity of mutualistic networks. However, it is unclear which mechanisms give rise to these network properties. Phenology seems important, because it shapes the topology of mutualistic networks, but its effects

  15. Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features

    Science.gov (United States)

    Obeso, Abraham Montoya; Benois-Pineau, Jenny; Acosta, Alejandro Álvaro Ramirez; Vázquez, Mireya Saraí García

    2017-01-01

    We propose a convolutional neural network to classify images of buildings using sparse features at the network's input in conjunction with primary color pixel values. As a result, a trained neuronal model is obtained to classify Mexican buildings in three classes according to the architectural styles: prehispanic, colonial, and modern with an accuracy of 88.01%. The problem of poor information in a training dataset is faced due to the unequal availability of cultural material. We propose a data augmentation and oversampling method to solve this problem. The results are encouraging and allow for prefiltering of the content in the search tasks.

  16. Analysis of Ego Network Structure in Online Social Networks

    OpenAIRE

    Arnaboldi, Valerio; Conti, Marco; Passarella, Andrea; Pezzoni, Fabio

    2012-01-01

    Results about offline social networks demonstrated that the social relationships that an individual (ego) maintains with other people (alters) can be organised into different groups according to the ego network model. In this model the ego can be seen as the centre of a series of layers of increasing size. Social relationships between ego and alters in layers close to ego are stronger than those belonging to more external layers. Online Social Networks are becoming a fundamental medium for hu...

  17. Formation Features of the Customer Segments for the Network Organizations in the Smart Era

    Directory of Open Access Journals (Sweden)

    Elena V. Yaroshenko

    2017-01-01

    Full Text Available Modern network society is based on the advances of information era of Smart, connecting information and communication technologies, intellectual resources and new forms of managing in the global electronic space. It leads to domination of network forms of the organization of economic activity. Many experts prove the importance of segmentation process of consumers when developing competitive strategy of the organization. Every company needs a competent segmentation of the customer base, allowing to concentrate the attention on satisfaction of requirements of the most perspective client segments. The network organizations have specific characteristics; therefore, it is important to understand how they can influence on the formation of client profiles. It causes the necessity of the network organizations’ research in terms of management of high-profitable client segments.The aim of this study is to determine the characteristics of the market segmentation and to choose the key customers for the network organizations. This purpose has defined the statement and the solution of the following tasks: to explore characteristic features of the network forms of the organization of economic activity of the companies, their prospects, Smart technologies’ influence on them; to reveal the work importance with different client profiles; to explore the existing methods and tools of formation of key customer segments; to define criteria for selection of key groups; to reveal the characteristics of customer segments’ formation for the network organizations.In the research process, methods of the system analysis, a method of analogies, methods of generalizations, a method of the expert evaluations, methods of classification and clustering were applied.This paper explores the characteristics and principles of functioning of network organizations, the appearance of which is directly linked with the development of Smart society. It shows the influence on the

  18. Electrochemically Smart Bimetallic Materials Featuring Group 11 Metals: In-situ Conductive Network Generation and Its Impact on Cell Capacity

    Energy Technology Data Exchange (ETDEWEB)

    Takeuchi, Esther [Stony Brook Univ., NY (United States)

    2016-11-30

    Our results for this program “Electrochemically smart bimetallic materials featuring Group 11 metals: in-situ conductive matrix generation and its impact on battery capacity, power and reversibility” have been highly successful: 1) we demonstrated material structures which generated in-situ conductive networks through electrochemical activation with increases in conductivity up to 10,000 fold, 2) we pioneered in situ analytical methodology to map the cathodes at several stages of discharge through the use of Energy Dispersive X-ray Diffraction (EDXRD) to elucidate the kinetic dependence of the conductive network formation, and 3) we successfully designed synthetic methodology for direct control of material properties including crystallite size and surface area which showed significant impact on electrochemical behavior.

  19. Structural equation models from paths to networks

    CERN Document Server

    Westland, J Christopher

    2015-01-01

    This compact reference surveys the full range of available structural equation modeling (SEM) methodologies.  It reviews applications in a broad range of disciplines, particularly in the social sciences where many key concepts are not directly observable.  This is the first book to present SEM’s development in its proper historical context–essential to understanding the application, strengths and weaknesses of each particular method.  This book also surveys the emerging path and network approaches that complement and enhance SEM, and that will grow in importance in the near future.  SEM’s ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists.  Latent variable theory and application are comprehensively explained, and methods are presented for extending their power, including guidelines for data preparation, sample size calculation, and the special treatment of Likert scale data.  Tables of software, methodologies and fit st...

  20. Preference of echo features for classification of seafloor sediments using neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    De, C.; Chakraborty, B.

    ) proposed a simple data-driven technique with fuzzy c-means algorithm to select a set of three most discriminating echo features. The study (De and Chakraborty, 2009) reveals that three sets of echo features namely [BS, TS, StatSkew], [BS, TS, HD... architecture and fuzzy algorithm. IEEE Geoscience and Remote Sensing Letters 6:743-747. Ghosh, J., L. M. Deuser, and S. D. Beck. 1992. A neural network based hybrid system for detection, characterization, and classification of short-duration oceanic...

  1. Features of vibrations of structural inhomogeneous solid media

    Science.gov (United States)

    Karimbaev, Telman; Baishagirov, Khairulla; Nurgaliyeva, Saltanat

    2017-09-01

    Homogeneous or quasi-homogeneous classic models of deformation are generally used at a mathematical de-scription of deformation composite materials (CM). These theories, however, are limited within initial conditions and do not cover the most important properties of CM: heterogeneity of inertia and elasticity of components, their interaction when deforming, etc. Among the models that complement the classical theories, it is possible to allocate the so-called theory of mixture where CM is considered as two (or more) interacting homogeneous continuum. Therefore, the model increase of motion freedom degrees of the particles of such heterogeneous medium allows each component of CM to show their inertial properties. This leads to the identification of such exclusive features as "bifurcation of fre-quencies", i.e., to description of motion on each form of normal modes at two different frequencies. In the research this phenomenon was investigated by the analysis of biquadratic equation obtained at solving the proper value problem of heterogeneous medium, and was verified by testing the normal mode of frequencies of blades made of CM. In the particular case there was received a simple calculation formula for determination of bar technical frequencies from CM on the basis of the characteristic equation of natural vibrations. In this case the numerical results for the lower forms of the vibration normal mode coincide with the experimental data for homogeneous medium. The characteristic equation contains the introduced physical parameters of the two-component theory; therefore, it describes the other forms of vibration. However, in this case the freedom of structural fluctuations of CM each component is limited to their coupling and interaction, providing continuity and shared the compound materials.

  2. Large-scale identification of potential drug targets based on the topological features of human protein-protein interaction network.

    Science.gov (United States)

    Li, Zhan-Chao; Zhong, Wen-Qian; Liu, Zhi-Qing; Huang, Meng-Hua; Xie, Yun; Dai, Zong; Zou, Xiao-Yong

    2015-04-29

    Identifying potential drug target proteins is a crucial step in the process of drug discovery and plays a key role in the study of the molecular mechanisms of disease. Based on the fact that the majority of proteins exert their functions through interacting with each other, we propose a method to recognize target proteins by using the human protein-protein interaction network and graph theory. In the network, vertexes and edges are weighted by using the confidence scores of interactions and descriptors of protein primary structure, respectively. The novel network topological features are defined and employed to characterize protein using existing databases. A widely used minimum redundancy maximum relevance and random forests algorithm are utilized to select the optimal feature subset and construct model for the identification of potential drug target proteins at the proteome scale. The accuracies of training set and test set are 89.55% and 85.23%. Using the constructed model, 2127 potential drug target proteins have been recognized and 156 drug target proteins have been validated in the database of drug target. In addition, some new drug target proteins can be considered as targets for treating diseases of mucopolysaccharidosis, non-arteritic anterior ischemic optic neuropathy, Bernard-Soulier syndrome and pseudo-von Willebrand, etc. It is anticipated that the proposed method may became a powerful high-throughput virtual screening tool of drug target. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. Epidemic spreading on complex networks with community structures

    CERN Document Server

    Stegehuis, Clara; van Leeuwaarden, Johan S H

    2016-01-01

    Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, while the other model only preserves the set of communities and the vertex degrees. These models show that community structure is an important determinant of the behavior of percolation processes on networks, such as information diffusion or virus spreading: the community structure can both \\textit{enforce} as well as \\textit{inhibit} diffusion processes. Our models further show that it is the mesoscopic set of communities that matters. The exact internal structures of communities barely influence the behavior of percolation processes across networks. This insensitivity is likely due to the relative denseness of the communities.

  4. Scene Classification of Remote Sensing Image Based on Multi-scale Feature and Deep Neural Network

    Directory of Open Access Journals (Sweden)

    XU Suhui

    2016-07-01

    Full Text Available Aiming at low precision of remote sensing image scene classification owing to small sample sizes, a new classification approach is proposed based on multi-scale deep convolutional neural network (MS-DCNN, which is composed of nonsubsampled Contourlet transform (NSCT, deep convolutional neural network (DCNN, and multiple-kernel support vector machine (MKSVM. Firstly, remote sensing image multi-scale decomposition is conducted via NSCT. Secondly, the decomposing high frequency and low frequency subbands are trained by DCNN to obtain image features in different scales. Finally, MKSVM is adopted to integrate multi-scale image features and implement remote sensing image scene classification. The experiment results in the standard image classification data sets indicate that the proposed approach obtains great classification effect due to combining the recognition superiority to different scenes of low frequency and high frequency subbands.

  5. Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network.

    Science.gov (United States)

    Jahidin, A H; Megat Ali, M S A; Taib, M N; Tahir, N Md; Yassin, I M; Lias, S

    2014-04-01

    This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  6. A mathematical model for networks with structures in the mesoscale

    OpenAIRE

    Criado, Regino; Flores, Julio; Gacia Del Amo, Alejandro Jose; Gómez, Jesus; Romance, Miguel

    2011-01-01

    Abstract The new concept of multilevel network is introduced in order to embody some topological properties of complex systems with structures in the mesoscale which are not completely captured by the classical models. This new model, which generalizes the hyper-network and hyper-structure models, fits perfectly with several real-life complex systems, including social and public transportation networks. We present an analysis of the structural properties of the mu...

  7. Classification of Human Emotions from EEG Signals using Statistical Features and Neural Network

    OpenAIRE

    Chai Tong Yuen; Woo San San; Tan Ching Seong; Mohamed Rizon

    2009-01-01

    A statistical based system for human emotions classification by using electroencephalogram (EEG) is proposed in this paper. The data used in this study is acquired using EEG and the emotions are elicited from six human subjects under the effect of emotion stimuli. This paper also proposed an emotion stimulation experiment using visual stimuli. From the EEG data, a total of six statistical features are computed and back-propagation neural network is applied for the classification of human emot...

  8. Evolving networks-Using past structure to predict the future

    Science.gov (United States)

    Shang, Ke-ke; Yan, Wei-sheng; Small, Michael

    2016-08-01

    Many previous studies on link prediction have focused on using common neighbors to predict the existence of links between pairs of nodes. More broadly, research into the structural properties of evolving temporal networks and temporal link prediction methods have recently attracted increasing attention. In this study, for the first time, we examine the use of links between a pair of nodes to predict their common neighbors and analyze the relationship between the weight and the structure in static networks, evolving networks, and in the corresponding randomized networks. We propose both new unweighted and weighted prediction methods and use six kinds of real networks to test our algorithms. In unweighted networks, we find that if a pair of nodes connect to each other in the current network, they will have a higher probability to connect common nodes both in the current and the future networks-and the probability will decrease with the increase of the number of neighbors. Furthermore, we find that the original networks have their particular structure and statistical characteristics which benefit link prediction. In weighted networks, the prediction algorithm performance of networks which are dominated by human factors decrease with the decrease of weight and are in general better in static networks. Furthermore, we find that geographical position and link weight both have significant influence on the transport network. Moreover, the evolving financial network has the lowest predictability. In addition, we find that the structure of non-social networks has more robustness than social networks. The structure of engineering networks has both best predictability and also robustness.

  9. Network nestedness as generalized core-periphery structures

    CERN Document Server

    Lee, Sang Hoon

    2016-01-01

    The concept of nestedness, in particular for ecological and economical networks, has been introduced as a structural characteristic of real interacting systems. We suggest that the nestedness is in fact another way to express a mesoscale network property called the core-periphery structure. With real ecological mutualistic networks and synthetic model networks, we reveal the strong correlation between the nestedness and core-peripheriness, by defining the network-level measures for nestedness and core-peripheriness in case of weighted and bipartite networks. However, at the same time, via more sophisticated null-model analysis, we also discover that the degree (the number of connected neighbors of a node) distribution poses quite severe restrictions on the possible nestedness and core-peripheriness parameter space. Therefore, there must exist structurally interwoven properties in more fundamental levels of network formation, behind this seemingly obvious relation between nestedness and core-periphery structur...

  10. Bearing performance degradation assessment based on time-frequency code features and SOM network

    Science.gov (United States)

    Zhang, Yan; Tang, Baoping; Han, Yan; Deng, Lei

    2017-04-01

    Bearing performance degradation assessment and prognostics are extremely important in supporting maintenance decision and guaranteeing the system’s reliability. To achieve this goal, this paper proposes a novel feature extraction method for the degradation assessment and prognostics of bearings. Features of time-frequency codes (TFCs) are extracted from the time-frequency distribution using a hybrid procedure based on short-time Fourier transform (STFT) and non-negative matrix factorization (NMF) theory. An alternative way to design the health indicator is investigated by quantifying the similarity between feature vectors using a self-organizing map (SOM) network. On the basis of this idea, a new health indicator called time-frequency code quantification error (TFCQE) is proposed to assess the performance degradation of the bearing. This indicator is constructed based on the bearing real-time behavior and the SOM model that is previously trained with only the TFC vectors under the normal condition. Vibration signals collected from the bearing run-to-failure tests are used to validate the developed method. The comparison results demonstrate the superiority of the proposed TFCQE indicator over many other traditional features in terms of feature quality metrics, incipient degradation identification and achieving accurate prediction. Highlights • Time-frequency codes are extracted to reflect the signals’ characteristics. • SOM network served as a tool to quantify the similarity between feature vectors. • A new health indicator is proposed to demonstrate the whole stage of degradation development. • The method is useful for extracting the degradation features and detecting the incipient degradation. • The superiority of the proposed method is verified using experimental data.

  11. Studies on the Structure and Dynamics of Urban Bus Networks in Indian Cities

    CERN Document Server

    Chatterjee, Atanu

    2015-01-01

    In recent times, the domain of network science has become extremely useful in understanding the underlying structure of various real-world networks and to answer non-trivial questions regarding them. In this study, we rigourously analyze the statistical properties of the bus networks of six major Indian cities as graphs in L- and P-space, using tools from network science. Although public transport networks, such as airline and railway networks have been extensively studied, a comprehensive study on the structure and growth of bus networks is lacking. In India, where bus networks play an important role in day-to-day commutation, it is of significant interest to analyze their topological structure, and answer some of the basic questions on their evolution, growth, robustness and resiliency. We start from an empirical analysis of these networks, and determine their principle characteristics in terms of the complex network theory. The common features of small-world property and heavy tails in degree-distribution ...

  12. Network-level structural covariance in the developing brain.

    Science.gov (United States)

    Zielinski, Brandon A; Gennatas, Efstathios D; Zhou, Juan; Seeley, William W

    2010-10-19

    Intrinsic or resting state functional connectivity MRI and structural covariance MRI have begun to reveal the adult human brain's multiple network architectures. How and when these networks emerge during development remains unclear, but understanding ontogeny could shed light on network function and dysfunction. In this study, we applied structural covariance MRI techniques to 300 children in four age categories (early childhood, 5-8 y; late childhood, 8.5-11 y; early adolescence, 12-14 y; late adolescence, 16-18 y) to characterize gray matter structural relationships between cortical nodes that make up large-scale functional networks. Network nodes identified from eight widely replicated functional intrinsic connectivity networks served as seed regions to map whole-brain structural covariance patterns in each age group. In general, structural covariance in the youngest age group was limited to seed and contralateral homologous regions. Networks derived using primary sensory and motor cortex seeds were already well-developed in early childhood but expanded in early adolescence before pruning to a more restricted topology resembling adult intrinsic connectivity network patterns. In contrast, language, social-emotional, and other cognitive networks were relatively undeveloped in younger age groups and showed increasingly distributed topology in older children. The so-called default-mode network provided a notable exception, following a developmental trajectory more similar to the primary sensorimotor systems. Relationships between functional maturation and structural covariance networks topology warrant future exploration.

  13. 3D FEATURE POINT EXTRACTION FROM LIDAR DATA USING A NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Y. Feng

    2016-06-01

    Full Text Available Accurate positioning of vehicles plays an important role in autonomous driving. In our previous research on landmark-based positioning, poles were extracted both from reference data and online sensor data, which were then matched to improve the positioning accuracy of the vehicles. However, there are environments which contain only a limited number of poles. 3D feature points are one of the proper alternatives to be used as landmarks. They can be assumed to be present in the environment, independent of certain object classes. To match the LiDAR data online to another LiDAR derived reference dataset, the extraction of 3D feature points is an essential step. In this paper, we address the problem of 3D feature point extraction from LiDAR datasets. Instead of hand-crafting a 3D feature point extractor, we propose to train it using a neural network. In this approach, a set of candidates for the 3D feature points is firstly detected by the Shi-Tomasi corner detector on the range images of the LiDAR point cloud. Using a back propagation algorithm for the training, the artificial neural network is capable of predicting feature points from these corner candidates. The training considers not only the shape of each corner candidate on 2D range images, but also their 3D features such as the curvature value and surface normal value in z axis, which are calculated directly based on the LiDAR point cloud. Subsequently the extracted feature points on the 2D range images are retrieved in the 3D scene. The 3D feature points extracted by this approach are generally distinctive in the 3D space. Our test shows that the proposed method is capable of providing a sufficient number of repeatable 3D feature points for the matching task. The feature points extracted by this approach have great potential to be used as landmarks for a better localization of vehicles.

  14. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

  15. Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae.

    Science.gov (United States)

    Mirzarezaee, Mitra; Araabi, Babak N; Sadeghi, Mehdi

    2010-12-19

    It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes. We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%. We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of Saccharomyces Cerevisiae's proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the possibility of predicting non-hubs, party hubs and date hubs

  16. Effective feature selection of clinical and genetic to predict warfarin dose using artificial neural network

    Directory of Open Access Journals (Sweden)

    Mohammad Karim Sohrabi

    2016-03-01

    Full Text Available Background: Warfarin is one of the most common oral anticoagulant, which role is to prevent the clots. The dose of this medicine is very important because changes can be dangerous for patients. Diagnosis is difficult for physicians because increase and decrease in use of warfarin is so dangerous for patients. Identifying the clinical and genetic features involved in determining dose could be useful to predict using data mining techniques. The aim of this paper is to provide a convenient way to select the clinical and genetic features to determine the dose of warfarin using artificial neural networks (ANN and evaluate it in order to predict the dose patients. Methods: This experimental study, was investigate from April to May 2014 on 552 patients in Tehran Heart Center Hospital (THC candidates for warfarin anticoagulant therapy within the international normalized ratio (INR therapeutic target. Factors affecting the dose include clinical characteristics and genetic extracted, and different methods of feature selection based on genetic algorithm and particle swarm optimization (PSO and evaluation function neural networks in MATLAB (MathWorks, MA, USA, were performed. Results: Between algorithms used, particle swarm optimization algorithm accuracy was more appropriate, for the mean square error (MSE, root mean square error (RMSE and mean absolute error (MAE were 0.0262, 0.1621 and 0.1164, respectively. Conclusion: In this article, the most important characteristics were identified using methods of feature selection and the stable dose had been predicted based on artificial neural networks. The output is acceptable and with less features, it is possible to achieve the prediction warfarin dose accurately. Since the prescribed dose for the patients is important, the output of the obtained model can be used as a decision support system.

  17. Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection

    Science.gov (United States)

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-03-01

    Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by

  18. Kinematic Structural Modelling in Bayesian Networks

    Science.gov (United States)

    Schaaf, Alexander; de la Varga, Miguel; Florian Wellmann, J.

    2017-04-01

    We commonly capture our knowledge about the spatial distribution of distinct geological lithologies in the form of 3-D geological models. Several methods exist to create these models, each with its own strengths and limitations. We present here an approach to combine the functionalities of two modeling approaches - implicit interpolation and kinematic modelling methods - into one framework, while explicitly considering parameter uncertainties and thus model uncertainty. In recent work, we proposed an approach to implement implicit modelling algorithms into Bayesian networks. This was done to address the issues of input data uncertainty and integration of geological information from varying sources in the form of geological likelihood functions. However, one general shortcoming of implicit methods is that they usually do not take any physical constraints into consideration, which can result in unrealistic model outcomes and artifacts. On the other hand, kinematic structural modelling intends to reconstruct the history of a geological system based on physically driven kinematic events. This type of modelling incorporates simplified, physical laws into the model, at the cost of a substantial increment of usable uncertain parameters. In the work presented here, we show an integration of these two different modelling methodologies, taking advantage of the strengths of both of them. First, we treat the two types of models separately, capturing the information contained in the kinematic models and their specific parameters in the form of likelihood functions, in order to use them in the implicit modelling scheme. We then go further and combine the two modelling approaches into one single Bayesian network. This enables the direct flow of information between the parameters of the kinematic modelling step and the implicit modelling step and links the exclusive input data and likelihoods of the two different modelling algorithms into one probabilistic inference framework. In

  19. Reverse Logistics Network Structures and Design

    NARCIS (Netherlands)

    M. Fleischmann (Moritz)

    2001-01-01

    textabstractLogistics network design is commonly recognized as a strategic supply chain issue of prime importance. The location of production facilities, storage concepts, and transportation strategies are major determinants of supply chain performance. This chapter considers logistics network

  20. Loss of integrity and atrophy in cingulate structural covariance networks in Parkinson's disease.

    Science.gov (United States)

    de Schipper, Laura J; van der Grond, Jeroen; Marinus, Johan; Henselmans, Johanna M L; van Hilten, Jacobus J

    2017-01-01

    In Parkinson's disease (PD), the relation between cortical brain atrophy on MRI and clinical progression is not straightforward. Determination of changes in structural covariance networks - patterns of covariance in grey matter density - has shown to be a valuable technique to detect subtle grey matter variations. We evaluated how structural network integrity in PD is related to clinical data. 3 Tesla MRI was performed in 159 PD patients. We used nine standardized structural covariance networks identified in 370 healthy subjects as a template in the analysis of the PD data. Clinical assessment comprised motor features (Movement Disorder Society-Unified Parkinson's Disease Rating Scale; MDS-UPDRS motor scale) and predominantly non-dopaminergic features (SEverity of Non-dopaminergic Symptoms in Parkinson's Disease; SENS-PD scale: postural instability and gait difficulty, psychotic symptoms, excessive daytime sleepiness, autonomic dysfunction, cognitive impairment and depressive symptoms). Voxel-based analyses were performed within networks significantly associated with PD. The anterior and posterior cingulate network showed decreased integrity, associated with the SENS-PD score, p = 0.001 (β = - 0.265, ηp2 = 0.070) and p = 0.001 (β = - 0.264, ηp2 = 0.074), respectively. Of the components of the SENS-PD score, cognitive impairment and excessive daytime sleepiness were associated with atrophy within both networks. We identified loss of integrity and atrophy in the anterior and posterior cingulate networks in PD patients. Abnormalities of both networks were associated with predominantly non-dopaminergic features, specifically cognition and excessive daytime sleepiness. Our findings suggest that (components of) the cingulate networks display a specific vulnerability to the pathobiology of PD and may operate as interfaces between networks involved in cognition and alertness.

  1. The structure of replicating kinetoplast DNA networks

    OpenAIRE

    1993-01-01

    Kinetoplast DNA (kDNA), the mitochondrial DNA of Crithidia fasciculata and related trypanosomatids, is a network containing approximately 5,000 covalently closed minicircles which are topologically interlocked. kDNA synthesis involves release of covalently closed minicircles from the network, and, after replication of the free minicircles, reattachment of the nicked or gapped progeny minicircles to the network periphery. We have investigated this process by electron microscopy of networks at ...

  2. Multilabel user classification using the community structure of online networks.

    Science.gov (United States)

    Rizos, Georgios; Papadopoulos, Symeon; Kompatsiaris, Yiannis

    2017-01-01

    We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  3. Multilabel user classification using the community structure of online networks.

    Directory of Open Access Journals (Sweden)

    Georgios Rizos

    Full Text Available We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE, an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  4. Structural Antecedents of Corporate Network Evolution

    NARCIS (Netherlands)

    F.H. Wijen (Frank); N. Noorderhaven (Niels); W. Vanhaverbeke (Wim)

    2011-01-01

    textabstractAbstract: While most network studies adopt a static view, we argue that corporate social networks are subject to endogenous dynamics of cognitive path dependence and self-reinforcing power relations. Over time, these dynamics drive corporate networks to become increasingly focused (i.e.,

  5. NIRS feature extraction based on deep auto-encoder neural network

    Science.gov (United States)

    Liu, Ting; Li, Zhongren; Yu, Chunxia; Qin, Yuhua

    2017-12-01

    As a secondary analysis method, Near Infrared Spectroscopy (NIRS) needs an effective feature extraction method to improve the model performance. Deep auto-encoder (DAE) can build up an adaptive multilayer encoder network to transform the high-dimensional data into a low-dimensional code with both linear and nonlinear feature combinations. To evaluate its capability, we experimented on the spectra data obtained from different categories of cigarette with the method of DAE, and compared with the principal component analysis (PCA). The results showed that the DAE can extract more nonlinear features to characterize cigarette quality. In addition, the DAE also got the linear distribution of cigarette quality by its nonlinear transformation of features. Finally, we employed k-Nearest Neighbor (kNN) to classify different categories of cigarette with the features extracted by the linear transformation methods as PCA and wavelet transform-principal component analysis (WT-PCA), and the nonlinear transformation methods as DAE and isometric mapping (ISOMAP). The results showed that the pattern recognition mode built on features extracted by DAE was provided with more validity.

  6. Health and the Structure of Adolescent Social Networks

    Science.gov (United States)

    Haas, Steven A.; Schaefer, David R.; Kornienko, Olga

    2010-01-01

    Much research has explored the role of social networks in promoting health through the provision of social support. However, little work has examined how social networks themselves may be structured by health. This article investigates the link between individuals' health and the characteristics of their social network positions.We first develop…

  7. Stable and emergent network topologies : A structural approach

    NARCIS (Netherlands)

    Herman Monsuur

    2007-01-01

    Economic, social and military networks have at least one thing in common: they change over time. For various reasons, nodes form and terminate links, thereby rearranging the network. In this paper, we present a structural network mechanism that formalizes a possible incentive that guides nodes in

  8. Teledesic Global Wireless Broadband Network: Space Infrastructure Architecture, Design Features and Technologies

    Science.gov (United States)

    Stuart, James R.

    1995-01-01

    The Teledesic satellites are a new class of small satellites which demonstrate the important commercial benefits of using technologies developed for other purposes by U.S. National Laboratories. The Teledesic satellite architecture, subsystem design features, and new technologies are described. The new Teledesic satellite manufacturing, integration, and test approaches which use modern high volume production techniques and result in surprisingly low space segment costs are discussed. The constellation control and management features and attendant software architecture features are addressed. After briefly discussing the economic and technological impact on the USA commercial space industries of the space communications revolution and such large constellation projects, the paper concludes with observations on the trend toward future system architectures using networked groups of much smaller satellites.

  9. Social features of online networks: the strength of weak ties in online social media

    CERN Document Server

    Grabowicz, Przemyslaw A; Moro, Esteban; Pujol, Josep; Eguiluz, Victor M

    2011-01-01

    An increasing fraction of today social interactions occurs using online social media as communication channels. Recent worldwide events, such as the revolts in Middle East or the recent social movements in Spain, reflect their capacity to boost people coordination. Online networks display, in general, a rich internal structure where users can choose among different types and intensity of interactions. Despite of this and their growing popularity, there are still open questions regarding the social value of online relations. For example, the existence of users with thousands or millions of online friends sheds doubts on the relevance of these connections. In this work, we focus on Twitter, one of the most popular online social networks, and find that the network formed by the basic type of relations is organized in groups. The activity of the users in the network is related to the landscape determined by such groups. Twitter's distinction between different types o interactions allows us to establish a parallel...

  10. Exploiting The Brain’s Network Structure in Identifying ADHD

    Directory of Open Access Journals (Sweden)

    Soumyabrata eDey

    2012-11-01

    Full Text Available Attention Deficit Hyperactive Disorder (ADHD is a common behavioral problem affecting children. In this work, we investigate the automatic classification of ADHD subjects using the resting state Functional Magnetic Resonance Imaging (fMRI sequences of the brain. We show that brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We compute the pairwise correlation of brain voxels' activity over the time frame of the experimental protocol which helps to model the function of a brain as a network. Different network features are computed for each of the voxels constructing the network. The concatenation of the network features of all the voxels in a brain serves as the feature vector. Feature vectors from a set of subjects are then used to train a PCA-LDA (principal component analysis-linear discriminant analysis based classifier. We hypothesized that ADHD related differences lie in some specific regions of brain and using features only from those regions are sufficient to discriminate ADHD and control subjects. We propose a method to create a brain mask which includes the useful regions only and demonstrate that using the feature from the masked regions improves classification accuracy on the test data set. We train our classifier with 776 subjects, and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge. We demonstrate the utility of graph-motif features, specifically the maps that represent the frequency of participation of voxels in network cycles of length 3. The best classification performance (69.59% is achieved using 3-cycle map features with masking. Our proposed approach holds promise in being able to diagnose and understand the disorder.

  11. nRC: non-coding RNA Classifier based on structural features.

    Science.gov (United States)

    Fiannaca, Antonino; La Rosa, Massimo; La Paglia, Laura; Rizzo, Riccardo; Urso, Alfonso

    2017-01-01

    Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs. In this work, we introduce a new ncRNA classification tool, nRC (non-coding RNA Classifier). Our approach is based on features extraction from the ncRNA secondary structure together with a supervised classification algorithm implementing a deep learning architecture based on convolutional neural networks. We tested our approach for the classification of 13 different ncRNA classes. We obtained classification scores, using the most common statistical measures. In particular, we reach an accuracy and sensitivity score of about 74%. The proposed method outperforms other similar classification methods based on secondary structure features and machine learning algorithms, including the RNAcon tool that, to date, is the reference classifier. nRC tool is freely available as a docker image at https://hub.docker.com/r/tblab/nrc/. The source code of nRC tool is also available at https://github.com/IcarPA-TBlab/nrc.

  12. Error and attack tolerance of synchronization in Hindmarsh–Rose neural networks with community structure

    Energy Technology Data Exchange (ETDEWEB)

    Li, Chun-Hsien, E-mail: chli@nknucc.nknu.edu.tw [Department of Mathematics, National Kaohsiung Normal University, Yanchao District, Kaohsiung City 82444, Taiwan (China); Yang, Suh-Yuh, E-mail: syyang@math.ncu.edu.tw [Department of Mathematics, National Central University, Jhongli City, Taoyuan County 32001, Taiwan (China)

    2014-03-01

    Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh–Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.

  13. Error and attack tolerance of synchronization in Hindmarsh-Rose neural networks with community structure

    Science.gov (United States)

    Li, Chun-Hsien; Yang, Suh-Yuh

    2014-03-01

    Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh-Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.

  14. Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Youjun Li

    2017-10-01

    Full Text Available The aim of this study is to recognize human emotions by electroencephalographic (EEG signals. The innovation of our research methods involves two aspects: First, we integrate the spatial characteristics, frequency domain, and temporal characteristics of the EEG signals, and map them to a two-dimensional image. With these images, we build a series of EEG Multidimensional Feature Image (EEG MFI sequences to represent the emotion variation with EEG signals. Second, we construct a hybrid deep neural network to deal with the EEG MFI sequences to recognize human emotional states where the hybrid deep neural network combined the Convolution Neural Networks (CNN and Long Short-Term-Memory (LSTM Recurrent Neural Networks (RNN. Empirical research is carried out with the open-source dataset DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals using our method, and the results demonstrate the significant improvements over current state-of-the-art approaches in this field. The average emotion classification accuracy of each subject with CLRNN (the hybrid neural networks that we proposed in this study is 75.21%.

  15. The National Biomedical Communications Network as a Developing Structure *

    Science.gov (United States)

    Davis, Ruth M.

    1971-01-01

    The National Biomedical Communications Network has evolved both from a set of conceptual recommendations over the last twelve years and an accumulation of needs manifesting themselves in the requests of members of the medical community. With a short history of three years this network and its developing structure have exhibited most of the stresses of technology interfacing with customer groups, and of a structure attempting to build itself upon many existing fragmentary unconnected segments of a potentially viable resourcesharing capability. In addition to addressing these topics, the paper treats a design appropriate to any network devoted to information transfer in a special interest user community. It discusses fundamentals of network design, highlighting that network structure most appropriate to a national information network. Examples are given of cost analyses of information services and certain conjectures are offered concerning the roles of national networks. PMID:5542912

  16. Random field Ising model and community structure in complex networks

    Science.gov (United States)

    Son, S.-W.; Jeong, H.; Noh, J. D.

    2006-04-01

    We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)

  17. Structural factoring approach for analyzing stochastic networks

    Science.gov (United States)

    Hayhurst, Kelly J.; Shier, Douglas R.

    1991-01-01

    The problem of finding the distribution of the shortest path length through a stochastic network is investigated. A general algorithm for determining the exact distribution of the shortest path length is developed based on the concept of conditional factoring, in which a directed, stochastic network is decomposed into an equivalent set of smaller, generally less complex subnetworks. Several network constructs are identified and exploited to reduce significantly the computational effort required to solve a network problem relative to complete enumeration. This algorithm can be applied to two important classes of stochastic path problems: determining the critical path distribution for acyclic networks and the exact two-terminal reliability for probabilistic networks. Computational experience with the algorithm was encouraging and allowed the exact solution of networks that have been previously analyzed only by approximation techniques.

  18. GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

    Directory of Open Access Journals (Sweden)

    Beiko Robert G

    2005-02-01

    Full Text Available Abstract Background The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence- and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results GANN (available at http://bioinformatics.org.au/gann is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

  19. Robust feature tracking for endoscopic pose estimation and structure recovery

    Science.gov (United States)

    Speidel, S.; Krappe, S.; Röhl, S.; Bodenstedt, S.; Müller-Stich, B.; Dillmann, R.

    2013-03-01

    Minimally invasive surgery is a highly complex medical discipline with several difficulties for the surgeon. To alleviate these difficulties, augmented reality can be used for intraoperative assistance. For visualization, the endoscope pose must be known which can be acquired with a SLAM (Simultaneous Localization and Mapping) approach using the endoscopic images. In this paper we focus on feature tracking for SLAM in minimally invasive surgery. Robust feature tracking and minimization of false correspondences is crucial for localizing the endoscope. As sensory input we use a stereo endoscope and evaluate different feature types in a developed SLAM framework. The accuracy of the endoscope pose estimation is validated with synthetic and ex vivo data. Furthermore we test the approach with in vivo image sequences from da Vinci interventions.

  20. How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection.

    Science.gov (United States)

    Bui-Xuan, Binh-Minh; Jones, Nick S

    2014-10-08

    By considering the task of finding the shortest walk through a Network, we find an algorithm for which the run time is not as O(2 n ), with n being the number of nodes, but instead scales with the number of nodes in a coarsened network. This coarsened network has a number of nodes related to the number of dense regions in the original graph. Since we exploit a form of local community detection as a preprocessing, this work gives support to the project of developing heuristic algorithms for detecting dense regions in networks: preprocessing of this kind can accelerate optimization tasks on networks. Our work also suggests a class of empirical conjectures for how structural features of efficient networked systems might scale with system size.

  1. Automatic detection of lung nodules: false positive reduction using convolution neural networks and handcrafted features

    Science.gov (United States)

    Fu, Ling; Ma, Jingchen; Ren, Yacheng; Han, Youn Seon; Zhao, Jun

    2017-03-01

    Lung cancer is the leading cause of cancer deaths worldwide. Early diagnosis is critical in increasing the 5-year survival rate of lung cancer, so the efficient and accurate detection of lung nodules, potential precursors to lung cancer, is evermore important. In this paper, a computer-aided lung nodule detection system using convolution neural networks (CNN) and handcrafted features for false positive reduction is developed. The CNNs were trained with three types of images: lung CT images, their nodule-enhanced images, and their blood vessel-enhanced images. For each nodule candidate, nine 2D patches from differently oriented planes were extracted from each type of images. Patches of the same orientation from the same type of image across different candidates were used to train the CNNs independently, which were used to extract 864 features. 88 handcrafted features including intensity, shape, and texture features were also obtained from the lung CT images. The CNN features and handcrafted features were then combined to train a classifier, and a support vector machine was adopted to achieve the final classification results. The proposed method was evaluated on 1004 CT scans from the LIDC-IDRI database using 10-fold cross-validation. Compared with the traditional CNN method using only lung CT images, the proposed method boosted the sensitivity of nodule detection from 89.0% to 90.9% at 4 FPs/scan and from 71.6% to 78.2% at 1 FP/scan. This indicates that a combination of handcrafted features and CNN features from both lung CT images and enhanced images is a promising method for lung nodule detection.

  2. Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks

    Science.gov (United States)

    Hosseini, S. M. Hadi; Kesler, Shelli R.

    2013-01-01

    In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures. PMID:23840672

  3. Comparative study on the topological structure of China Education Network

    Science.gov (United States)

    Yu, Ming-Min; Zhang, Ning; Mao, Guo-Yong

    2017-07-01

    China Education Network (CEN) of year 2014 was studied as a complex network object. By searching the domain of “.edu.cn” and filtering some unexpected results, we finally get a network with 14,100,628 pages and 213,513,401 links. The topology of this network was studied to get the features such as out-degree distribution, in-degree distribution and average shortest path length. These features were compared with that of year 2007 and 2004 to observe the evolution mechanisms of CEN. According to the statistical results, it is found that some topology features of CEN such as out-degree distribution, in-degree distribution and average shortest path have changed a lot and the related reasons for these changes are given in this paper.

  4. Multi-threshold white matter structural networks fusion for accurate diagnosis of Tourette syndrome children

    Science.gov (United States)

    Wen, Hongwei; Liu, Yue; Wang, Shengpei; Li, Zuoyong; Zhang, Jishui; Peng, Yun; He, Huiguang

    2017-03-01

    Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. To date, TS is still misdiagnosed due to its varied presentation and lacking of obvious clinical symptoms. Therefore, studies of objective imaging biomarkers are of great importance for early TS diagnosis. As tic generation has been linked to disturbed structural networks, and many efforts have been made recently to investigate brain functional or structural networks using machine learning methods, for the purpose of disease diagnosis. However, few studies were related to TS and some drawbacks still existed in them. Therefore, we propose a novel classification framework integrating a multi-threshold strategy and a network fusion scheme to address the preexisting drawbacks. Here we used diffusion MRI probabilistic tractography to construct the structural networks of 44 TS children and 48 healthy children. We ameliorated the similarity network fusion algorithm specially to fuse the multi-threshold structural networks. Graph theoretical analysis was then implemented, and nodal degree, nodal efficiency and nodal betweenness centrality were selected as features. Finally, support vector machine recursive feature extraction (SVM-RFE) algorithm was used for feature selection, and then optimal features are fed into SVM to automatically discriminate TS children from controls. We achieved a high accuracy of 89.13% evaluated by a nested cross validation, demonstrated the superior performance of our framework over other comparison methods. The involved discriminative regions for classification primarily located in the basal ganglia and frontal cortico-cortical networks, all highly related to the pathology of TS. Together, our study may provide potential neuroimaging biomarkers for early-stage TS diagnosis.

  5. Exploring network structure, dynamics, and function using networkx

    Energy Technology Data Exchange (ETDEWEB)

    Hagberg, Aric [Los Alamos National Laboratory; Swart, Pieter [Los Alamos National Laboratory; S Chult, Daniel [COLGATE UNIV

    2008-01-01

    NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility mades NetworkX ideal for representing networks found in many different scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distribution and many more. NetworkX can read and write various graph formats for eash exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdoes-Renyi, Small World, and Barabasi-Albert models, are included. The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational networks.

  6. Process Features in Writing: Internal Structure and Incremental Value over Product Features. Research Report. ETS RR-15-27

    Science.gov (United States)

    Zhang, Mo; Deane, Paul

    2015-01-01

    In educational measurement contexts, essays have been evaluated and formative feedback has been given based on the end product. In this study, we used a large sample collected from middle school students in the United States to investigate the factor structure of the writing process features gathered from keystroke logs and the association of that…

  7. Vicus: Exploiting local structures to improve network-based analysis of biological data.

    Science.gov (United States)

    Wang, Bo; Huang, Lin; Zhu, Yuke; Kundaje, Anshul; Batzoglou, Serafim; Goldenberg, Anna

    2017-10-01

    Biological networks entail important topological features and patterns critical to understanding interactions within complicated biological systems. Despite a great progress in understanding their structure, much more can be done to improve our inference and network analysis. Spectral methods play a key role in many network-based applications. Fundamental to spectral methods is the Laplacian, a matrix that captures the global structure of the network. Unfortunately, the Laplacian does not take into account intricacies of the network's local structure and is sensitive to noise in the network. These two properties are fundamental to biological networks and cannot be ignored. We propose an alternative matrix Vicus. The Vicus matrix captures the local neighborhood structure of the network and thus is more effective at modeling biological interactions. We demonstrate the advantages of Vicus in the context of spectral methods by extensive empirical benchmarking on tasks such as single cell dimensionality reduction, protein module discovery and ranking genes for cancer subtyping. Our experiments show that using Vicus, spectral methods result in more accurate and robust performance in all of these tasks.

  8. Comparing Community Structure to Characteristics in Online Collegiate Social Networks

    OpenAIRE

    Traud, Amanda L.; Kelsic, Eric D.; Mucha, Peter J; Porter, Mason A.

    2008-01-01

    We study the structure of social networks of students by examining the graphs of Facebook "friendships" at five American universities at a single point in time. We investigate each single-institution network's community structure and employ graphical and quantitative tools, including standardized pair-counting methods, to measure the correlations between the network communities and a set of self-identified user characteristics (residence, class year, major, and high school). We review the bas...

  9. Tensor Spectral Clustering for Partitioning Higher-order Network Structures.

    Science.gov (United States)

    Benson, Austin R; Gleich, David F; Leskovec, Jure

    2015-01-01

    Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of interest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order structure of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms.

  10. Features for Exploiting Black-Box Optimization Problem Structure

    DEFF Research Database (Denmark)

    Tierney, Kevin; Malitsky, Yuri; Abell, Tinus

    2013-01-01

    Black-box optimization (BBO) problems arise in numerous scientic and engineering applications and are characterized by compu- tationally intensive objective functions, which severely limit the number of evaluations that can be performed. We present a robust set of features that analyze the tness...

  11. Facial features matching using a virtual structuring element

    NARCIS (Netherlands)

    Valenti, R.; Sebe, N.; Gevers, T.

    2008-01-01

    Face analysis in a real-world environment is a complex task as it should deal with challenging problems such as pose variations, illumination changes and complex backgrounds. The use of active appearance models for facial features detection is often successful in restricted environments, but the

  12. Network versus portfolio structure in financial systems

    Science.gov (United States)

    Kobayashi, Teruyoshi

    2013-10-01

    The question of how to stabilize financial systems has attracted considerable attention since the global financial crisis of 2007-2009. Recently, Beale et al. [Proc. Natl. Acad. Sci. USA 108, 12647 (2011)] demonstrated that higher portfolio diversity among banks would reduce systemic risk by decreasing the risk of simultaneous defaults at the expense of a higher likelihood of individual defaults. In practice, however, a bank default has an externality in that it undermines other banks’ balance sheets. This paper explores how each of these different sources of risk, simultaneity risk and externality, contributes to systemic risk. The results show that the allocation of external assets that minimizes systemic risk varies with the topology of the financial network as long as asset returns have negative correlations. In the model, a well-known centrality measure, PageRank, reflects an appropriately defined “infectiveness” of a bank. An important result is that the most infective bank needs not always to be the safest bank. Under certain circumstances, the most infective node should act as a firewall to prevent large-scale collective defaults. The introduction of a counteractive portfolio structure will significantly reduce systemic risk.

  13. Novel insights through the integration of structural and functional genomics data with protein networks.

    Science.gov (United States)

    Clarke, Declan; Bhardwaj, Nitin; Gerstein, Mark B

    2012-09-01

    In recent years, major advances in genomics, proteomics, macromolecular structure determination, and the computational resources capable of processing and disseminating the large volumes of data generated by each have played major roles in advancing a more systems-oriented appreciation of biological organization. One product of systems biology has been the delineation of graph models for describing genome-wide protein-protein interaction networks. The network organization and topology which emerges in such models may be used to address fundamental questions in an array of cellular processes, as well as biological features intrinsic to the constituent proteins (or "nodes") themselves. However, graph models alone constitute an abstraction which neglects the underlying biological and physical reality that the network's nodes and edges are highly heterogeneous entities. Here, we explore some of the advantages of introducing a protein structural dimension to such models, as the marriage of conventional network representations with macromolecular structural data helps to place static node and edge constructs in a biologically more meaningful context. We emphasize that 3D protein structures constitute a valuable conceptual and predictive framework by discussing examples of the insights provided, such as enabling in silico predictions of protein-protein interactions, providing rational and compelling classification schemes for network elements, as well as revealing interesting intrinsic differences between distinct node types, such as disorder and evolutionary features, which may then be rationalized in light of their respective functions within networks. Copyright © 2012 Elsevier Inc. All rights reserved.

  14. Online Social Networks: Essays on Membership, Privacy, and Structure

    NARCIS (Netherlands)

    Hofstra, B.

    2017-01-01

    The structure of social networks is crucial for obtaining social support, for meaningful connections to unknown social groups, and to overcome prejudice. Yet, we know little about the structure of social networks beyond those contacts that stand closest to us. This lack of knowledge results from a

  15. A scanning method for detecting clustering pattern of both attribute and structure in social networks

    Science.gov (United States)

    Wang, Tai-Chi; Phoa, Frederick Kin Hing

    2016-03-01

    Community/cluster is one of the most important features in social networks. Many cluster detection methods were proposed to identify such an important pattern, but few were able to identify the statistical significance of the clusters by considering the likelihood of network structure and its attributes. Based on the definition of clustering, we propose a scanning method, originated from analyzing spatial data, for identifying clusters in social networks. Since the properties of network data are more complicated than those of spatial data, we verify our method's feasibility via simulation studies. The results show that the detection powers are affected by cluster sizes and connection probabilities. According to our simulation results, the detection accuracy of structure clusters and both structure and attribute clusters detected by our proposed method is better than that of other methods in most of our simulation cases. In addition, we apply our proposed method to some empirical data to identify statistically significant clusters.

  16. Classification of mitotic figures with convolutional neural networks and seeded blob features.

    Science.gov (United States)

    Malon, Christopher D; Cosatto, Eric

    2013-01-01

    The mitotic figure recognition contest at the 2012 International Conference on Pattern Recognition (ICPR) challenges a system to identify all mitotic figures in a region of interest of hematoxylin and eosin stained tissue, using each of three scanners (Aperio, Hamamatsu, and multispectral). Our approach combines manually designed nuclear features with the learned features extracted by convolutional neural networks (CNN). The nuclear features capture color, texture, and shape information of segmented regions around a nucleus. The use of a CNN handles the variety of appearances of mitotic figures and decreases sensitivity to the manually crafted features and thresholds. On the test set provided by the contest, the trained system achieves F1 scores up to 0.659 on color scanners and 0.589 on multispectral scanner. We demonstrate a powerful technique combining segmentation-based features with CNN, identifying the majority of mitotic figures with a fair precision. Further, we show that the approach accommodates information from the additional focal planes and spectral bands from a multi-spectral scanner without major redesign.

  17. Classification of mitotic figures with convolutional neural networks and seeded blob features

    Directory of Open Access Journals (Sweden)

    Christopher D Malon

    2013-01-01

    Full Text Available Background: The mitotic figure recognition contest at the 2012 International Conference on Pattern Recognition (ICPR challenges a system to identify all mitotic figures in a region of interest of hematoxylin and eosin stained tissue, using each of three scanners (Aperio, Hamamatsu, and multispectral. Methods: Our approach combines manually designed nuclear features with the learned features extracted by convolutional neural networks (CNN. The nuclear features capture color, texture, and shape information of segmented regions around a nucleus. The use of a CNN handles the variety of appearances of mitotic figures and decreases sensitivity to the manually crafted features and thresholds. Results : On the test set provided by the contest, the trained system achieves F1 scores up to 0.659 on color scanners and 0.589 on multispectral scanner. Conclusions : We demonstrate a powerful technique combining segmentation-based features with CNN, identifying the majority of mitotic figures with a fair precision. Further, we show that the approach accommodates information from the additional focal planes and spectral bands from a multi-spectral scanner without major redesign.

  18. Artificial neural network as the tool in prediction rheological features of raw minced meat.

    Science.gov (United States)

    Balejko, Jerzy A; Nowak, Zbigniew; Balejko, Edyta

    2012-01-01

    The aim of the study was to elaborate a method of modelling and forecasting rheological features which could be applied to raw minced meat at the stage of mixture preparation with a given ingredient composition. The investigated material contained pork and beef meat, pork fat, fat substitutes, ice and curing mixture in various proportions. Seven texture parameters were measured for each sample of raw minced meat. The data obtained were processed using the artificial neural network module in Statistica 9.0 software. The model that reached the lowest training error was a multi-layer perceptron MLP with three neural layers and architecture 7:7-11-7:7. Correlation coefficients between the experimental and calculated values in training, verification and testing subsets were similar and rather high (around 0.65) which indicated good network performance. High percentage of the total variance explained in PCA analysis (73.5%) indicated that the percentage composition of raw minced meat can be successfully used in the prediction of its rheological features. Statistical analysis of the results revealed, that artificial neural network model is able to predict rheological parameters and thus a complete texture profile of raw minced meat.

  19. Towards an Efficient Artificial Neural Network Pruning and Feature Ranking Tool

    KAUST Repository

    AlShahrani, Mona

    2015-05-24

    Artificial Neural Networks (ANNs) are known to be among the most effective and expressive machine learning models. Their impressive abilities to learn have been reflected in many broad application domains such as image recognition, medical diagnosis, online banking, robotics, dynamic systems, and many others. ANNs with multiple layers of complex non-linear transformations (a.k.a Deep ANNs) have shown recently successful results in the area of computer vision and speech recognition. ANNs are parametric models that approximate unknown functions in which parameter values (weights) are adapted during training. ANN’s weights can be large in number and thus render the trained model more complex with chances for “overfitting” training data. In this study, we explore the effects of network pruning on performance of ANNs and ranking of features that describe the data. Simplified ANN model results in fewer parameters, less computation and faster training. We investigate the use of Hessian-based pruning algorithms as well as simpler ones (i.e. non Hessian-based) on nine datasets with varying number of input features and ANN parameters. The Hessian-based Optimal Brain Surgeon algorithm (OBS) is robust but slow. Therefore a faster parallel Hessian- approximation is provided. An additional speedup is provided using a variant we name ‘Simple n Optimal Brain Surgeon’ (SNOBS), which represents a good compromise between robustness and time efficiency. For some of the datasets, the ANN pruning experiments show on average 91% reduction in the number of ANN parameters and about 60% - 90% in the number of ANN input features, while maintaining comparable or better accuracy to the case when no pruning is applied. Finally, we show through a comprehensive comparison with seven state-of-the art feature filtering methods that the feature selection and ranking obtained as a byproduct of the ANN pruning is comparable in accuracy to these methods.

  20. Robustness and modular structure in networks

    DEFF Research Database (Denmark)

    Bagrow, James P.; Lehmann, Sune; Ahn, Yong-Yeol

    2015-01-01

    Complex networks have recently attracted much interest due to their prevalence in nature and our daily lives [1, 2]. A critical property of a network is its resilience to random breakdown and failure [3-6], typically studied as a percolation problem [7-9] or by modeling cascading failures[10....... If overlapping modular organization plays a role in overall functionality, networks may be far more vulnerable than predicted by conventional percolation theory....

  1. Topological effects of network structure on long-term social network dynamics in a wild mammal.

    Science.gov (United States)

    Ilany, Amiyaal; Booms, Andrew S; Holekamp, Kay E

    2015-07-01

    Social structure influences ecological processes such as dispersal and invasion, and affects survival and reproductive success. Recent studies have used static snapshots of social networks, thus neglecting their temporal dynamics, and focused primarily on a limited number of variables that might be affecting social structure. Here, instead we modelled effects of multiple predictors of social network dynamics in the spotted hyena, using observational data collected during 20 years of continuous field research in Kenya. We tested the hypothesis that the current state of the social network affects its long-term dynamics. We employed stochastic agent-based models that allowed us to estimate the contribution of multiple factors to network changes. After controlling for environmental and individual effects, we found that network density and individual centrality affected network dynamics, but that social bond transitivity consistently had the strongest effects. Our results emphasise the significance of structural properties of networks in shaping social dynamics. © 2015 John Wiley & Sons Ltd/CNRS.

  2. Fuzzy stochastic neural network model for structural system identification

    Science.gov (United States)

    Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong

    2017-01-01

    This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.

  3. Investigations of Protein Aggregation Using Sequence and Structure Based Features

    OpenAIRE

    M. Michael Gromiha; A. Mary Thangakani; Sandeep Kumar; D. Velmurugan

    2016-01-01

    The main cause of several neurodegenerative diseases such as Alzhemier, Parkinson and spongiform encephalopathies is formation of amyloid fibrils and plaques in proteins. We have analyzed different sets of proteins and peptides to understand the influence of sequence based features on protein aggregation process. The comparison of 373 pairs of homologous mesophilic and thermophilic proteins showed that aggregation prone regions (APRs) are present in both. But, the thermophilic protein monomer...

  4. A Cross-Layer Duty Cycle MAC Protocol Supporting a Pipeline Feature for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Young-Chon Kim

    2011-05-01

    Full Text Available Although the conventional duty cycle MAC protocols for Wireless Sensor Networks (WSNs such as RMAC perform well in terms of saving energy and reducing end-to-end delivery latency, they were designed independently and require an extra routing protocol in the network layer to provide path information for the MAC layer. In this paper, we propose a new cross-layer duty cycle MAC protocol with data forwarding supporting a pipeline feature (P-MAC for WSNs. P-MAC first divides the whole network into many grades around the sink. Each node identifies its grade according to its logical hop distance to the sink and simultaneously establishes a sleep/wakeup schedule using the grade information. Those nodes in the same grade keep the same schedule, which is staggered with the schedule of the nodes in the adjacent grade. Then a variation of the RTS/CTS handshake mechanism is used to forward data continuously in a pipeline fashion from the higher grade to the lower grade nodes and finally to the sink. No extra routing overhead is needed, thus increasing the network scalability while maintaining the superiority of duty-cycling. The simulation results in OPNET show that P-MAC has better performance than S-MAC and RMAC in terms of packet delivery latency and energy efficiency.

  5. Global-local feature attention network with reranking strategy for image caption generation

    Science.gov (United States)

    Wu, Jie; Xie, Si-ya; Shi, Xin-bao; Chen, Yao-wen

    2017-11-01

    In this paper, a novel framework, named as global-local feature attention network with reranking strategy (GLAN-RS), is presented for image captioning task. Rather than only adopting unitary visual information in the classical models, GLAN-RS explores the attention mechanism to capture local convolutional salient image maps. Furthermore, we adopt reranking strategy to adjust the priority of the candidate captions and select the best one. The proposed model is verified using the Microsoft Common Objects in Context (MSCOCO) benchmark dataset across seven standard evaluation metrics. Experimental results show that GLAN-RS significantly outperforms the state-of-the-art approaches, such as multimodal recurrent neural network (MRNN) and Google NIC, which gets an improvement of 20% in terms of BLEU4 score and 13 points in terms of CIDER score.

  6. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates ...

  7. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    Corresponding author. E-mail: Kiran.Kolwankar@gmail.com. Abstract. We study the effect of learning dynamics on network topology. Firstly, a network of dis- crete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the ...

  8. Wireless Sensor Networks : Structure and Algorithms

    NARCIS (Netherlands)

    van Dijk, T.C.|info:eu-repo/dai/nl/304841293

    2014-01-01

    In this thesis we look at various problems in wireless networking. First we consider two problems in physical-model networks. We introduce a new model for localisation. The model is based on a range-free model of radio transmissions. The first scheme is randomised and we analyse its expected

  9. Spatial Structure and Scaling of Agricultural Networks

    CERN Document Server

    Sousa, Daniel

    2016-01-01

    Considering agricultural landscapes as networks can provide information about spatial connectivity relevant for a wide range of applications including pollination, pest management, and ecology. Global agricultural networks are well-described by power law rank-size distributions. However, regional analyses capture only a subset of the total global network. Most analyses are regional. In this paper, we seek to address the following questions: Does the globally observed scale-free property of agricultural networks hold over smaller spatial domains? Can similar properties be observed at kilometer to meter scales? We analyze 9 intensively cultivated Landsat scenes on 5 continents with a wide range of vegetation distributions. We find that networks of vegetation fraction within the domain of each of these Landsat scenes exhibit substantial variability - but still possess similar scaling properties to the global distribution of agriculture. We also find similar results using a 39 km2 IKONOS image. To illustrate an a...

  10. Structure and properties of triolein-based polyurethane networks.

    Science.gov (United States)

    Zlatanić, Alisa; Petrović, Zoran S; Dusek, Karel

    2002-01-01

    Polyurethane networks based on vegetable oils have very heterogeneous composition, and it is difficult to find a close correlation between their structure and properties. To establish benchmark structure-properties relationships, we have prepared model polyurethane networks based on triolein and 4,4'-diphenylmethane diisocyanate (MDI). Cross-linking in the middle of fatty acid chains leaves significant parts of the triglyceride as dangling chains. To examine their effect on properties, we have synthesized another polyurethane network using triolein without dangling chains (removed by metathesis). The structure of polyols was studied in detail since it affects the structure of polyurethane networks. The network structure was analyzed from swelling and mechanical measurements and by applying network and rubber elasticity theories. The cross-linking density in both networks was found to be close to theoretical. The triolein-based model network displayed modulus (around 6 MPa), tensile strength (8.7 MPa), and elongation at break (136%), characteristic of hard rubbers. Glass transition temperatures of the networks from triolein and its metathesis analogue were 25 and 31.5 degrees C, respectively.

  11. Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts.

    Science.gov (United States)

    Gómez-Adorno, Helena; Markov, Ilia; Sidorov, Grigori; Posadas-Durán, Juan-Pablo; Sanchez-Perez, Miguel A; Chanona-Hernandez, Liliana

    2016-01-01

    We introduce a lexical resource for preprocessing social media data. We show that a neural network-based feature representation is enhanced by using this resource. We conducted experiments on the PAN 2015 and PAN 2016 author profiling corpora and obtained better results when performing the data preprocessing using the developed lexical resource. The resource includes dictionaries of slang words, contractions, abbreviations, and emoticons commonly used in social media. Each of the dictionaries was built for the English, Spanish, Dutch, and Italian languages. The resource is freely available.

  12. Adapting Bayes Network Structures to Non-stationary Domains

    DEFF Research Database (Denmark)

    Nielsen, Søren Holbech; Nielsen, Thomas Dyhre

    2006-01-01

    When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit observations, as they are read from a database, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN...

  13. Adapting Bayes Network Structures to Non-stationary Domains

    DEFF Research Database (Denmark)

    Nielsen, Søren Holbech; Nielsen, Thomas Dyhre

    2008-01-01

    When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN...

  14. Social Network Analysis of a Supply Network Structural Investigation of the South Korean Automotive Industry

    OpenAIRE

    Kim, Jin-Baek

    2015-01-01

    Part 3: Knowledge Based Production Management; International audience; In this paper, we analyzed the structure of the South Korean automotive industry using social network analysis (SNA) metrics. Based on the data collected from 275 companies, a social network model of the supply network was constructed. Centrality measures in the SNA field were used to interpret the result and identify key companies. The results show that SNA metrics can be useful to understand the structure of a supply net...

  15. Association between MRI structural features and cognitive measures in pediatric multiple sclerosis

    Science.gov (United States)

    Amoroso, N.; Bellotti, R.; Fanizzi, A.; Lombardi, A.; Monaco, A.; Liguori, M.; Margari, L.; Simone, M.; Viterbo, R. G.; Tangaro, S.

    2017-09-01

    Multiple sclerosis (MS) is an inflammatory and demyelinating disease associated with neurodegenerative processes that lead to brain structural changes. The disease affects mostly young adults, but 3-5% of cases has a pediatric onset (POMS). Magnetic Resonance Imaging (MRI) is generally used for diagnosis and follow-up in MS patients, however the most common MRI measures (e.g. new or enlarging T2-weighted lesions, T1-weighted gadolinium- enhancing lesions) have often failed as surrogate markers of MS disability and progression. MS is clinically heterogenous with symptoms that can include both physical changes (such as visual loss or walking difficulties) and cognitive impairment. 30-50% of POMS experience prominent cognitive dysfunction. In order to investigate the association between cognitive measures and brain morphometry, in this work we present a fully automated pipeline for processing and analyzing MRI brain scans. Relevant anatomical structures are segmented with FreeSurfer; besides, statistical features are computed. Thus, we describe the data referred to 12 patients with early POMS (mean age at MRI: 15.5 +/- 2.7 years) with a set of 181 structural features. The major cognitive abilities measured are verbal and visuo-spatial learning, expressive language and complex attention. Data was collected at the Department of Basic Sciences, Neurosciences and Sense Organs, University of Bari, and exploring different abilities like the verbal and visuo-spatial learning, expressive language and complex attention. Different regression models and parameter configurations are explored to assess the robustness of the results, in particular Generalized Linear Models, Bayes Regression, Random Forests, Support Vector Regression and Artificial Neural Networks are discussed.

  16. Exploration of the structural features and magnetic behaviour in a ...

    Indian Academy of Sciences (India)

    /fulltext/jcsc/127/02/0257-0264. Keywords. Mixed ligand system; Co(II)-based 3D framework; interpenetrated structure; sql/Shubnikov tetragonal plane net topology; non-covalent interactions; magnetic studies.

  17. Structural features of free and covalently bound glycans

    NARCIS (Netherlands)

    Blanchard, Véronique

    2006-01-01

    Carbohydrates act in many cellular functions and biological processes such as cell-cell recognition and adhesion, inflammation, fertilization, signal transduction, and development. In this context, structural information is required to understand molecular mechanisms involving carbohydrates. The

  18. Structural features based genome-wide characterization and prediction of nucleosome organization

    Directory of Open Access Journals (Sweden)

    Gan Yanglan

    2012-03-01

    Full Text Available Abstract Background Nucleosome distribution along chromatin dictates genomic DNA accessibility and thus profoundly influences gene expression. However, the underlying mechanism of nucleosome formation remains elusive. Here, taking a structural perspective, we systematically explored nucleosome formation potential of genomic sequences and the effect on chromatin organization and gene expression in S. cerevisiae. Results We analyzed twelve structural features related to flexibility, curvature and energy of DNA sequences. The results showed that some structural features such as DNA denaturation, DNA-bending stiffness, Stacking energy, Z-DNA, Propeller twist and free energy, were highly correlated with in vitro and in vivo nucleosome occupancy. Specifically, they can be classified into two classes, one positively and the other negatively correlated with nucleosome occupancy. These two kinds of structural features facilitated nucleosome binding in centromere regions and repressed nucleosome formation in the promoter regions of protein-coding genes to mediate transcriptional regulation. Based on these analyses, we integrated all twelve structural features in a model to predict more accurately nucleosome occupancy in vivo than the existing methods that mainly depend on sequence compositional features. Furthermore, we developed a novel approach, named DLaNe, that located nucleosomes by detecting peaks of structural profiles, and built a meta predictor to integrate information from different structural features. As a comparison, we also constructed a hidden Markov model (HMM to locate nucleosomes based on the profiles of these structural features. The result showed that the meta DLaNe and HMM-based method performed better than the existing methods, demonstrating the power of these structural features in predicting nucleosome positions. Conclusions Our analysis revealed that DNA structures significantly contribute to nucleosome organization and influence

  19. The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks.

    Science.gov (United States)

    Gu, Weiwei; Gong, Li; Lou, Xiaodan; Zhang, Jiang

    2017-10-13

    Network embedding which encodes all vertices in a network as a set of numerical vectors in accordance with it's local and global structures, has drawn widespread attention. Network embedding not only learns significant features of a network, such as the clustering and linking prediction but also learns the latent vector representation of the nodes which provides theoretical support for a variety of applications, such as visualization, link prediction, node classification, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although those algorithms have drawn much attention for their high scores in learning efficiency and accuracy, there is still a lack of theoretical explanation, and the transparency of those algorithms has been doubted. Here, we propose an approach based on the open-flow network model to reveal the underlying flow structure and its hidden metric space of different random walk strategies on networks. We show that the essence of embedding based on random walks is the latent metric structure defined on the open-flow network. This not only deepens our understanding of random- walk-based embedding algorithms but also helps in finding new potential applications in network embedding.

  20. Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae

    Directory of Open Access Journals (Sweden)

    Araabi Babak N

    2010-12-01

    Full Text Available Abstract Background It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes. Results We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%. Conclusions We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of Saccharomyces Cerevisiae's proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the

  1. Completely random measures for modelling block-structured sparse networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Schmidt, Mikkel Nørgaard; Mørup, Morten

    2016-01-01

    Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world networks...... [2014] proposed the use of a different notion of exchangeability due to Kallenberg [2006] and obtained a network model which admits power-law behaviour while retaining desirable statistical properties, however this model does not capture latent vertex traits such as block-structure. In this work we re......-introduce the use of block-structure for network models obeying allenberg’s notion of exchangeability and thereby obtain a model which admits the inference of block-structure and edge inhomogeneity. We derive a simple expression for the likelihood and an efficient sampling method. The obtained model...

  2. Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.

    Science.gov (United States)

    Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

    2016-01-22

    The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named "DeepMethyl" to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

  3. Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach.

    Science.gov (United States)

    Erguzel, Turker Tekin; Ozekes, Serhat; Tan, Oguz; Gultekin, Selahattin

    2015-10-01

    Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set. © EEG and Clinical Neuroscience Society (ECNS) 2014.

  4. Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks

    Science.gov (United States)

    Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

    2016-01-01

    The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named “DeepMethyl” to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

  5. Structural Features and Healthy Properties of Polysaccharides Occurring in Mushrooms

    Directory of Open Access Journals (Sweden)

    Eva Guillamón

    2012-12-01

    Full Text Available Polysaccharides from mushrooms have attracted a great deal of attention due to the many healthy benefits they have demonstrated, such as immunomodulation, anticancer activity, prevention and treatment of cardiovascular diseases, antiviral and antimicrobial effects, among others. Isolation and purification of polysaccharides commonly involve several steps, and different techniques are actually available in order to increase extraction yield and purity. Studies have demonstrated that the molecular structure and arrangement significantly influence the biological activity; therefore, there is a wide range of analytical techniques for the elucidation of chemical structures. Different polysaccharides have been isolated from mushrooms, most of them consisting of β-linked glucans, such as lentinan from Lentinus edodes, pleuran from Pleurotus species, schizophyllan from Schizophyllum commune, calocyban from Calocybe indica, or ganoderan and ganopoly from Ganoderma lucidum. This article reviews the main methods of polysaccharide isolation and structural characterization, as well as some of the most important polysaccharides isolated from mushrooms and the healthy benefits they provide.

  6. Optimizing and Understanding Network Structure for Diffusion

    OpenAIRE

    Zhang, Yao

    2017-01-01

    Given a population contact network and electronic medical records of patients, how to distribute vaccines to individuals to effectively control a flu epidemic? Similarly, given the Twitter following network and tweets, how to choose the best communities/groups to stop rumors from spreading? How to find the best accounts that bridge celebrities and ordinary users? These questions are related to diffusion (aka propagation) phenomena. Diffusion can be treated as a behavior of spreading contagion...

  7. Reverse Logistics Network Structures and Design

    OpenAIRE

    Fleischmann, Moritz

    2001-01-01

    textabstractLogistics network design is commonly recognized as a strategic supply chain issue of prime importance. The location of production facilities, storage concepts, and transportation strategies are major determinants of supply chain performance. This chapter considers logistics network design for the particular case of closed-loop supply chains. We highlight key issues that companies are facing when deciding upon the logistics implementation of a product recovery initiative. In partic...

  8. Social network theory applied to resting-state fMRI connectivity data in the identification of epilepsy networks with iterative feature selection.

    Science.gov (United States)

    Zhang, Xiaohui; Tokoglu, Fuyuze; Negishi, Michiro; Arora, Jagriti; Winstanley, Scott; Spencer, Dennis D; Constable, R Todd

    2011-07-15

    Epilepsy is a brain disorder usually associated with abnormal cortical and/or subcortical functional networks. Exploration of the abnormal network properties and localization of the brain regions involved in human epilepsy networks are critical for both the understanding of the epilepsy networks and planning therapeutic strategies. Currently, most localization of seizure networks come from ictal EEG observations. Functional MRI provides high spatial resolution together with more complete anatomical coverage compared with EEG and may have advantages if it can be used to identify the network(s) associated with seizure onset and propagation. Epilepsy networks are believed to be present with detectable abnormal signatures even during the interictal state. In this study, epilepsy networks were investigated using resting-state fMRI acquired with the subjects in the interictal state. We tested the hypothesis that social network theory applied to resting-state fMRI data could reveal abnormal network properties at the group level. Using network data as input to a classification algorithm allowed separation of medial temporal lobe epilepsy (MTLE) patients from normal control subjects indicating the potential value of such network analyses in epilepsy. Five local network properties obtained from 36 anatomically defined ROIs were input as features to the classifier. An iterative feature selection strategy based on the classification efficiency that can avoid 'over-fitting' is proposed to further improve the classification accuracy. An average sensitivity of 77.2% and specificity of 83.86% were achieved via 'leave one out' cross validation. This finding of significantly abnormal network properties in group level data confirmed our initial hypothesis and provides motivation for further investigation of the epilepsy process at the network level. Copyright © 2011 Elsevier B.V. All rights reserved.

  9. STRUCTURAL AND SEMANTIC FEATURES OF ONLINE INFORMATION RESOURCES

    Directory of Open Access Journals (Sweden)

    Ivan Georgievich Zubkov

    2014-07-01

    Full Text Available Information society is a global economic, political, technological and antroposocial project involving controlled civilizational transition to the world social system in which the dominant role in all spheres of life will play a mass communication system, implemented with the help of computer technology, particularly Internet technology. The question of formation and presentation of information is highly relevant today. The world community has entered a transitional stage from its post-industrial state to the informational. At this stage of the development, society is increasingly dependent on the quality, availability, reliability, timeliness of information, which is created by its own representatives. Especially clearly it is evident in the Internet space, where all information flows merge into a single closed and continuously update system that has its own characteristics, features and qualities.DOI: http://dx.doi.org/10.12731/2218-7405-2014-4-7

  10. DHPC: a new tool to express genome structural features.

    Science.gov (United States)

    Deng, Xuegong; Deng, Xuemei; Rayner, Simon; Liu, Xiangdong; Zhang, Qingling; Yang, Yupu; Li, Ning

    2008-05-01

    The DHPC (DNA Hilbert-Peano curve) is a new tool for visualizing large-scale genome sequences by mapping sequences into a two-dimensional square. It utilizes the space-filling function of Hilbert-Peano mapping. By applying a Gauss smoothing technique and a user-defined color function, a large-scale genome sequence can be mapped into a two-dimensional color image. In the calculated DHPCs, many genome characteristics are revealed. In this article we introduce the method and show how DHPCs may be used to identify regions of different base composition. The power of the method is demonstrated by presenting multiple examples such as repeating sequences, degree of base bias, regions of homogeneity and their boundaries, and mark of annotated segments. We also present several genome curves generated by DHPC to demonstrate how DHPC can be used to find previously unidentified sequence features in these genomes.

  11. Pre-trained convolutional neural networks as feature extractors for tuberculosis detection.

    Science.gov (United States)

    Lopes, U K; Valiati, J F

    2017-10-01

    It is estimated that in 2015, approximately 1.8 million people infected by tuberculosis died, most of them in developing countries. Many of those deaths could have been prevented if the disease had been detected at an earlier stage, but the most advanced diagnosis methods are still cost prohibitive for mass adoption. One of the most popular tuberculosis diagnosis methods is the analysis of frontal thoracic radiographs; however, the impact of this method is diminished by the need for individual analysis of each radiography by properly trained radiologists. Significant research can be found on automating diagnosis by applying computational techniques to medical images, thereby eliminating the need for individual image analysis and greatly diminishing overall costs. In addition, recent improvements on deep learning accomplished excellent results classifying images on diverse domains, but its application for tuberculosis diagnosis remains limited. Thus, the focus of this work is to produce an investigation that will advance the research in the area, presenting three proposals to the application of pre-trained convolutional neural networks as feature extractors to detect the disease. The proposals presented in this work are implemented and compared to the current literature. The obtained results are competitive with published works demonstrating the potential of pre-trained convolutional networks as medical image feature extractors. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Acoustic measurement and morphological features of organic sediment deposits in combined sewer networks.

    Science.gov (United States)

    Carnacina, Iacopo; Larrarte, Frédérique; Leonardi, Nicoletta

    2017-04-01

    The performance of sewer networks has important consequences from an environmental and social point of view. Poor functioning can result in flood risk and pollution at a large scale. Sediment deposits forming in sewer trunks might severely compromise the sewer line by affecting the flow field, reducing cross-sectional areas, and increasing roughness coefficients. In spite of numerous efforts, the morphological features of these depositional environments remain poorly understood. The interface between water and sediment remains inefficiently identified and the estimation of the stock of deposit is frequently inaccurate. In part, this is due to technical issues connected to difficulties in collecting accurate field measurements without disrupting existing morphologies. In this paper, results from an extensive field campaign are presented; during the campaign a new survey methodology based on acoustic techniques has been tested. Furthermore, a new algorithm for the detection of the soil-water interface, and therefore for the correct esteem of sediment stocks is proposed. Finally, results in regard to bed topography, and morphological features at two different field sites are presented and reveal that a large variability in bed forms is present along sewer networks. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Identification and tracking of vertebrae in ultrasound using deep networks with unsupervised feature learning

    Science.gov (United States)

    Hetherington, Jorden; Pesteie, Mehran; Lessoway, Victoria A.; Abolmaesumi, Purang; Rohling, Robert N.

    2017-03-01

    Percutaneous needle insertion procedures on the spine often require proper identification of the vertebral level in order to effectively deliver anesthetics and analgesic agents to achieve adequate block. For example, in obstetric epidurals, the target is at the L3-L4 intervertebral space. The current clinical method involves "blind" identification of the vertebral level through manual palpation of the spine, which has only 30% accuracy. This implies the need for better anatomical identification prior to needle insertion. A system is proposed to identify the vertebrae, assigning them to their respective levels, and track them in a standard sequence of ultrasound images, when imaged in the paramedian plane. Machine learning techniques are developed to identify discriminative features of the laminae. In particular, a deep network is trained to automatically learn the anatomical features of the lamina peaks, and classify image patches, for pixel-level classification. The chosen network utilizes multiple connected auto-encoders to learn the anatomy. Pre-processing with ultrasound bone enhancement techniques is done to aid the pixel-level classification performance. Once the lamina are identified, vertebrae are assigned levels and tracked in sequential frames. Experimental results were evaluated against an expert sonographer. Based on data acquired from 15 subjects, vertebrae identification with sensitivity of 95% and precision of 95% was achieved within each frame. Between pairs of subsequently analyzed frames, matches of predicted vertebral level labels were correct in 94% of cases, when compared to matches of manually selected labels

  14. Introduction to Focus Issue: Complex Dynamics in Networks, Multilayered Structures and Systems

    Science.gov (United States)

    Boccaletti, Stefano; Criado, Regino; Romance, Miguel; Torres, Joaquín J.

    2016-06-01

    In the last years, network scientists have directed their interest to the multi-layer character of real-world systems, and explicitly considered the structural and dynamical organization of graphs made of diverse layers between its constituents. Most complex systems include multiple subsystems and layers of connectivity and, in many cases, the interdependent components of systems interact through many different channels. Such a new perspective is indeed found to be the adequate representation for a wealth of features exhibited by networked systems in the real world. The contributions presented in this Focus Issue cover, from different points of view, the many achievements and still open questions in the field of multi-layer networks, such as: new frameworks and structures to represent and analyze heterogeneous complex systems, different aspects related to synchronization and centrality of complex networks, interplay between layers, and applications to logistic, biological, social, and technological fields.

  15. Inferring the interplay of network structure and market effects in Bitcoin

    CERN Document Server

    Kondor, Dániel; Szüle, János; Pósfai, Márton; Vattay, Gábor

    2014-01-01

    A main focus in economics research is understanding the time series of prices of goods and assets. While statistical models using only the properties of the time series itself have been successful in many aspects, we expect to gain a better understanding of the phenomena involved if we can model the underlying system of interacting agents. In this article, we consider the history of Bitcoin, a novel digital currency system, for which the complete list of transactions is available for analysis. Using this dataset, we reconstruct the transaction network between users and analyze changes in the structure of the subgraph induced by the most active users. Our approach is based on the unsupervised identification of important features of the time variation of the network. Applying the widely used method of Principal Component Analysis to the matrix constructed from snapshots of the network at different times, we are able to show how structural changes in the network accompany significant changes in the exchange pric...

  16. Ultrasonic and structural features of some borosilicate glasses ...

    Indian Academy of Sciences (India)

    ... was prepared and studied by Fourier transform infrared spectroscopy, density and ultrasonic techniques to debate the issue of the role of SiO2 in the structureof lead alkali borate glasses. The results indicate that SiO2 generates an abundance of bridging oxygen atoms, [BO 4 ] and [SiO 4 ] structural units and changes the ...

  17. Radiographic features of mandibular trabecular bone structure in hypodontia

    NARCIS (Netherlands)

    Créton, M.; Geraets, W.; Verhoeven, J.W.; van der Stelt, P.F.; Verhey, H.; Cune, M.

    2012-01-01

    Purpose: Radiographic parameters of mandibular trabecular bone structure between 67 subjects having hypodontia and those without were studied on digital panoramic radiographs. Materials and Methods: Three regions of interest (ROI) were defined: the ascending ramus, apical of the mandibular molar and

  18. Joint Modelling of Structural and Functional Brain Networks

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther; Herlau, Tue; Mørup, Morten

    Functional and structural magnetic resonance imaging have become the most important noninvasive windows to the human brain. A major challenge in the analysis of brain networks is to establish the similarities and dissimilarities between functional and structural connectivity. We formulate a non......-parametric Bayesian network model which allows for joint modelling and integration of multiple networks. We demonstrate the model’s ability to detect vertices that share structure across networks jointly in functional MRI (fMRI) and diffusion MRI (dMRI) data. Using two fMRI and dMRI scans per subject, we establish...... significant structures that are consistently shared across subjects and data splits. This provides an unsupervised approach for modeling of structure-function relations in the brain and provides a general framework for multimodal integration....

  19. Structure of Retail Services in the Brazilian Hosting Network

    Directory of Open Access Journals (Sweden)

    Claudio Zancan

    2015-08-01

    Full Text Available this research has identified Brazilian hosting networks through infrastructure services indicators that it was sold to tourists in organizations that form these networks. The theory consulted the discussion of structural techniques present in Social Network Analysis. The study has three stages: documental research, creation of Tourism database and interviews. The results identified three networks with the highest expression in Brazil formed by hotels, lodges, and resorts. Different char-acteristics of infrastructure and services were observed between hosting networks. Future studies suggest a comparative analysis of structural indicators present in other segments of tourism services, as well as the existing international influ-ence on the development of the Brazilian hosting networks.

  20. Self-organization in neural networks - Applications in structural optimization

    Science.gov (United States)

    Hajela, Prabhat; Fu, B.; Berke, Laszlo

    1993-01-01

    The present paper discusses the applicability of ART (Adaptive Resonance Theory) networks, and the Hopfield and Elastic networks, in problems of structural analysis and design. A characteristic of these network architectures is the ability to classify patterns presented as inputs into specific categories. The categories may themselves represent distinct procedural solution strategies. The paper shows how this property can be adapted in the structural analysis and design problem. A second application is the use of Hopfield and Elastic networks in optimization problems. Of particular interest are problems characterized by the presence of discrete and integer design variables. The parallel computing architecture that is typical of neural networks is shown to be effective in such problems. Results of preliminary implementations in structural design problems are also included in the paper.

  1. Structural features of glycan recognition among viral pathogens.

    Science.gov (United States)

    Shanker, Sreejesh; Hu, Liya; Ramani, Sasirekha; Atmar, Robert L; Estes, Mary K; Venkataram Prasad, B V

    2017-06-01

    Recognition and binding to host glycans present on cellular surfaces is an initial and critical step in viral entry. Diverse families of host glycans such as histo-blood group antigens, sialoglycans and glycosaminoglycans are recognized by viruses. Glycan binding determines virus-host specificity, tissue tropism, pathogenesis and potential for interspecies transmission. Viruses including noroviruses, rotaviruses, enteroviruses, influenza, and papillomaviruses have evolved novel strategies to bind specific glycans often in a strain-specific manner. Structural studies have been instrumental in elucidating the molecular determinants of these virus-glycan interactions, aiding in developing vaccines and antivirals targeting this key interaction. Our review focuses on these key structural aspects of virus-glycan interactions, particularly highlighting the different strain-specific strategies employed by viruses to bind host glycans. Copyright © 2017. Published by Elsevier Ltd.

  2. Organizational structure and features of Sports Committee of Ukraine

    Directory of Open Access Journals (Sweden)

    Oleksandr Tomenko

    2015-12-01

    Full Text Available Purpose: in order to improve the management of non-Olympic sports in Ukraine to determine the structure and functions of the Sports Committee of Ukraine. Material and Methods: analysis and synthesis of scientific literature; analysis of documentary materials; sociological methods. Results: we find the structure of the Sports Committee of Ukraine as hierarchical with complexity traits. Local management bodies are: the General Assembly, the Executive Committee, the Bureau and the President. The decisions of the governing bodies implementation shall exercise the Executive Directorate and four commissions. It is shown that the strategic resource of the Sports Committee of Ukraine are knowledge and exclusive experience; capability perspective of reforming the sphere of physical culture and sports, work on the development of the Academy of Sports Committee of Ukraine

  3. Concordance of visual and structural features between siblings with albinism.

    Science.gov (United States)

    Heinmiller, Laura J; Holleschau, Ann; Summers, C Gail

    2016-02-01

    To evaluate similarities and differences in visual function and ocular structure between siblings with albinism. The medical records of all siblings diagnosed with albinism were retrospectively reviewed. Comparisons were made using examination at oldest age for younger sibling and examination closest to that age for older siblings. A total of 111 patients from 54 families were studied. Mean age was 12.9 years (range, 2 months to 44.2 years). Mean difference in ages between sibling pair examinations was 11.5 months (range, 0-87 months). Of 45 families, best-corrected visual acuity was equal in 9 (20%), within 1/2 octave in 9 (20%), >1/2 but albinism should be counseled with due caution because visual function is often disparate despite similar structural findings. Copyright © 2016 American Association for Pediatric Ophthalmology and Strabismus. Published by Elsevier Inc. All rights reserved.

  4. Ultrasonic and structural features of some borosilicate glasses ...

    Indian Academy of Sciences (India)

    2017-06-09

    Jun 9, 2017 ... Abstract. A quaternary glass system Na1.4B2.8Six Pb0.3−x O5.2+x , with 0 ≤x ≤ 0.3, was prepared and studied by Fourier transform infrared spectroscopy, density and ultrasonic techniques to debate the issue of the role of SiO2 in the structure of lead alkali borate glasses. The results indicate that SiO2 ...

  5. Special features of modern transformation of social structure Ukraines population

    OpenAIRE

    Mandubura, V.; Batagok, S.

    2010-01-01

    Issues of social structure of population of Ukraine are described. Reasons and problems concerning large-ranged marginalization of inhabitants of Ukraine are disclosed problems of improvement of methodological approach to structurising the spectrum of displays of marginal and polymarginal condition of population are revealed. A category underclass is defined and impact of marginalization on social and economical stability in context of subseguent development of society is disclosed.

  6. Geophysical determination of buried structural features at Ovbiogie ...

    African Journals Online (AJOL)

    Computer iterated data from the study area made the structural determination possible with resistivity values ranging from 85ohm-m to 735ohm-m and depth ... soil at a depth of 1.0m, sandy soil at a depth of 3.0m, clay and mart at a depth of 12.6m, river sand and gravel at a depth of 35.1m and basalt at a depth of infinity.

  7. The Structural Features of Substantive-Nominative Proposemes

    Directory of Open Access Journals (Sweden)

    Елена Александровна Петрова

    2015-12-01

    Full Text Available The article is devoted to to the mental representation of grammatical structures, namely to the ‎structural analysis analysis of substantive-nominative proposemes that is to the Compound ‎proposemes, composing the main and the subordinate predicative clauses and proposemes-‎balances, composing two subordinate clauses - subject and predicative. The material for the ‎research are substantive-nominative proposemes taken from the works of British and American ‎writers of the XX century. In the work the definitional analysis, the classification method and ‎the modeling method of structural-functional analysis, the method of linguistic observation, the ‎descriptive and analytical method were used. The results of the research showed that the ‎analyzed proposemes can be treated, first. as the constructions of primary syntactic position ‎which, in the functional plan, are substantive-nominative, secondly, monolithic - on the degree of ‎intensity of connection between their clauses and, thirdly, from a logic-cognitive point of view - ‎monomial, expressing one difficult judgment with the event nature of an objectivization of ‎thought.‎

  8. Altered temporal features of intrinsic connectivity networks in boys with combined type of attention deficit hyperactivity disorder

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Xun-Heng, E-mail: xhwang@hdu.edu.cn [College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018 (China); School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096 (China); Li, Lihua [College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018 (China)

    2015-05-15

    Highlights: • Temporal patterns within ICNs provide new way to investigate ADHD brains. • ADHD exhibits enhanced temporal activities within and between ICNs. • Network-wise ALFF influences functional connectivity between ICNs. • Univariate patterns within ICNs are correlated to behavior scores. - Abstract: Purpose: Investigating the altered temporal features within and between intrinsic connectivity networks (ICNs) for boys with attention-deficit/hyperactivity disorder (ADHD); and analyzing the relationships between altered temporal features within ICNs and behavior scores. Materials and methods: A cohort of boys with combined type of ADHD and a cohort of age-matched healthy boys were recruited from ADHD-200 Consortium. All resting-state fMRI datasets were preprocessed and normalized into standard brain space. Using general linear regression, 20 ICNs were taken as spatial templates to analyze the time-courses of ICNs for each subject. Amplitude of low frequency fluctuations (ALFFs) were computed as univariate temporal features within ICNs. Pearson correlation coefficients and node strengths were computed as bivariate temporal features between ICNs. Additional correlation analysis was performed between temporal features of ICNs and behavior scores. Results: ADHD exhibited more activated network-wise ALFF than normal controls in attention and default mode-related network. Enhanced functional connectivities between ICNs were found in ADHD. The network-wise ALFF within ICNs might influence the functional connectivity between ICNs. The temporal pattern within posterior default mode network (pDMN) was positively correlated to inattentive scores. The subcortical network, fusiform-related DMN and attention-related networks were negatively correlated to Intelligence Quotient (IQ) scores. Conclusion: The temporal low frequency oscillations of ICNs in boys with ADHD were more activated than normal controls during resting state; the temporal features within ICNs could

  9. Predicting Successful Memes using Network and Community Structure

    OpenAIRE

    Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

    2014-01-01

    We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of...

  10. Structural dimensions of knowledge-action networks for sustainability

    Science.gov (United States)

    Tischa A. Munoz; B.B. Cutts

    2016-01-01

    Research on the influence of social network structure over flows of knowledge in support of sustainability governance and action has recently flourished. These studies highlight three challenges to evaluating knowledge-action networks: first, defining boundaries; second, characterizing power distributions; and third, identifying obstacles to knowledge sharing and...

  11. Structural and Infrastructural Underpinnings of International R&D Networks

    DEFF Research Database (Denmark)

    Niang, Mohamed; Sørensen, Brian Vejrum

    2009-01-01

    This paper explores the process of globally distributing R&D activities with an emphasis on the effects of network maturity. It discusses emerging configurations by asking how the structure and infrastructure of international R&D networks evolve along with the move from a strong R&D center...

  12. The National Biomedical Communications Network as a Developing Structure.

    Science.gov (United States)

    Davis, Ruth M.

    The National Biomedical Communications Network has evolved both from a set of conceptual recommendations over the last twelve years and an accumulation of needs manifesting themselves in the requests of members of the medical community. With a short history of three years this Network and its developing structure have exhibited most of the…

  13. Information Propagation in Complex Networks : Structures and Dynamics

    NARCIS (Netherlands)

    Märtens, M.

    2018-01-01

    This thesis is a contribution to a deeper understanding of how information propagates and what this process entails. At its very core is the concept of the network: a collection of nodes and links, which describes the structure of the systems under investigation. The network is a mathematical model

  14. Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease.

    Science.gov (United States)

    Shi, Jun; Zheng, Xiao; Li, Yan; Zhang, Qi; Ying, Shihui

    2018-01-01

    The accurate diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal neuroimaging data, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), has shown its effectiveness for AD diagnosis. The deep polynomial networks (DPN) is a recently proposed deep learning algorithm, which performs well on both large-scale and small-size datasets. In this study, a multimodal stacked DPN (MM-SDPN) algorithm, which MM-SDPN consists of two-stage SDPNs, is proposed to fuse and learn feature representation from multimodal neuroimaging data for AD diagnosis. Specifically speaking, two SDPNs are first used to learn high-level features of MRI and PET, respectively, which are then fed to another SDPN to fuse multimodal neuroimaging information. The proposed MM-SDPN algorithm is applied to the ADNI dataset to conduct both binary classification and multiclass classification tasks. Experimental results indicate that MM-SDPN is superior over the state-of-the-art multimodal feature-learning-based algorithms for AD diagnosis.

  15. Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit

    Science.gov (United States)

    Schawinski, Kevin; Zhang, Ce; Zhang, Hantian; Fowler, Lucas; Santhanam, Gokula Krishnan

    2017-05-01

    Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data. Conventional deconvolution techniques are limited in their ability to recover features in imaging data by the Shannon-Nyquist sampling theorem. Here, we train a generative adversarial network (GAN) on a sample of 4550 images of nearby galaxies at 0.01 < z < 0.02 from the Sloan Digital Sky Survey and conduct 10× cross-validation to evaluate the results. We present a method using a GAN trained on galaxy images that can recover features from artificially degraded images with worse seeing and higher noise than the original with a performance that far exceeds simple deconvolution. The ability to better recover detailed features such as galaxy morphology from low signal to noise and low angular resolution imaging data significantly increases our ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope (LSST) and the Hubble and James Webb space telescopes.

  16. Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features.

    Science.gov (United States)

    Adetiba, Emmanuel; Olugbara, Oludayo O

    2015-01-01

    This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their "nonensemble" variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations.

  17. Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

    Directory of Open Access Journals (Sweden)

    Emmanuel Adetiba

    2015-01-01

    Full Text Available This paper reports an experimental comparison of artificial neural network (ANN and support vector machine (SVM ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG and local binary pattern (LBP state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations.

  18. Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks.

    Science.gov (United States)

    Jiang, Yu-Gang; Wu, Zuxuan; Wang, Jun; Xue, Xiangyang; Chang, Shih-Fu

    2018-02-01

    In this paper, we study the challenging problem of categorizing videos according to high-level semantics such as the existence of a particular human action or a complex event. Although extensive efforts have been devoted in recent years, most existing works combined multiple video features using simple fusion strategies and neglected the utilization of inter-class semantic relationships. This paper proposes a novel unified framework that jointly exploits the feature relationships and the class relationships for improved categorization performance. Specifically, these two types of relationships are estimated and utilized by imposing regularizations in the learning process of a deep neural network (DNN). Through arming the DNN with better capability of harnessing both the feature and the class relationships, the proposed regularized DNN (rDNN) is more suitable for modeling video semantics. We show that rDNN produces better performance over several state-of-the-art approaches. Competitive results are reported on the well-known Hollywood2 and Columbia Consumer Video benchmarks. In addition, to stimulate future research on large scale video categorization, we collect and release a new benchmark dataset, called FCVID, which contains 91,223 Internet videos and 239 manually annotated categories.

  19. Ranking Features on Psychological Dynamics of Cooperative Team Work through Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Pilar Fuster-Parra

    2016-05-01

    Full Text Available The aim of this study is to rank some features that characterize the psychological dynamics of cooperative team work in order to determine priorities for interventions and formation: leading positive feedback, cooperative manager and collaborative manager features. From a dataset of 20 cooperative sport teams (403 soccer players, the characteristics of the prototypical sports teams are studied using an average Bayesian network (BN and two special types of BNs, the Bayesian classifiers: naive Bayes (NB and tree augmented naive Bayes (TAN. BNs are selected as they are able to produce probability estimates rather than predictions. BN results show that the antecessors (the “top” features ranked are the team members’ expectations and their attraction to the social aspects of the task. The main node is formed by the cooperative behaviors, the consequences ranked at the BN bottom (ratified by the TAN trees and the instantiations made, the roles assigned to the members and their survival inside the same team. These results should help managers to determine contents and priorities when they have to face team-building actions.

  20. EEG signal classification method based on fractal features and neural network.

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    2008-01-01

    In this paper, we propose a method to classify electroencephalogram (EEG) signal recorded from left- and right-hand movement imaginations. Three subjects (two males and one female) are volunteered to participate in the experiment. We use a technique of complexity measure based on fractal analysis to reveal feature patterns in the EEG signal. Effective algorithm, namely, detrended fluctuation analysis (DFA) has been selected to estimate embedded fractal dimension (FD) values between relaxing and imaging states of the recorded EEG signal. To show the waveform of FDs, we use a windowing-based method or called time-dependent fractal dimension (TDFD) and the Kullback-Leibler (K-L) divergence. Two feature parameters; K-L divergence and different expected values are proposed to be input variables of the classifier. Finally, featured data are classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Experimental results can be considerably applied in a brain-computer interface (BCI) application and show that the proposed method is more effective than the conventional method by improving average classification rates of 87.5% and 88.3% for left- and right-hand movement imagery tasks, respectively.

  1. Social features of online networks: the strength of intermediary ties in online social media.

    Directory of Open Access Journals (Sweden)

    Przemyslaw A Grabowicz

    Full Text Available An increasing fraction of today's social interactions occur using online social media as communication channels. Recent worldwide events, such as social movements in Spain or revolts in the Middle East, highlight their capacity to boost people's coordination. Online networks display in general a rich internal structure where users can choose among different types and intensity of interactions. Despite this, there are still open questions regarding the social value of online interactions. For example, the existence of users with millions of online friends sheds doubts on the relevance of these relations. In this work, we focus on Twitter, one of the most popular online social networks, and find that the network formed by the basic type of connections is organized in groups. The activity of the users conforms to the landscape determined by such groups. Furthermore, Twitter's distinction between different types of interactions allows us to establish a parallelism between online and offline social networks: personal interactions are more likely to occur on internal links to the groups (the weakness of strong ties; events transmitting new information go preferentially through links connecting different groups (the strength of weak ties or even more through links connecting to users belonging to several groups that act as brokers (the strength of intermediary ties.

  2. Social features of online networks: the strength of intermediary ties in online social media.

    Science.gov (United States)

    Grabowicz, Przemyslaw A; Ramasco, José J; Moro, Esteban; Pujol, Josep M; Eguiluz, Victor M

    2012-01-01

    An increasing fraction of today's social interactions occur using online social media as communication channels. Recent worldwide events, such as social movements in Spain or revolts in the Middle East, highlight their capacity to boost people's coordination. Online networks display in general a rich internal structure where users can choose among different types and intensity of interactions. Despite this, there are still open questions regarding the social value of online interactions. For example, the existence of users with millions of online friends sheds doubts on the relevance of these relations. In this work, we focus on Twitter, one of the most popular online social networks, and find that the network formed by the basic type of connections is organized in groups. The activity of the users conforms to the landscape determined by such groups. Furthermore, Twitter's distinction between different types of interactions allows us to establish a parallelism between online and offline social networks: personal interactions are more likely to occur on internal links to the groups (the weakness of strong ties); events transmitting new information go preferentially through links connecting different groups (the strength of weak ties) or even more through links connecting to users belonging to several groups that act as brokers (the strength of intermediary ties).

  3. Social Features of Online Networks: The Strength of Intermediary Ties in Online Social Media

    Science.gov (United States)

    Grabowicz, Przemyslaw A.; Ramasco, José J.; Moro, Esteban; Pujol, Josep M.; Eguiluz, Victor M.

    2012-01-01

    An increasing fraction of today's social interactions occur using online social media as communication channels. Recent worldwide events, such as social movements in Spain or revolts in the Middle East, highlight their capacity to boost people's coordination. Online networks display in general a rich internal structure where users can choose among different types and intensity of interactions. Despite this, there are still open questions regarding the social value of online interactions. For example, the existence of users with millions of online friends sheds doubts on the relevance of these relations. In this work, we focus on Twitter, one of the most popular online social networks, and find that the network formed by the basic type of connections is organized in groups. The activity of the users conforms to the landscape determined by such groups. Furthermore, Twitter's distinction between different types of interactions allows us to establish a parallelism between online and offline social networks: personal interactions are more likely to occur on internal links to the groups (the weakness of strong ties); events transmitting new information go preferentially through links connecting different groups (the strength of weak ties) or even more through links connecting to users belonging to several groups that act as brokers (the strength of intermediary ties). PMID:22247773

  4. Cluster Features in Reactions and Structure of Heavy Nuclei

    CERN Document Server

    Adamian, G G; Jolos, R V; Scheid, W; Shneidman, T M

    2003-01-01

    Cluster effects in the structure of heavy nuclei are considered. The properties of the states of the alternating parity bands in Ra, Th, U and Pu isotopes are analyzed within a cluster model. The model is based on the assumption that cluster type shapes are produced by the motion of the nuclear system in the mass asymmetry coordinate. The results of calculations of the spin dependence of the parity splitting and of the electric multipole transition moments are in agreement with the experimental data.

  5. Prediction of enzyme activity with neural network models based on electronic and geometrical features of substrates.

    Science.gov (United States)

    Szaleniec, Maciej

    2012-01-01

    Artificial Neural Networks (ANNs) are introduced as robust and versatile tools in quantitative structure-activity relationship (QSAR) modeling. Their application to the modeling of enzyme reactivity is discussed, along with methodological issues. Methods of input variable selection, optimization of network internal structure, data set division and model validation are discussed. The application of ANNs in the modeling of enzyme activity over the last 20 years is briefly recounted. The discussed methodology is exemplified by the case of ethylbenzene dehydrogenase (EBDH). Intelligent Problem Solver and genetic algorithms are applied for input vector selection, whereas k-means clustering is used to partition the data into training and test cases. The obtained models exhibit high correlation between the predicted and experimental values (R(2) > 0.9). Sensitivity analyses and study of the response curves are used as tools for the physicochemical interpretation of the models in terms of the EBDH reaction mechanism. Neural networks are shown to be a versatile tool for the construction of robust QSAR models that can be applied to a range of aspects important in drug design and the prediction of biological activity.

  6. Abnormalities in the structural covariance of emotion regulation networks in major depressive disorder.

    Science.gov (United States)

    Wu, Huawang; Sun, Hui; Wang, Chao; Yu, Lin; Li, Yilan; Peng, Hongjun; Lu, Xiaobing; Hu, Qingmao; Ning, Yuping; Jiang, Tianzi; Xu, Jinping; Wang, Jiaojian

    2017-01-01

    Major depressive disorder (MDD) is a common psychiatric disorder that is characterized by cognitive deficits and affective symptoms. To date, an increasing number of neuroimaging studies have focused on emotion regulation and have consistently shown that emotion dysregulation is one of the central features and underlying mechanisms of MDD. Although gray matter morphological abnormalities in regions within emotion regulation networks have been identified in MDD, the interactions and relationships between these gray matter structures remain largely unknown. Thus, in this study, we adopted a structural covariance method based on gray matter volume to investigate the brain morphological abnormalities within the emotion regulation networks in a large cohort of 65 MDD patients and 65 age- and gender-matched healthy controls. A permutation test with p emotion dysregulation is an underlying mechanism of MDD by revealing disrupted structural covariance patterns in the emotion regulation network. Copyright © 2016 Elsevier Ltd. All rights reserved.

  7. Mesoscopic structure conditions the emergence of cooperation on social networks

    Energy Technology Data Exchange (ETDEWEB)

    Lozano, S.; Arenas, A.; Sanchez, A.

    2008-12-01

    We study the evolutionary Prisoner's Dilemma on two social networks substrates obtained from actual relational data. We find very different cooperation levels on each of them that cannot be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding a good agreement with the observations in both real substrates. Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. Further, the study allows us to define new quantitative parameters that summarize the mesoscopic structure of any network. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.

  8. Neural Network Algorithm for Prediction of Secondary Protein Structure

    National Research Council Canada - National Science Library

    Zikrija Avdagic; Elvir Purisevic; Emir Buza; Zlatan Coralic

    2009-01-01

    .... In this paper we describe the method and results of using CB513 as a dataset suitable for development of artificial neural network algorithms for prediction of secondary protein structure with MATLAB...

  9. Integrating Temporal and Spatial Scales: Human Structural Network Motifs Across Age and Region of Interest Size

    Science.gov (United States)

    Echtermeyer, Christoph; Han, Cheol E.; Rotarska-Jagiela, Anna; Mohr, Harald; Uhlhaas, Peter J.; Kaiser, Marcus

    2011-01-01

    Human brain networks can be characterized at different temporal or spatial scales given by the age of the subject or the spatial resolution of the neuroimaging method. Integration of data across scales can only be successful if the combined networks show a similar architecture. One way to compare networks is to look at spatial features, based on fiber length, and topological features of individual nodes where outlier nodes form single node motifs whose frequency yields a fingerprint of the network. Here, we observe how characteristic single node motifs change over age (12–23 years) and network size (414, 813, and 1615 nodes) for diffusion tensor imaging structural connectivity in healthy human subjects. First, we find the number and diversity of motifs in a network to be strongly correlated. Second, comparing different scales, the number and diversity of motifs varied across the temporal (subject age) and spatial (network resolution) scale: certain motifs might only occur at one spatial scale or for a certain age range. Third, regions of interest which show one motif at a lower resolution may show a range of motifs at a higher resolution which may or may not include the original motif at the lower resolution. Therefore, both the type and localization of motifs differ for different spatial resolutions. Our results also indicate that spatial resolution has a higher effect on topological measures whereas spatial measures, based on fiber lengths, remain more comparable between resolutions. Therefore, spatial resolution is crucial when comparing characteristic node fingerprints given by topological and spatial network features. As node motifs are based on topological and spatial properties of brain connectivity networks, these conclusions are also relevant to other studies using connectome analysis. PMID:21811454

  10. Neural network definitions of highly predictable protein secondary structure classes

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A. [Los Alamos National Lab., NM (United States)]|[Santa Fe Inst., NM (United States); Steeg, E. [Toronto Univ., ON (Canada). Dept. of Computer Science; Farber, R. [Los Alamos National Lab., NM (United States)

    1994-02-01

    We use two co-evolving neural networks to determine new classes of protein secondary structure which are significantly more predictable from local amino sequence than the conventional secondary structure classification. Accurate prediction of the conventional secondary structure classes: alpha helix, beta strand, and coil, from primary sequence has long been an important problem in computational molecular biology. Neural networks have been a popular method to attempt to predict these conventional secondary structure classes. Accuracy has been disappointingly low. The algorithm presented here uses neural networks to similtaneously examine both sequence and structure data, and to evolve new classes of secondary structure that can be predicted from sequence with significantly higher accuracy than the conventional classes. These new classes have both similarities to, and differences with the conventional alpha helix, beta strand and coil.

  11. Structural Changes in Online Discussion Networks

    DEFF Research Database (Denmark)

    Yang, Yang; Medaglia, Rony

    2014-01-01

    Social networking platforms in China provide a hugely interesting and relevant source for understanding dynamics of online discussions in a unique socio-cultural and institutional environment. This paper investigates the evolution of patterns of similar-minded and different-minded interactions over...

  12. Structural power and the evolution of collective fairness in social networks.

    Science.gov (United States)

    Santos, Fernando P; Pacheco, Jorge M; Paiva, Ana; Santos, Francisco C

    2017-01-01

    From work contracts and group buying platforms to political coalitions and international climate and economical summits, often individuals assemble in groups that must collectively reach decisions that may favor each part unequally. Here we quantify to which extent our network ties promote the evolution of collective fairness in group interactions, modeled by means of Multiplayer Ultimatum Games (MUG). We show that a single topological feature of social networks-which we call structural power-has a profound impact on the tendency of individuals to take decisions that favor each part equally. Increased fair outcomes are attained whenever structural power is high, such that the networks that tie individuals allow them to meet the same partners in different groups, thus providing the opportunity to strongly influence each other. On the other hand, the absence of such close peer-influence relationships dismisses any positive effect created by the network. Interestingly, we show that increasing the structural power of a network leads to the appearance of well-defined modules-as found in human social networks that often exhibit community structure-providing an interaction environment that maximizes collective fairness.

  13. Structural power and the evolution of collective fairness in social networks.

    Directory of Open Access Journals (Sweden)

    Fernando P Santos

    Full Text Available From work contracts and group buying platforms to political coalitions and international climate and economical summits, often individuals assemble in groups that must collectively reach decisions that may favor each part unequally. Here we quantify to which extent our network ties promote the evolution of collective fairness in group interactions, modeled by means of Multiplayer Ultimatum Games (MUG. We show that a single topological feature of social networks-which we call structural power-has a profound impact on the tendency of individuals to take decisions that favor each part equally. Increased fair outcomes are attained whenever structural power is high, such that the networks that tie individuals allow them to meet the same partners in different groups, thus providing the opportunity to strongly influence each other. On the other hand, the absence of such close peer-influence relationships dismisses any positive effect created by the network. Interestingly, we show that increasing the structural power of a network leads to the appearance of well-defined modules-as found in human social networks that often exhibit community structure-providing an interaction environment that maximizes collective fairness.

  14. Classification of Weed Species Using Artificial Neural Networks Based on Color Leaf Texture Feature

    Science.gov (United States)

    Li, Zhichen; An, Qiu; Ji, Changying

    The potential impact of herbicide utilization compel people to use new method of weed control. Selective herbicide application is optimal method to reduce herbicide usage while maintain weed control. The key of selective herbicide is how to discriminate weed exactly. The HIS color co-occurrence method (CCM) texture analysis techniques was used to extract four texture parameters: Angular second moment (ASM), Entropy(E), Inertia quadrature (IQ), and Inverse difference moment or local homogeneity (IDM).The weed species selected for studying were Arthraxon hispidus, Digitaria sanguinalis, Petunia, Cyperus, Alternanthera Philoxeroides and Corchoropsis psilocarpa. The software of neuroshell2 was used for designing the structure of the neural network, training and test the data. It was found that the 8-40-1 artificial neural network provided the best classification performance and was capable of classification accuracies of 78%.

  15. The effects of traffic structure on application and network performance

    CERN Document Server

    Aikat, Jay; Smith, F Donelson

    2012-01-01

    Over the past three decades, the Internet's rapid growth has spurred the development of new applications in mobile computing, digital music, online video, gaming and social networks. These applications rely heavily upon various underlying network protocols and mechanisms to enable, maintain and enhance their Internet functionalityThe Effects of Traffic Structure on Application and Network Performance provides the necessary tools for maximizing the network efficiency of any Internet application, and presents ground-breaking research that will influence how these applications are built in the fu

  16. BP network for atorvastatin effect evaluation from ultrasound images features classification

    Science.gov (United States)

    Fang, Mengjie; Yang, Xin; Liu, Yang; Xu, Hongwei; Liang, Huageng; Wang, Yujie; Ding, Mingyue

    2013-10-01

    Atherosclerotic lesions at the carotid artery are a major cause of emboli or atheromatous debris, resulting in approximately 88% of ischemic strokes in the USA in 2006. Stroke is becoming the most common cause of death worldwide, although patient management and prevention strategies have reduced stroke rate considerably over the past decades. Many research studies have been carried out on how to quantitatively evaluate local arterial effects for potential carotid disease treatments. As an inexpensive, convenient and fast means of detection, ultrasonic medical testing has been widespread in the world, so it is very practical to use ultrasound technology in the prevention and treatment of carotid atherosclerosis. This paper is dedicated to this field. Currently, many ultrasound image characteristics on carotid plaque have been proposed. After screening a large number of features (including 26 morphological and 85 texture features), we have got six shape characteristics and six texture characteristics in the combination. In order to test the validity and accuracy of these combined features, we have established a Back-Propagation (BP) neural network to classify atherosclerosis plaques between atorvastatin group and placebo group. The leave-one-case-out protocol was utilized on a database of 768 carotid ultrasound images of 12 patients (5 subjects of placebo group and 7 subjects of atorvastatin group) for the evaluation. The classification results showed that the combined features and classification have good recognition ability, with the overall accuracy 83.93%, sensitivity 82.14%, specificity 85.20%, positive predictive value 79.86%, negative predictive value 86.98%, Matthew's correlation coefficient 67.08%, and Youden's index 67.34%. And the receiver operating characteristic (ROC) curve in our test also performed well.

  17. Artificial Neural Network Based Fault Diagnostics of Rolling Element Bearings Using Time-Domain Features

    Science.gov (United States)

    Samanta, B.; Al-Balushi, K. R.

    2003-03-01

    A procedure is presented for fault diagnosis of rolling element bearings through artificial neural network (ANN). The characteristic features of time-domain vibration signals of the rotating machinery with normal and defective bearings have been used as inputs to the ANN consisting of input, hidden and output layers. The features are obtained from direct processing of the signal segments using very simple preprocessing. The input layer consists of five nodes, one each for root mean square, variance, skewness, kurtosis and normalised sixth central moment of the time-domain vibration signals. The inputs are normalised in the range of 0.0 and 1.0 except for the skewness which is normalised between -1.0 and 1.0. The output layer consists of two binary nodes indicating the status of the machine—normal or defective bearings. Two hidden layers with different number of neurons have been used. The ANN is trained using backpropagation algorithm with a subset of the experimental data for known machine conditions. The ANN is tested using the remaining set of data. The effects of some preprocessing techniques like high-pass, band-pass filtration, envelope detection (demodulation) and wavelet transform of the vibration signals, prior to feature extraction, are also studied. The results show the effectiveness of the ANN in diagnosis of the machine condition. The proposed procedure requires only a few features extracted from the measured vibration data either directly or with simple preprocessing. The reduced number of inputs leads to faster training requiring far less iterations making the procedure suitable for on-line condition monitoring and diagnostics of machines.

  18. A cost effective wireless structural health monitoring network for buildings in earthquake zones

    Science.gov (United States)

    Pentaris, F. P.; Stonham, J.; Makris, J. P.

    2014-10-01

    The design, programming and implementation of a cost effective wireless structural health monitoring system (wSHMs) is presented, able to monitor the seismic and/or man-made acceleration in buildings. This system actually operates as a sensor network exploiting internet connections that commonly exist, aiming to monitor the structural health of the buildings being installed. Key-feature of wSHMs is that it can be implemented in Wide Area Network mode to cover many remote structures and buildings, on metropolitan scale. Acceleration data is able to send, in real time, from dozens of buildings of a broad metropolitan area, to a central database, where they are analyzed in order to depict possible structural damages or nonlinear characteristics and alert for non-appropriateness of specific structures.

  19. Recognition of Paddy, Brown Rice and White Rice Cultivars Based on Textural Features of Images and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    I Golpour

    2015-03-01

    Full Text Available Identification of rice cultivars is very important in modern agriculture. Texture properties could be used to identify of rice cultivars among of the various factors. The digital images processing can be used as a new approach to extract texture features. The objective of this research was to identify rice cultivars using of texture features with using image processing and back propagation artificial neural networks. To identify rice cultivars, five rice cultivars Fajr, Shiroodi, Neda, Tarom mahalli and Khazar were selected. Finally, 108 textural features were extracted from rice images using gray level co-occurrence matrix. Then cultivar identification was carried out using Back Propagation Artificial Neural Network. After evaluation of the network with one hidden layer using texture features, the highest classification accuracy for paddy cultivars, brown rice and white rice were obtained 92.2%, 97.8% and 98.9%, respectively. After evaluation of the network with two hidden layers, the average accuracy for classification of paddy cultivars was obtained to be 96.67%, for brown rice it was 97.78% and for white rice the classification accuracy was 98.88%. The highest mean classification accuracy acquired for paddy cultivars with 45 features was achieved to be 98.9%, for brown rice cultivars with 11 selected features it was 93.3% and it was 96.7% with 18 selected features for rice cultivars.

  20. Structural and functional features of central nervous system lymphatic vessels.

    Science.gov (United States)

    Louveau, Antoine; Smirnov, Igor; Keyes, Timothy J; Eccles, Jacob D; Rouhani, Sherin J; Peske, J David; Derecki, Noel C; Castle, David; Mandell, James W; Lee, Kevin S; Harris, Tajie H; Kipnis, Jonathan

    2015-07-16

    One of the characteristics of the central nervous system is the lack of a classical lymphatic drainage system. Although it is now accepted that the central nervous system undergoes constant immune surveillance that takes place within the meningeal compartment, the mechanisms governing the entrance and exit of immune cells from the central nervous system remain poorly understood. In searching for T-cell gateways into and out of the meninges, we discovered functional lymphatic vessels lining the dural sinuses. These structures express all of the molecular hallmarks of lymphatic endothelial cells, are able to carry both fluid and immune cells from the cerebrospinal fluid, and are connected to the deep cervical lymph nodes. The unique location of these vessels may have impeded their discovery to date, thereby contributing to the long-held concept of the absence of lymphatic vasculature in the central nervous system. The discovery of the central nervous system lymphatic system may call for a reassessment of basic assumptions in neuroimmunology and sheds new light on the aetiology of neuroinflammatory and neurodegenerative diseases associated with immune system dysfunction.

  1. Chaotic features of nuclear structure and dynamics: selected topics

    Science.gov (United States)

    Zelevinsky, Vladimir; Volya, Alexander

    2016-03-01

    Quantum chaos has become an important element of our knowledge about physics of complex systems. In typical mesoscopic systems of interacting particles the dynamics invariably become chaotic when the level density, growing by combinatorial reasons, leads to the increasing probability of mixing simple mean-field (particle-hole) configurations. The resulting stationary states have exceedingly complicated structures that are comparable to those in random matrix theory. We discuss the main properties of mesoscopic quantum chaos and show that it can serve as a justification for application of statistical mechanics to mesoscopic systems. We show that quantum chaos becomes a powerful instrument for experimental, theoretical and computational work. The generalization to open systems and effects in the continuum are discussed with the help of the effective non-Hermitian Hamiltonian; it is shown how to formulate this approach for numerous problems of quantum signal transmission. The artificially introduced randomness can also be helpful for a deeper understanding of physics. We indicate the problems that require more investigation so as to be understood further.

  2. Structural and functional features of central nervous system lymphatics

    Science.gov (United States)

    Louveau, Antoine; Smirnov, Igor; Keyes, Timothy J.; Eccles, Jacob D.; Rouhani, Sherin J.; Peske, J. David; Derecki, Noel C.; Castle, David; Mandell, James W.; Kevin, S. Lee; Harris, Tajie H.; Kipnis, Jonathan

    2015-01-01

    One of the characteristics of the CNS is the lack of a classical lymphatic drainage system. Although it is now accepted that the CNS undergoes constant immune surveillance that takes place within the meningeal compartment1–3, the mechanisms governing the entrance and exit of immune cells from the CNS remain poorly understood4–6. In searching for T cell gateways into and out of the meninges, we discovered functional lymphatic vessels lining the dural sinuses. These structures express all of the molecular hallmarks of lymphatic endothelial cells, are able to carry both fluid and immune cells from the CSF, and are connected to the deep cervical lymph nodes. The unique location of these vessels may have impeded their discovery to date, thereby contributing to the long-held concept of the absence of lymphatic vasculature in the CNS. The discovery of the CNS lymphatic system may call for a reassessment of basic assumptions in neuroimmunology and shed new light on the etiology of neuroinflammatory and neurodegenerative diseases associated with immune system dysfunction. PMID:26030524

  3. Community structure from spectral properties in complex networks

    Science.gov (United States)

    Servedio, V. D. P.; Colaiori, F.; Capocci, A.; Caldarelli, G.

    2005-06-01

    We analyze the spectral properties of complex networks focusing on their relation to the community structure, and develop an algorithm based on correlations among components of different eigenvectors. The algorithm applies to general weighted networks, and, in a suitably modified version, to the case of directed networks. Our method allows to correctly detect communities in sharply partitioned graphs, however it is useful to the analysis of more complex networks, without a well defined cluster structure, as social and information networks. As an example, we test the algorithm on a large scale data-set from a psychological experiment of free word association, where it proves to be successful both in clustering words, and in uncovering mental association patterns.

  4. Ranking influential nodes in complex networks with structural holes

    Science.gov (United States)

    Hu, Ping; Mei, Ting

    2018-01-01

    Ranking influential nodes in complex networks is of great theoretical and practical significance to ensure the safe operations of networks. In view of the important role structural hole nodes usually play in information spreading in complex networks, we propose a novel ranking method of influential nodes using structural holes called E-Burt method, which can be applied to weighted networks. This method fully takes into account the total connectivity strengths of the node in its local scope, the number of the connecting edges and the distributions of the total connectivity strengths on its connecting edges. The simulation results on the susceptible-infectious-recovered (SIR) dynamics suggest that the proposed E-Burt method can rank influential nodes more effectively and accurately in complex networks.

  5. Maps of random walks on complex networks reveal community structure.

    Science.gov (United States)

    Rosvall, Martin; Bergstrom, Carl T

    2008-01-29

    To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.

  6. Realization of Broadband Matched Filter Structures Based on Dual Networks

    Directory of Open Access Journals (Sweden)

    M. Gerding

    2005-01-01

    Full Text Available This paper deals with the basic electrical properties of dual networks and with their application in broadband matched filter structures. Starting with the main characteristics and different realization methods of dual networks, a filter structure is presented, which is based on a combination of dual networks and which provides a broadband matched input and two decoupled output ports. This filter synthesis focuses on the design of high pass filters, which are suitable to be used as differentiating stages in electrical pulse generators as a part of the so-called pulse shaping network. In order to achieve a proper pulse shape and for the prevention of multiple reflections between the switching circuit and the differentiating network, a broadband matched filter is a basic requirement.

  7. The structure of complex networks theory and applications

    CERN Document Server

    Estrada, Ernesto

    2012-01-01

    This book deals with the analysis of the structure of complex networks by combining results from graph theory, physics, and pattern recognition. The book is divided into two parts. 11 chapters are dedicated to the development of theoretical tools for the structural analysis of networks, and 7 chapters are illustrating, in a critical way, applications of these tools to real-world scenarios. The first chapters provide detailed coverage of adjacency and metric and topologicalproperties of networks, followed by chapters devoted to the analysis of individual fragments and fragment-based global inva

  8. Modeling Temporal Evolution and Multiscale Structure in Networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard

    2013-01-01

    -point model to account for the temporal evolution of each vertex. We demonstrate that our model is able to infer time-varying multiscale structure in synthetic as well as three real world time-evolving complex networks. Our modeling of the temporal evolution of hierarchies brings new insights......Many real-world networks exhibit both temporal evolution and multiscale structure. We propose a model for temporally correlated multifurcating hierarchies in complex networks which jointly capture both effects. We use the Gibbs fragmentation tree as prior over multifurcating trees and a change...

  9. Improving the Reliability of Network Metrics in Structural Brain Networks by Integrating Different Network Weighting Strategies into a Single Graph

    Directory of Open Access Journals (Sweden)

    Stavros I. Dimitriadis

    2017-12-01

    Full Text Available Structural brain networks estimated from diffusion MRI (dMRI via tractography have been widely studied in healthy controls and patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost fully-weighted. Here, we scanned five healthy participants five times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROI from the Automated Anatomical Labeling (AAL template. The edges were weighted according to nine different methods. We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an Integrated Weighted Structural Brain Network (ISWBN. Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of: (a intra-class correlation coefficient (ICC of well-known network metrics, both node-wise and per network level; and (b the recognition accuracy of each subject compared to the remainder of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject, after first applying our proposed topological filtering scheme. Based on a threshold where the network level ICC should be >0.90, our findings revealed that six out of nine NWS lead to unreliable results at the network level, while all nine NWS were unreliable at the node level. In comparison, our proposed ISWBN performed as well as the best performing individual NWS at the network level, and the ICC was higher compared to all individual NWS at the node

  10. Structure and Evolution of the Foreign Exchange Networks

    Science.gov (United States)

    Kwapień, J.; Gworek, S.; Drożdż, S.

    2009-01-01

    We investigate topology and temporal evolution of the foreign currency exchange market viewed from a weighted network perspective. Based on exchange rates for a set of 46 currencies (including precious metals), we construct different representations of the FX network depending on a choice of the base currency. Our results show that the network structure is not stable in time, but there are main clusters of currencies, which persist for a long period of time despite the fact that their size and content are variable. We find a long-term trend in the network's evolution which affects the USD and EUR nodes. In all the network representations, the USD node gradually loses its centrality, while, on contrary, the EUR node has become slightly more central than it used to be in its early years. Despite this directional trend, the overall evolution of the network is noisy.

  11. Neural network structure for navigation using potential fields

    Science.gov (United States)

    Plumer, Edward S.

    1992-01-01

    A hybrid-network method for obstacle avoidance in the truck-backing system of D. Nguyen and B. Widrow (1989) is presented. A neural network technique for vehicle navigation and control in the presence of obstacles has been developed. A potential function which peaks at the surface of obstacles and has its minimum at the proper vehicle destination is computed using a network structure. The field is guaranteed not to have spurious local minima and does not have the property of flattening-out far from the goal. A feedforward neural network is used to control the steering of the vehicle using local field information. The network is trained in an obstacle-free space to follow the negative gradient of the field, after which the network is able to control and navigate the truck to its target destination in a space of obstacles which may be stationary or movable.

  12. Fragmented Romanian Sociology: Growth and Structure of the Collaboration Network

    Science.gov (United States)

    Hâncean, Marian-Gabriel; Perc, Matjaž; Vlăsceanu, Lazăr

    2014-01-01

    Structural patterns in collaboration networks are essential for understanding how new ideas, research practices, innovation or cooperation circulate and develop within academic communities and between and within university departments. In our research, we explore and investigate the structure of the collaboration network formed by the academics working full-time within all the 17 sociology departments across Romania. We show that the collaboration network is sparse and fragmented, and that it constitutes an environment that does not promote the circulation of new ideas and innovation within the field. Although recent years have witnessed an increase in the productivity of Romanian sociologists, there is still ample room for improvement in terms of the interaction infrastructure that ought to link individuals together so that they could maximize their potentials. We also fail to discern evidence in favor of the Matthew effect governing the growth of the network, which suggests scientific success and productivity are not rewarded. Instead, the structural properties of the collaboration network are partly those of a core-periphery network, where the spread of innovation and change can be explained by structural equivalence rather than by interpersonal influence models. We also provide support for the idea that, within the observed network, collaboration is the product of homophily rather than prestige effects. Further research on the subject based on data from other countries in the region is needed to place our results in a comparative framework, in particular to discern whether the behavior of the Romanian sociologist community is unique or rather common. PMID:25409180

  13. Fragmented Romanian sociology: growth and structure of the collaboration network.

    Directory of Open Access Journals (Sweden)

    Marian-Gabriel Hâncean

    Full Text Available Structural patterns in collaboration networks are essential for understanding how new ideas, research practices, innovation or cooperation circulate and develop within academic communities and between and within university departments. In our research, we explore and investigate the structure of the collaboration network formed by the academics working full-time within all the 17 sociology departments across Romania. We show that the collaboration network is sparse and fragmented, and that it constitutes an environment that does not promote the circulation of new ideas and innovation within the field. Although recent years have witnessed an increase in the productivity of Romanian sociologists, there is still ample room for improvement in terms of the interaction infrastructure that ought to link individuals together so that they could maximize their potentials. We also fail to discern evidence in favor of the Matthew effect governing the growth of the network, which suggests scientific success and productivity are not rewarded. Instead, the structural properties of the collaboration network are partly those of a core-periphery network, where the spread of innovation and change can be explained by structural equivalence rather than by interpersonal influence models. We also provide support for the idea that, within the observed network, collaboration is the product of homophily rather than prestige effects. Further research on the subject based on data from other countries in the region is needed to place our results in a comparative framework, in particular to discern whether the behavior of the Romanian sociologist community is unique or rather common.

  14. Fragmented Romanian sociology: growth and structure of the collaboration network.

    Science.gov (United States)

    Hâncean, Marian-Gabriel; Perc, Matjaž; Vlăsceanu, Lazăr

    2014-01-01

    Structural patterns in collaboration networks are essential for understanding how new ideas, research practices, innovation or cooperation circulate and develop within academic communities and between and within university departments. In our research, we explore and investigate the structure of the collaboration network formed by the academics working full-time within all the 17 sociology departments across Romania. We show that the collaboration network is sparse and fragmented, and that it constitutes an environment that does not promote the circulation of new ideas and innovation within the field. Although recent years have witnessed an increase in the productivity of Romanian sociologists, there is still ample room for improvement in terms of the interaction infrastructure that ought to link individuals together so that they could maximize their potentials. We also fail to discern evidence in favor of the Matthew effect governing the growth of the network, which suggests scientific success and productivity are not rewarded. Instead, the structural properties of the collaboration network are partly those of a core-periphery network, where the spread of innovation and change can be explained by structural equivalence rather than by interpersonal influence models. We also provide support for the idea that, within the observed network, collaboration is the product of homophily rather than prestige effects. Further research on the subject based on data from other countries in the region is needed to place our results in a comparative framework, in particular to discern whether the behavior of the Romanian sociologist community is unique or rather common.

  15. Imaging structural and functional brain networks in temporal lobe epilepsy

    Science.gov (United States)

    Bernhardt, Boris C.; Hong, SeokJun; Bernasconi, Andrea; Bernasconi, Neda

    2013-01-01

    Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy. PMID:24098281

  16. Unifying Inference of Meso-Scale Structures in Networks.

    Directory of Open Access Journals (Sweden)

    Birkan Tunç

    Full Text Available Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities of the brain, as well as its auxiliary characteristics (core-periphery.

  17. Unifying Inference of Meso-Scale Structures in Networks.

    Science.gov (United States)

    Tunç, Birkan; Verma, Ragini

    2015-01-01

    Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities) of the brain, as well as its auxiliary characteristics (core-periphery).

  18. Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

    Science.gov (United States)

    Zhang, Xue; Acencio, Marcio Luis; Lemke, Ney

    2016-01-01

    Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research. PMID:27014079

  19. Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review.

    Science.gov (United States)

    Zhang, Xue; Acencio, Marcio Luis; Lemke, Ney

    2016-01-01

    Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research.

  20. Conversation practices and network structure in Twitter

    DEFF Research Database (Denmark)

    Rossi, Luca; Magnani, Matteo

    2012-01-01

    the participation in the same hashtag based conversation change the follower list of the participants? Is it possible to point out specific social behaviors that would produce a major gain of followers? Our conclusions are based on real data concerning the popular TV show Xfactor, that largely used Twitter......The public by default nature of Twitter messages, together with the adoption of the #hashtag convention led, in few years, to the creation of a digital space able to host worldwide conversation on almost every kind of topic. From major TV shows to Natural disasters there is no contemporary event...... that does not have its own #hashtag to gather together the ongoing Twitter conversation. These topical discussions take place outside of the Twitter network made of followers and friends. Nevertheless this topical network is where many of the most studied phenomena take place. Therefore Twitter based...